GenAI4ELab/papercli-papers huggingface dataset:
Efficient Heterogeneity-Aware Federated Active Data Selection
Authors: Ying-Peng Tang; Chao Ren; Xiaoli Tang; Sheng-Jun Huang; Lizhen Cui; Han Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Federated Active Learning (FAL) aims to learn an effective global model, while minimizing label queries. Owing to privacy requirements, it is challenging to design effective active data selection schemes due to the lack of cross-client query information. In this paper, we bridge this important gap by proposing the \underline{F}ederated \underline{A}ctive data selection by \underline{LE}verage score sampling (FALE) method. It is designed for regression tasks in the presence of non-i.i.d. client data to enable the server to select data globally in a privacy-preserving manner. Based on FedSVD, FALE aims to estimate the utility of unlabeled data and perform data selection via leverage score sampling. Besides, a secure model learning framework is designed for federated regression tasks to exploit supervision. FALE can operate without requiring an initial labeled set and select the instances in a single pass, significantly reducing communication overhead. Theoretical analyze establishes the query complexity for FALE to achieve constant factor approximation and relative error approximation. Extensive experiments on 11 benchmark datasets demonstrate significant improvements of FALE over existing state-of-the-art methods.
The Polynomial Stein Discrepancy for Assessing Moment Convergence
Authors: Narayan Srinivasan; Matthew Sutton; Christopher Drovandi; Leah F South
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalable Bayesian sampling algorithms, such as stochastic gradient Langevin dynamics, that are asymptotically biased. Instead, the gold standard is to use the kernel Stein Discrepancy (KSD), which is itself not scalable given its quadratic cost in the number of samples. The KSD and its faster extensions also typically suffer from the curse-of-dimensionality and can require extensive tuning. To address these limitations, we develop the polynomial Stein discrepancy (PSD) and an associated goodness-of-fit test. While the new test is not fully convergence-determining, we prove that it detects differences in the first $r$ moments for Gaussian targets. We empirically show that the test has higher power than its competitors in several examples, and at a lower computational cost. Finally, we demonstrate that the PSD can assist practitioners to select hyper-parameters of Bayesian sampling algorithms more efficiently than competitors.
The Role of Sparsity for Length Generalization in LLMs
Authors: Noah Golowich; Samy Jelassi; David Brandfonbrener; Sham M. Kakade; Eran Malach
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Training large language models to predict beyond their training context lengths has drawn much attention in recent years, yet the principles driving such behavior of length generalization remain underexplored. We propose a new theoretical framework to study length generalization for the next-token prediction task, as performed by decoder-only transformers. Conceptually, we show that length generalization occurs as long as each predicted token depends on a small (fixed) number of previous tokens. We formalize such tasks via a notion we call k-sparse planted correlation distributions, and show that an idealized model of transformers which generalize attention heads successfully length-generalize on such tasks. As a bonus, our theoretical model allows us to provide justifications for techniques to modify positional embeddings which have been introduced to improve length generalization, such as position coupling.

We support our theoretical results with experiments on synthetic tasks and natural language, which confirm that a key factor driving length generalization is indeed a ``sparse'' dependency structure of each token on the previous ones. Further, inspired by our theory, we introduce Predictive Position Coupling, a generalization of position coupling which trains the transformer to predict the position IDs used in a positional coupling approach.  Predictive Position Coupling thereby allows us to broaden the array of tasks to which Position Coupling can successfully be applied to achieve length generalization.
Cost-efficient Collaboration between On-device and Cloud Language Models
Authors: Avanika Narayan; Dan Biderman; Sabri Eyuboglu; Avner May; Scott Linderman; James Zou; Christopher Re
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We investigate an emerging setup in which a small, on-device language model (LM) with access to local data collaborates with a frontier, cloud-hosted LM to solve real-world tasks involving financial, medical, and scientific reasoning over long documents. 
*Can a local-remote collaboration reduce cloud inference costs while preserving quality?*
First, we consider a naïve collaboration protocol, coined MINION, where the local and remote models simply chat back and forth. Because only the local model ingests the full context, this protocol reduces cloud costs by 30.4x, but recovers only 87% of the performance of the frontier model.
We identify two key limitations of this protocol: the local model struggles to (1) follow the remote model's multi-step instructions and (2) reason over long contexts. Motivated by these observations, we propose MINIONS, a protocol in which the remote model decomposes the task into easier subtasks over shorter chunks of the document, that are executed locally in parallel. MINIONS reduces costs by 5.7× on average while recovering 97.9% of the remote-only performance. Our analysis reveals several key design choices that influence the trade-off between cost and performance in local-remote systems.
