GenAI4ELab/papercli-papers huggingface dataset:
Explicit Exploration for High-Welfare Equilibria in Game-Theoretic Multiagent Reinforcement Learning
Authors: Austin A. Nguyen; Anri Gu; Michael P. Wellman
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Iterative extension of empirical game models through deep reinforcement learning (RL) has proved an effective approach for finding equilibria in complex games. When multiple equilibria exist, we may also be interested in finding solutions with particular characteristics. We address this issue of equilibrium selection in the context of Policy Space Response Oracles (PSRO), a flexible game-solving framework based on deep RL, by skewing the strategy exploration process towards higher-welfare solutions. At each iteration, we create an exploration policy that imitates high welfare-yielding behavior and train a response to the current solution, regularized to be similar to the exploration policy. With no additional simulation expense, our approach, named Ex$^2$PSRO, tends to find higher welfare equilibria than vanilla PSRO in two benchmarks: a sequential bargaining game and a social dilemma game. Further experiments demonstrate Ex$^2$PSRO's composability with other PSRO variants and illuminate the relationship between exploration policy choice and algorithmic performance.
Rank-One Modified Value Iteration
Authors: Arman Sharifi Kolarijani; Tolga Ok; Peyman Mohajerin Esfahani; Mohamad Amin Sharifi Kolarijani
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we provide a novel algorithm for solving planning and learning problems of Markov decision processes. 
The proposed algorithm follows a policy iteration-type update by using a rank-one approximation of the transition probability matrix in the policy evaluation step. 
This rank-one approximation is closely related to the stationary distribution of the corresponding transition probability matrix, 
which is approximated using the power method. 
We provide theoretical guarantees for the convergence of the proposed algorithm to optimal (action-)value function with the same rate and computational complexity as the value iteration algorithm in the planning problem and as the Q-learning algorithm in the learning problem. 
Through our extensive numerical simulations, however, we show that the proposed algorithm consistently outperforms first-order algorithms and their accelerated versions for both planning and learning problems.
SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Authors: Zhao Yang; Jiwei Zhu; Bing Su
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore, considering the multi-species and multi-profile nature of genomic profile prediction, we introduce our **S**pecies-**P**rofile **A**daptive **C**ollaborative **E**xperts (SPACE) that leverages Mixture of Experts (MoE) to better capture the relationships between DNA sequences across different species and genomic profiles, thereby learning more effective DNA representations. Through extensive experiments across various tasks, our model achieves state-of-the-art performance, establishing that DNA models trained with supervised genomic profiles serve as powerful DNA representation learners.
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra
Authors: Alan Nawzad Amin; Andres Potapczynski; Andrew Gordon Wilson
Venue: ICML 2025 (ICML 2025 poster)

Abstract: To understand how genetic variants in human genomes manifest in phenotypes - traits like height or diseases like asthma - geneticists have sequenced and measured hundreds of thousands of individuals. Geneticists use this data to build models that predict how a genetic variant impacts phenotype given genomic features of the variant, like DNA accessibility or the presence of nearby DNA-bound proteins. As more data and features become available, one might expect predictive models to improve. Unfortunately, training these models is bottlenecked by the need to solve expensive linear algebra problems because variants in the genome are correlated with nearby variants, requiring inversion of large matrices. Previous methods have therefore been restricted to fitting small models, and fitting simplified summary statistics, rather than the full likelihood of the statistical model. In this paper, we leverage modern fast linear algebra techniques to develop DeepWAS (Deep genome Wide Association Studies), a method to train large and flexible neural network predictive models to optimize likelihood. Surprisingly, we find that larger models only improve performance when using our full likelihood approach; when trained by fitting traditional summary statistics, larger models perform no better than small ones. We find larger models trained on more features make better predictions, potentially improving disease predictions and therapeutic target identification.
QPRL : Learning Optimal Policies with Quasi-Potential Functions for Asymmetric Traversal
Authors: Jumman Hossain; Nirmalya Roy
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Reinforcement learning (RL) in real-world tasks such as robotic navigation often encounters environments with asymmetric traversal costs, where actions like climbing uphill versus moving downhill incur distinctly different penalties, or transitions may become irreversible. While recent quasimetric RL methods relax symmetry assumptions, they typically do not explicitly account for path-dependent costs or provide rigorous safety guarantees. We introduce Quasi-Potential Reinforcement Learning (QPRL), a novel framework that explicitly decomposes asymmetric traversal costs into a path-independent potential function ($\Phi$) and a path-dependent residual ($\Psi$). This decomposition allows efficient learning and stable policy optimization via a Lyapunov-based safety mechanism. Theoretically, we prove that QPRL achieves convergence with improved sample complexity of $\tilde{O}(\sqrt{T})$, surpassing prior quasimetric RL bounds of $\tilde{O}(T)$. Empirically, our experiments demonstrate that QPRL attains state-of-the-art performance across various navigation and control tasks, significantly reducing irreversible constraint violations by approximately $4\times$ compared to baselines.
DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control
Authors: Guiliang Liu; Yueci Deng; Runyi Zhao; Huayi Zhou; Jian Chen; Jietao Chen; Ruiyan Xu; Yunxin Tai; Kui Jia
Venue: ICML 2025 (ICML 2025 poster)

Abstract: A critical prerequisite for achieving generalizable robot control is the availability of a large-scale robot training dataset. Due to the expense of collecting realistic robotic data, recent studies explored simulating and recording robot skills in virtual environments. While simulated data can be generated at higher speeds, lower costs, and larger scales, the applicability of such simulated data remains questionable due to the gap between simulated and realistic environments. To advance the Sim2Real generalization, in this study, we present DexScale, a data engine designed to perform automatic skills simulation and scaling for learning deployable robot manipulation policies. Specifically, DexScale ensures the usability of simulated skills by integrating diverse forms of realistic data into the simulated environment, preserving semantic alignment with the target tasks. For each simulated skill in the environment, DexScale facilitates effective Sim2Real data scaling by automating the process of domain randomization and adaptation. Tuned by the scaled dataset, the control policy achieves zero-shot Sim2Real generalization across diverse tasks, multiple robot embodiments, and widely studied policy model architectures, highlighting its importance in advancing Sim2Real embodied intelligence.
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
Authors: Talor Abramovich; Meet Udeshi; Minghao Shao; Kilian Lieret; Haoran Xi; Kimberly Milner; Sofija Jancheska; John Yang; Carlos E Jimenez; Farshad Khorrami; Prashanth Krishnamurthy; Brendan Dolan-Gavitt; Muhammad Shafique; Karthik R Narasimhan; Ramesh Karri; Ofir Press
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited. We present *EnIGMA*, an LM agent for autonomously solving  Capture The Flag (CTF) challenges. We introduce new tools and interfaces to improve the agent's ability to find and exploit security vulnerabilities, focusing on interactive terminal programs.  These novel *Interactive Agent Tools* enable LM agents, for the first time,  to run interactive utilities, such as a debugger and a server connection tool, which are essential for solving these challenges.
Empirical analysis on 390 CTF challenges across four benchmarks demonstrate that these new tools and interfaces substantially improve our agent's performance, achieving state-of-the-art results on NYU CTF, Intercode-CTF, and CyBench. Finally, we analyze data leakage, developing new methods to quantify it and identifying a new phenomenon we term *soliloquizing*, where the model self-generates hallucinated observations without interacting with the environment.
Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
Authors: Mohammadreza Daneshvaramoli; Helia Karisani; Adam Lechowicz; Bo Sun; Cameron N Musco; Mohammad Hajiesmaili
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This paper introduces a family of learning-augmented algorithms for online knapsack problems that achieve near Pareto-optimal consistency-robustness trade-offs through a simple combination of trusted learning-augmented and worst-case algorithms. Our approach relies on succinct, practical predictions—single values or intervals estimating the minimum value of any item in an offline solution. Additionally, we propose a novel fractional-to-integral conversion procedure, offering new insights for online algorithm design.
Linear Contextual Bandits With Interference
Authors: Yang Xu; Wenbin Lu; Rui Song
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings where multiple units are present in the same round, interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in interference-aware settings, how to model interference in CB remains significantly underexplored. In this paper, we introduce a systematic framework to address interference in Linear CB (LinCB), bridging the gap between causal inference and online decision-making. We propose a series of algorithms that explicitly quantify the interference effect in the reward modeling process and provide comprehensive theoretical guarantees, including sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The effectiveness of our approach is demonstrated through simulations and a synthetic data generated based on MovieLens data.