Overcoming Non-monotonicity in Transducer-based Streaming Generation
Authors: Zhengrui Ma; Yang Feng; Min zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Streaming generation models are utilized across fields, with the Transducer architecture being popular in industrial applications. However, its input-synchronous decoding mechanism presents challenges in tasks requiring non-monotonic alignments, such as simultaneous translation. In this research, we address this issue by integrating Transducer's decoding with the history of input stream via a learnable monotonic attention. Our approach leverages the forward-backward algorithm to infer the posterior probability of alignments between the predictor states and input timestamps, which is then used to estimate the monotonic context representations, thereby avoiding the need to enumerate the exponentially large alignment space during training. Extensive experiments show that our MonoAttn-Transducer effectively handles non-monotonic alignments in streaming scenarios, offering a robust solution for complex generation tasks. Code is available at https://github.com/ictnlp/MonoAttn-Transducer.
Neural Solver Selection for Combinatorial Optimization
Authors: Chengrui Gao; Haopu Shang; Ke Xue; Chao Qian
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Machine learning has increasingly been employed to solve NP-hard combinatorial optimization problems, resulting in the emergence of neural solvers that demonstrate remarkable performance, even with minimal domain-specific knowledge. To date, the community has created numerous open-source neural solvers with distinct motivations and inductive biases. While considerable efforts are devoted to designing powerful single solvers, our findings reveal that existing solvers typically demonstrate complementary performance across different problem instances. This suggests that significant improvements could be achieved through effective coordination of neural solvers at the instance level. In this work, we propose the first general framework to coordinate the neural solvers, which involves feature extraction, selection model, and selection strategy, aiming to allocate each instance to the most suitable solvers. To instantiate, we collect several typical neural solvers with state-of-the-art performance as alternatives, and explore various methods for each component of the framework. We evaluated our framework on two typical problems, Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP). Experimental results show that our framework can effectively distribute instances and the resulting composite solver can achieve significantly better performance (e.g., reduce the optimality gap by 0.88% on TSPLIB and 0.71% on CVRPLIB) than the best individual neural solver with little extra time cost.
In-Context Denoising with One-Layer Transformers: Connections between Attention and Associative Memory Retrieval
Authors: Matthew Smart; Alberto Bietti; Anirvan M. Sengupta
Venue: ICML 2025 (ICML 2025 oral)

Abstract: We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a Bayesian framework, we show theoretically and empirically that certain restricted denoising problems can be solved optimally even by a single-layer transformer. We demonstrate that a trained attention layer processes each denoising prompt by performing a single gradient descent update on a context-aware DAM energy landscape, where context tokens serve as associative memories and the query token acts as an initial state. This one-step update yields better solutions than exact retrieval of either a context token or a spurious local minimum, providing a concrete example of DAM networks extending beyond the standard retrieval paradigm. Overall, this work solidifies the link between associative memory and attention mechanisms first identified by Ramsauer et al., and demonstrates the relevance of associative memory models in the study of in-context learning.
Origin Identification for Text-Guided Image-to-Image Diffusion Models
Authors: Wenhao Wang; Yifan Sun; Zongxin Yang; Zhentao Tan; Zhengdong Hu; Yi Yang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for *spreading misinformation*, *infringing on copyrights*, and *evading content tracing*. This motivates us to introduce the task of origin **ID**entification for text-guided **I**mage-to-image **D**iffusion models (**ID$\mathbf{^2}$**), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images. However, due to *visual discrepancy* across generations produced by different diffusion models, this similarity-based approach fails when training on images from one model and testing on those from another, limiting its effectiveness in real-world applications. To solve this challenge of the proposed ID$^2$ task, we contribute the first dataset and a theoretically guaranteed method, both emphasizing generalizability. The curated dataset, **OriPID**, contains abundant **Ori**gins and guided **P**rompts, which can be used to train and test potential **ID**entification models across various diffusion models. In the method section, we first prove the *existence* of a linear transformation that minimizes the distance between the pre-trained Variational Autoencoder embeddings of generated samples and their origins. Subsequently, it is demonstrated that such a simple linear transformation can be *generalized* across different diffusion models. Experimental results show that the proposed method achieves satisfying generalization performance, significantly surpassing similarity-based methods (+31.6% mAP), even those with generalization designs. The project is available at https://id2icml.github.io.