LEMoN: Label Error Detection using Multimodal Neighbors
Authors: Haoran Zhang; Aparna Balagopalan; Nassim Oufattole; Hyewon Jeong; Yan Wu; Jiacheng Zhu; Marzyeh Ghassemi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large repositories of image-caption pairs are essential for the development of vision-language models. However, these datasets are often extracted from noisy data scraped from the web, and contain many mislabeled instances. In order to improve the reliability of downstream models, it is important to identify and filter images with incorrect captions. However, beyond filtering based on image-caption embedding similarity, no prior works have proposed other methods to filter noisy multimodal data, or concretely assessed the impact of noisy captioning data on downstream training. In this work, we propose, theoretically justify, and empirically validate LEMoN, a method to identify label errors in image-caption datasets. Our method leverages the multimodal neighborhood of image-caption pairs in the latent space of contrastively pretrained multimodal models to automatically identify label errors. Through empirical evaluations across eight datasets and twelve baselines, we find that LEMoN outperforms the baselines by over 3% in label error detection, and that training on datasets filtered using our method improves downstream captioning performance by more than 2 BLEU points over noisy training.
Interpreting the Repeated Token Phenomenon in Large Language Models
Authors: Itay Yona; Ilia Shumailov; Jamie Hayes; Yossi Gandelsman
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately repeat a single word when prompted to, and instead output unrelated text. This unexplained failure mode represents a *vulnerability*, allowing even end users to diverge models away from their intended behavior. We aim to explain the causes for this phenomenon and link it to the concept of "attention sinks", an emergent LLM behavior crucial for fluency, in which the initial token receives disproportionately high attention scores. Our investigation identifies the neural circuit responsible for attention sinks and shows how long repetitions disrupt this circuit. We extend this finding to other nonrepeating sequences that exhibit similar circuit disruptions.  To address this, we propose a targeted patch that effectively resolves the issue without negatively impacting the overall performance of the model.  This study provides a mechanistic explanation for an LLM vulnerability, demonstrating how interpretability can diagnose and address issues, and offering insights that pave the way for more secure and reliable models.
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
Authors: Guangyi Liu; Suzan Iloglu; Michael Caldara; Joseph W Durham; Michael M. Zavlanos
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynamic package induction rates, which can lead to increased package recirculation. To tackle this challenge, we introduce a Distributionally Robust Multi-Agent Reinforcement Learning (DRMARL) framework that learns a destination-to-chute mapping policy that is resilient to adversarial variations in induction rates. Specifically, DRMARL relies on group distributionally robust optimization (DRO) to learn a policy that performs well not only on average but also on each individual subpopulation of induction rates within the group that capture, for example, different seasonality or operation modes of the system. This approach is then combined with a novel contextual bandit-based estimator of the worst-case induction distribution for each state-action pair, significantly reducing the cost of exploration and thereby increasing the learning efficiency and scalability of our framework. Extensive simulations demonstrate that DRMARL achieves robust chute mapping in the presence of varying induction distributions, reducing package recirculation by an average of 80% in the simulation scenario.
Deep Electromagnetic Structure Design Under Limited Evaluation Budgets
Authors: Shijian Zheng; Fangxiao Jin; Shuhai Zhang; Quan Xue; Mingkui Tan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Electromagnetic structure (EMS) design plays a critical role in developing advanced antennas and materials, but remains challenging due to high-dimensional design spaces and expensive evaluations. While existing methods commonly employ high-quality predictors or generators to alleviate evaluations, they are often data-intensive and struggle with real-world scale and budget constraints. To address this, we propose a novel method called Progressive Quadtree-based Search (PQS). Rather than exhaustively exploring the high-dimensional space, PQS converts the conventional image-like layout into a quadtree-based hierarchical representation, enabling a progressive search from global patterns to local details. Furthermore, to lessen reliance on highly accurate predictors, we introduce a consistency-driven sample selection mechanism. This mechanism quantifies the reliability of predictions, balancing exploitation and exploration when selecting candidate designs. We evaluate PQS on two real-world engineering tasks, i.e., Dual-layer Frequency Selective Surface and High-gain Antenna. Experimental results show that our method can achieve satisfactory designs under limited computational budgets, outperforming baseline methods. In particular, compared to generative approaches, it cuts evaluation costs by 75∼85%, effectively saving 20.27∼38.80 days of product designing cycle.
Disparate Conditional Prediction in Multiclass Classifiers
Authors: Sivan Sabato; Eran Treister; Elad Yom-Tov
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose methods for auditing multiclass classifiers for fairness under multiclass equalized odds, by estimating the deviation from equalized odds when the classifier is not completely fair. We generalize to multiclass classifiers the measure of Disparate Conditional Prediction (DCP), originally suggested by Sabato & Yom-Tov (2020) for binary classifiers. DCP is defined as the fraction of the population for which the classifier predicts with conditional prediction probabilities that differ from the closest common baseline. We provide new local-optimization methods for estimating the multiclass DCP under two different regimes, one in which the conditional confusion matrices for each protected sub-population are known, and one in which these cannot be estimated, for instance, because the classifier is inaccessible or
because good-quality individual-level data is not available. These methods can be used to detect classifiers that likely treat a significant fraction of the population unfairly. Experiments demonstrate the accuracy of the methods. The code for the experiments is provided as supplementary material.
Supercharging Graph Transformers with Advective Diffusion
Authors: Qitian Wu; Chenxiao Yang; Kaipeng Zeng; Michael M. Bronstein
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structures, one important aspect neglected by prior studies is how machine learning models generalize under topological shifts. This paper proposes AdvDIFFormer, a physics-inspired graph Transformer model designed to address this challenge. The model is derived from advective diffusion equations which describe a class of continuous message passing process with observed and latent topological structures. We show that AdvDIFFormer has provable capability for controlling generalization error with topological shifts, which in contrast cannot be guaranteed by graph diffusion models, i.e., the generalization of common graph neural networks in continuous space. Empirically, the model demonstrates superiority in various predictive tasks across information networks, molecular screening and protein interactions
Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks
Authors: Sungwon Kim; Namkyeong Lee; Yunyoung Doh; Seungmin Shin; Guimok Cho; Seung-Won Jeon; Sangkook Kim; Chanyoung Park
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mesh-based 3D static analysis methods have recently emerged as efficient alternatives to traditional computational numerical solvers, significantly reducing computational costs and runtime for various physics-based analyses. However, these methods primarily focus on surface topology and geometry, often overlooking the inherent thickness of real-world 3D objects, which exhibits high correlations and similar behavior between opposing surfaces. This limitation arises from the disconnected nature of these surfaces and the absence of internal edge connections within the mesh.  
In this work, we propose a novel framework, the Thickness-aware E(3)-Equivariant 3D Mesh Neural Network (T-EMNN), that effectively integrates the thickness of 3D objects while maintaining the computational efficiency of surface meshes. Additionally, we introduce data-driven coordinates that encode spatial information while preserving E(3)-equivariance or invariance properties, ensuring consistent and robust analysis. Evaluations on a real-world industrial dataset demonstrate the superior performance of T-EMNN in accurately predicting node-level 3D deformations, effectively capturing thickness effects while maintaining computational efficiency.
BAME: Block-Aware Mask Evolution for Efficient N:M Sparse Training
Authors: Chenyi yang; Wenjie Nie; Yuxin Zhang; Yuhang Wu; Xiawu Zheng; GUANNAN JIANG; Rongrong Ji
Venue: ICML 2025 (ICML 2025 poster)

Abstract: N:M sparsity stands as a progressively important tool for DNN compression, achieving practical speedups by stipulating at most N non-zero components within M sequential weights. Unfortunately, most existing works identify the N:M sparse mask through dense backward propagation to update all weights, which incurs exorbitant training costs. In this paper, we introduce BAME, a method that maintains consistent sparsity throughout the N:M sparse training process. BAME perpetually keeps both sparse forward and backward propagation, while iteratively performing weight pruning-and-regrowing within designated weight blocks to tailor the N:M mask. These blocks are selected through a joint assessment based on accumulated mask oscillation frequency and expected loss reduction of mask adaptation, thereby ensuring stable and efficient identification of the optimal N:M mask. Our empirical results substantiate the effectiveness of BAME, illustrating it performs comparably to or better than previous works that fully maintaining dense backward propagation during training. For instance, BAME attains a 72.0% top-1 accuracy while training a 1:16 sparse ResNet-50 on ImageNet, eclipsing SR-STE by 0.5%, despite achieving 2.37 training FLOPs reduction. Code is released at \url{https://github.com/BAME-xmu/BAME}
Low-Rank Thinning
Authors: Annabelle Michael Carrell; Albert Gong; Abhishek Shetty; Raaz Dwivedi; Lester Mackey
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The goal in thinning is to summarize a dataset using a small set of representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel Halving and Compress can match the quality of uniform subsampling while substantially reducing the number of summary points. However, existing guarantees cover only a restricted range of distributions and kernel-based quality measures and suffer from pessimistic dimension dependence. To address these deficiencies, we introduce a new low-rank analysis of sub-Gaussian thinning that applies to any distribution and any kernel, guaranteeing high-quality compression whenever the kernel or data matrix is approximately low-rank. To demonstrate the broad applicability of the techniques, we design practical sub-Gaussian thinning approaches that improve upon the best known guarantees for approximating attention in transformers, accelerating stochastic gradient training through reordering, and distinguishing distributions in near-linear time.