Training Software Engineering Agents and Verifiers with SWE-Gym
Authors: Jiayi Pan; Xingyao Wang; Graham Neubig; Navdeep Jaitly; Heng Ji; Alane Suhr; Yizhe Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present SWE-Gym, the first environment for training real-world software engineering (SWE) agents. SWE-Gym contains 2,438 real-world Python task instances, each comprising a codebase with an executable runtime environment, unit tests, and a task specified in natural language. We use SWE-Gym to train language model based SWE agents, achieving up to 19% absolute gains in resolve rate on the popular SWE-Bench Verified and Lite test sets. We also experiment with inference-time scaling through verifiers trained on agent trajectories sampled from SWE-Gym. When combined with our fine-tuned SWE agents, we achieve 32.0% and 26.0% on SWE-Bench Verified and Lite, respectively, reflecting a new state-of-the-art for open-weight SWE agents. To facilitate further research, we publicly release SWE-Gym, models, and agent trajectories.
SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution
Authors: Wenwen He; Wenke Huang; Bin Yang; ShuKan Liu; Mang Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Federated Learning (FL) enables collaborative training with privacy preservation but is vulnerable to backdoor attacks, where malicious clients degrade model performance on targeted inputs. These attacks exploit FL decentralized nature, while existing defenses, based on isolated behaviors and fixed rules, can be bypassed by adaptive attackers. To address these limitations, we propose **SPMC**, a marginal collaboration defense mechanism that leverages intrinsic consistency across clients to estimate inter-client marginal contributions. This allows the system to dynamically reduce the influence of clients whose behavior deviates from the collaborative norm, thus maintaining robustness even as the number of attackers changes. In addition to overcoming proxy-dependent purification's weaknesses, we introduce a self-purification process that locally adjusts suspicious gradients. By aligning them with margin-based model updates, we mitigate the effect of local poisoning. Together, these two modules significantly improve the adaptability and resilience of FL systems, both at the client and server levels. Experimental results on a variety of classification benchmarks demonstrate that SPMC achieves strong defense performance against sophisticated backdoor attacks without sacrificing accuracy on benign tasks. The code is posted at: https://github.com/WenddHe0119/SPMC.
Linear Mode Connectivity between Multiple Models modulo Permutation Symmetries
Authors: Akira Ito; Masanori Yamada; Atsutoshi Kumagai
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Ainsworth et al. empirically demonstrated that linear mode connectivity (LMC) can be achieved between two independently trained neural networks (NNs) by applying an appropriate parameter permutation. LMC is satisfied if a linear path with non-increasing test loss exists between the models, suggesting that NNs trained with stochastic gradient descent (SGD) converge to a single approximately convex low-loss basin under permutation symmetries. However, Ainsworth et al. verified LMC for two models and provided only limited discussion on its extension to multiple models. In this paper, we conduct a more detailed empirical analysis. First, we show that existing permutation search methods designed for two models can fail to transfer multiple models into the same convex low-loss basin. Next, we propose a permutation search method using a straight-through estimator for multiple models (STE-MM). We then experimentally demonstrate that even when multiple models are given, the test loss of the merged model remains nearly the same as the losses of the original models when using STE-MM, and the loss barriers between all permuted model pairs are also small. Additionally, from the perspective of the trace of the Hessian matrix, we show that the loss sharpness around the merged model decreases as the number of models increases with STE-MM, indicating that LMC for multiple models is more likely to hold. The source code implementing our method is available at https://github.com/e5-a/STE-MM.
Constrained Exploitability Descent: An Offline Reinforcement Learning Method for Finding Mixed-Strategy Nash Equilibrium
Authors: Runyu Lu; Yuanheng Zhu; Dongbin Zhao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper proposes Constrained Exploitability Descent (CED), a model-free offline reinforcement learning (RL) algorithm for solving adversarial Markov games (MGs). CED combines the game-theoretical approach of Exploitability Descent (ED) with policy constraint methods from offline RL. While policy constraints can perturb the optimal pure-strategy solutions in single-agent scenarios, we find the side effect less detrimental in adversarial games, where the optimal policy can be a mixed-strategy Nash equilibrium. We theoretically prove that, under the uniform coverage assumption on the dataset, CED converges to a stationary point in deterministic two-player zero-sum Markov games. We further prove that the min-player policy at the stationary point follows the property of mixed-strategy Nash equilibrium in MGs. Compared to the model-based ED method that optimizes the max-player policy, our CED method no longer relies on a generalized gradient. Experiments in matrix games, a tree-form game, and an infinite-horizon soccer game verify that CED can find an equilibrium policy for the min-player as long as the offline dataset guarantees uniform coverage. Besides, CED achieves a significantly lower NashConv compared to an existing pessimism-based method and can gradually improve the behavior policy even under non-uniform data coverages. When combined with neural networks, CED also outperforms behavior cloning and offline self-play in a large-scale two-team robotic combat game.
Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
Authors: Kejia Chen; Jiawen Zhang; Jiacong Hu; Yu Wang; Jian Lou; Zunlei Feng; Mingli Song
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Quantized large language models (LLMs) have gained increasing attention and significance for enabling deployment in resource-constrained environments. However, emerging studies on a few calibration dataset-free quantization methods suggest that quantization may compromise the safety capabilities of LLMs, underscoring the urgent need for systematic safety evaluations and effective mitigation strategies. In this paper, we present comprehensive safety evaluations across various mainstream quantization techniques and diverse calibration datasets, utilizing widely accepted safety benchmarks. To address the identified safety vulnerabilities, we propose a quantization-aware safety patching framework, Q-resafe, to efficiently restore the safety capabilities of quantized LLMs while minimizing any adverse impact on utility.  Extensive experiment results demonstrate that Q-resafe successfully re-aligns the safety of quantized LLMs with their pre-quantization counterparts, even under challenging evaluation scenarios. Project page: https://github.com/Thecommonirin/Qresafe.
Subgoal-Guided Policy Heuristic Search with Learned Subgoals
Authors: Jake Tuero; Michael Buro; Levi Lelis
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Policy tree search is a family of tree search algorithms that use a policy to guide the search. These algorithms provide guarantees on the number of expansions required to solve a given problem that are based on the quality of the policy. While these algorithms have shown promising results, the process in which they are trained requires complete solution trajectories to train the policy. Search trajectories are obtained during a trial-and-error search process. When the training problem instances are hard, learning can be prohibitively costly, especially when starting from a randomly initialized policy. As a result, search samples are wasted in failed attempts to solve these hard instances. This paper introduces a novel method for learning subgoal-based policies for policy tree search algorithms. The subgoals and policies conditioned on subgoals are learned from the trees that the search expands while attempting to solve problems, including the search trees of failed attempts. We empirically show that our policy formulation and training method improve the sample efficiency of learning a policy and heuristic function in this online setting.
Diversifying Policy Behaviors with Extrinsic Behavioral Curiosity
Authors: Zhenglin Wan; Xingrui Yu; David Mark Bossens; Yueming Lyu; Qing Guo; Flint Xiaofeng Fan; Yew-Soon Ong; Ivor Tsang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Imitation learning (IL) has shown promise in various applications (e.g. robot locomotion) but is often limited to learning a single expert policy, constraining behavior diversity and robustness in unpredictable real-world scenarios. To address this, we introduce Quality Diversity Inverse Reinforcement Learning (QD-IRL), a novel framework that integrates quality-diversity optimization with IRL methods, enabling agents to learn diverse behaviors from limited demonstrations. This work introduces Extrinsic Behavioral Curiosity (EBC), which allows agents to receive additional curiosity rewards from an external critic based on how novel the behaviors are with respect to a large behavioral archive. To validate the effectiveness of EBC in exploring diverse locomotion behaviors, we evaluate our method on multiple robot locomotion tasks. EBC improves the performance of QD-IRL instances with GAIL, VAIL, and DiffAIL across all included environments by up to 185\%, 42\%, and 150\%, even surpassing expert performance by 20\% in Humanoid. Furthermore, we demonstrate that EBC is applicable to Gradient-Arborescence-based Quality Diversity Reinforcement Learning  (QD-RL) algorithms, where it substantially improves performance and provides a generic technique for learning behavioral diverse policies. The source code of this work is provided at https://github.com/vanzll/EBC.
Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation
Authors: Yang Yang; Haonan Xu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Out-of-distribution (OOD) detection aims to ensure AI system reliability by rejecting inputs outside the training distribution. Recent work shows that memorizing atypical samples during later stages of training can hurt OOD detection, while strategies for forgetting them show promising improvements. However, directly forgetting atypical samples sacrifices ID generalization and limits the model's OOD detection capability. To address this issue, we propose Progressive Self-Knowledge Distillation (PSKD) framework, which strengthens the OOD detection capability by leveraging self-provided uncertainty-embedded targets. Specifically, PSKD adaptively selects a self-teacher model from the training history using pseudo-outliers, facilitating the learning of uncertainty knowledge via multi-level distillation applied to features and responses. As a result, PSKD achieves better ID generalization and uncertainty estimation for OOD detection. Moreover, PSKD is orthogonal to most existing methods and can be integrated as a plugin to collaborate with them. Experimental results from multiple OOD scenarios verify the effectiveness and general applicability of PSKD.