GenAI4ELab/papercli-papers huggingface dataset:
Reinforcement Learning for Quantum Control under Physical Constraints
Authors: Jan Ole Ernst; Aniket Chatterjee; Tim Franzmeyer; Axel Kuhn
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Quantum control is concerned with the realisation of desired dynamics in quantum systems, serving as a linchpin for advancing quantum technologies and fundamental research. 
Analytic approaches and standard optimisation algorithms do not yield satisfactory solutions for more complex quantum systems, and especially not for real world quantum systems which are open and noisy. 
We devise a physics-constrained Reinforcement Learning (RL) algorithm that restricts the space of possible solutions.
We incorporate priors about the desired time scales of the quantum state dynamics - as well as realistic control signal limitations - as constraints to the RL algorithm. 
These constraints improve solution quality and enhance computational scaleability. 
We evaluate our method on three broadly relevant quantum systems and incorporate real-world complications, arising from dissipation and control signal perturbations. 
We achieve both higher fidelities - which exceed 0.999 across all systems -  and better robustness to time-dependent perturbations and experimental imperfections than previous methods. 
Lastly, we demonstrate that incorporating multi-step feedback can yield solutions robust even to strong perturbations. Our implementation can be found at: https://github.com/jan-o-e/RL4qcWpc.
Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions
Authors: Yik Siu Chan; Narutatsu Ri; Yuxin Xiao; Marzyeh Ghassemi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite extensive safety alignment efforts, large language models (LLMs) remain vulnerable to jailbreak attacks that elicit harmful behavior. While existing studies predominantly focus on attack methods that require technical expertise, two critical questions remain underexplored: (1) Are jailbroken responses truly useful in enabling average users to carry out harmful actions? (2) Do safety vulnerabilities exist in more common, simple human-LLM interactions? In this paper, we demonstrate that LLM responses most effectively facilitate harmful actions when they are both *actionable* and *informative*---two attributes easily elicited in multi-step, multilingual interactions. Using this insight, we propose HarmScore, a jailbreak metric that measures how effectively an LLM response enables harmful actions, and Speak Easy, a simple multi-step, multilingual attack framework. Notably, by incorporating Speak Easy into direct request and jailbreak baselines, we see an average absolute increase of $0.319$ in Attack Success Rate and $0.426$ in HarmScore in both open-source and proprietary LLMs across four safety benchmarks. Our work reveals a critical yet often overlooked vulnerability: Malicious users can easily exploit common interaction patterns for harmful intentions.
Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables
Authors: Arhit Chakrabarti; Yang Ni; Debdeep Pati; Bani Mallick
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We consider the problem of clustering grouped data for which the observations may include group-specific variables in addition to the variables that are shared across groups. This type of data is quite common; for example, in cancer genomic studies, molecular information is available for all cancers whereas cancer-specific clinical information may only be available for certain cancers. Existing grouped clustering methods only consider the shared variables but ignore valuable information from the group-specific variables. To allow for these group-specific variables to aid in the clustering, we propose a novel Bayesian nonparametric approach, termed global-local (GLocal) Dirichlet process, that models the "global-local" structure of the observations across groups. We characterize the GLocal Dirichlet process using the stick-breaking representation and the representation as a limit of a finite mixture model. We theoretically quantify the approximation errors of the truncated prior, the corresponding finite mixture model, and the associated posterior distribution. We develop a fast variational Bayes algorithm for scalable posterior inference, which we illustrate with extensive simulations and a TCGA pan-gastrointestinal cancer dataset.
Self-Organizing Visual Prototypes for Non-Parametric Representation Learning
Authors: Thalles Silva; Helio Pedrini; Adín Ramírez Rivera
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present Self-Organizing Visual Prototypes (SOP), a new training technique for unsupervised visual feature learning. Unlike existing prototypical self-supervised learning (SSL) methods that rely on a single prototype to encode all relevant features of a hidden cluster in the data, we propose the SOP strategy. In this strategy, a prototype is represented by many semantically similar representations, or support embeddings (SEs), each containing a complementary set of features that together better characterize their region in space and maximize training performance. We reaffirm the feasibility of non-parametric SSL by introducing novel non-parametric adaptations of two loss functions that implement the SOP strategy. Notably, we introduce the SOP Masked Image Modeling (SOP-MIM) task, where masked representations are reconstructed from the perspective of multiple non-parametric local SEs. We comprehensively evaluate the representations learned using the SOP strategy on a range of benchmarks, including retrieval, linear evaluation, fine-tuning, and object detection. Our pre-trained encoders achieve state-of-the-art performance on many retrieval benchmarks and demonstrate increasing performance gains with more complex encoders.
Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation
Authors: Yixin Chen; Wenjing Chen; Alan Kuhnle
Venue: ICML 2025 (ICML 2025 poster)

Abstract: With the rapid growth of data in modern applications, parallel combinatorial algorithms for maximizing non-monotone submodular functions have gained significant attention. In the parallel computation setting, the state-of-the-art approximation ratio of $1/e$ is achieved by a continuous algorithm (Ene & Nguyen, 2020) with adaptivity $\mathcal O (log(n))$. In this work, we focus on size constraints and present the first combinatorial algorithm matching this bound – a randomized parallel approach achieving $1/e − \epsilon$ approximation ratio. This result bridges
the gap between continuous and combinatorial approaches for this problem. As a byproduct, we also develop a simpler $(1/4 − \epsilon)$-approximation algorithm with high probability $(\ge 1 − 1/n)$. Both algorithms achieve $\mathcal O (log(n) log(k))$ adaptivity and $\mathcal O (n log(n) log(k)) query complexity. Empirical results show our algorithms achieve competitive objective values, with the $(1/4 − \epsilon)$-approximation algorithm particularly efficient in queries.
Are Large Language Models Ready for Multi-Turn Tabular Data Analysis?
Authors: Jinyang Li; Nan Huo; Yan Gao; Jiayi Shi; Yingxiu Zhao; Ge Qu; Bowen Qin; Yurong Wu; Xiaodong Li; Chenhao Ma; Jian-Guang Lou; Reynold Cheng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Conversational Tabular Data Analysis, a collaboration between humans and machines, enables real-time data exploration for informed decision-making. The challenges and costs of collecting realistic conversational logs for tabular data analysis hinder comprehensive quantitative evaluation of Large Language Models (LLMs) in this task. To mitigate this issue, we introduce **CoTA**, a new benchmark to evaluate LLMs on conversational tabular data analysis. **CoTA** contains 1013 conversations, covering 4 practical scenarios: Normal, Action, Private, and Private Action. Notably, **CoTA** is constructed by an economical multi-agent environment, Decision Company, with few human efforts. This environment ensures efficiency and scalability of generating new conversational data. Our comprehensive study, conducted by data analysis experts, demonstrates that Decision Company is capable of producing diverse and high-quality data, laying the groundwork for efficient data annotation. We evaluate popular and advanced LLMs in **CoTA**, which highlights the challenges of conversational tabular data analysis. Furthermore, we propose Adaptive Conversation Reflection (ACR), a self-generated reflection strategy that guides LLMs to learn from successful histories. Experiments demonstrate that ACR can evolve LLMs into effective conversational data analysis agents, achieving a relative performance improvement of up to 35.14%.
Prediction via Shapley Value Regression
Authors: Amr Alkhatib; Roman Bresson; Henrik Boström; Michalis Vazirgiannis
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To overcome this, a novel method, called ViaSHAP, is proposed, that learns a function to compute Shapley values, from which the predictions can be derived directly by summation. Two approaches to implement the proposed method are explored; one based on the universal approximation theorem and the other on the Kolmogorov-Arnold representation theorem. Results from a large-scale empirical investigation are presented, showing that ViaSHAP using Kolmogorov-Arnold Networks performs on par with state-of-the-art algorithms for tabular data. It is also shown that the explanations of ViaSHAP are significantly more accurate than the popular approximator FastSHAP on both tabular data and images.
Towards a Formal Theory of Representational Compositionality
Authors: Eric Elmoznino; Thomas Jiralerspong; Yoshua Bengio; Guillaume Lajoie
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Compositionality is believed to be fundamental to intelligence. In humans, it underlies the structure of thought and language. In AI, it enables a powerful form of out-of-distribution generalization, in which a model systematically adapts to novel combinations of known concepts. However, while we have strong intuitions about what compositionality is, we lack satisfying formal definitions for it. Here, we propose such a definition called representational compositionality that is conceptually simple, quantitative, and grounded in algorithmic information theory. Intuitively, representational compositionality states that a compositional representation is both expressive and describable as a simple function of parts. We validate our definition on both real and synthetic data, and show how it unifies disparate intuitions from across the literature in both AI and cognitive science. We hope that our definition can inspire the design of novel, theoretically-driven models that better capture the mechanisms of compositional thought. We make our code available at https://github.com/EricElmoznino/complexity_compositionality.
Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data
Authors: Corinna Cortes; Anqi Mao; Mehryar Mohri; Yutao Zhong
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Class imbalance remains a major challenge in machine learning, especially in multi-class problems with long-tailed distributions. Existing methods, such as data resampling, cost-sensitive techniques, and logistic loss modifications, though popular and often effective, lack solid theoretical foundations. As an example, we demonstrate that cost-sensitive methods  are not Bayes-consistent. This paper introduces a novel theoretical framework for analyzing generalization in imbalanced classification. We propose a new class-imbalanced margin loss function for both binary and multi-class settings, prove its strong $H$-consistency, and derive corresponding learning guarantees based on empirical loss and a new notion of class-sensitive Rademacher complexity. Leveraging these theoretical results, we devise novel and general learning algorithms, IMMAX (*Imbalanced Margin Maximization*), which incorporate confidence margins and are applicable to various hypothesis sets. While our focus is theoretical, we also present extensive empirical results demonstrating the effectiveness of our algorithms compared to existing baselines.
Learning with Exact Invariances in Polynomial Time
Authors: Ashkan Soleymani; Behrooz Tahmasebi; Stefanie Jegelka; Patrick Jaillet
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: We study the statistical-computational trade-offs for learning with exact invariances (or symmetries) using kernel regression. Traditional methods, such as data augmentation, group averaging, canonicalization, and frame-averaging, either fail to provide a polynomial-time solution or are not applicable in the kernel setting. However, with oracle access to the geometric properties of the input space, we propose a polynomial-time algorithm that learns a classifier with *exact* invariances. Moreover, our approach achieves the same excess population risk (or generalization error) as the original kernel regression problem.  To the best of our knowledge, this is the first polynomial-time algorithm to achieve exact (as opposed to approximate) invariances in this setting. In developing our approach, we also resolve a question recently posed by Dıaz et al. (2025) on efficient computation of invariant bases and kernels with respect to finite groups, even when the group size is prohibitively large. Our proof leverages tools from differential geometry, spectral theory, and optimization. A key result in our development is a new reformulation of the problem of learning under invariances as optimizing an infinite number of linearly constrained convex quadratic programs, which may be of independent interest.
Action-Dependent Optimality-Preserving Reward Shaping
Authors: Grant Collier Forbes; Jianxun Wang; Leonardo Villalobos-Arias; Arnav Jhala; David Roberts
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent RL research has utilized reward shaping--particularly complex shaping rewards such as intrinsic motivation (IM)--to encourage agent exploration in sparse-reward environments. While often effective, ``reward hacking'' can lead to the shaping reward being optimized at the expense of the extrinsic reward, resulting in a suboptimal policy. Potential-Based Reward Shaping (PBRS) techniques such as Generalized Reward Matching (GRM) and Policy-Invariant Explicit Shaping (PIES) have mitigated this. These methods allow for implementing IM without altering optimal policies. In this work we show that they are effectively unsuitable for complex, exploration-heavy environments with long-duration episodes. To remedy this, we introduce Action-Dependent Optimality Preserving Shaping (ADOPS), a method of converting intrinsic rewards to an optimality-preserving form that allows agents to utilize IM more effectively in the extremely sparse environment of Montezuma's Revenge. We also prove ADOPS accommodates reward shaping functions that cannot be written in a potential-based form: while PBRS-based methods require the cumulative discounted intrinsic return be independent of actions, ADOPS allows for intrinsic cumulative returns to be dependent on agents' actions while still preserving the optimal policy set. We show how action-dependence enables ADOPS's to preserve optimality while learning in complex, sparse-reward environments where other methods struggle.
DiffusionVLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression
Authors: Junjie Wen; Yichen Zhu; Minjie Zhu; Zhibin Tang; Jinming Li; Zhongyi Zhou; Xiaoyu Liu; Chaomin Shen; Yaxin Peng; Feifei Feng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we present DiffusionVLA, a novel framework that integrates autoregressive reasoning with diffusion policies to address the limitations of existing methods: while autoregressive Vision-Language-Action (VLA) models lack precise and robust action generation, diffusion-based policies inherently lack reasoning capabilities. Central to our approach is autoregressive reasoning — a task decomposition and explanation process enabled by a pre-trained VLM — to guide diffusion-based action policies. To tightly couple reasoning with action generation, we introduce a reasoning injection module that directly embeds self-generated reasoning phrases into the policy learning process. The framework is simple, flexible, and efficient, enabling seamless deployment across diverse robotic platforms.

We conduct extensive experiments using multiple real robots to validate the effectiveness of DiVLA. Our tests include a challenging factory sorting task, where DiVLA successfully categorizes objects, including those not seen during training. The reasoning injection module enhances interpretability, enabling explicit failure diagnosis by visualizing the model’s decision process. Additionally, we test DiVLA on a zero-shot bin-picking task, achieving \textbf{63.7\% accuracy on 102 previously unseen objects}. Our method demonstrates robustness to visual changes, such as distractors and new backgrounds, and easily adapts to new embodiments. Furthermore, DiVLA can follow novel instructions and retain conversational ability. Notably, DiVLA is data-efficient and fast at inference; our smallest DiVLA-2B runs 82Hz on a single A6000 GPU. Finally, we scale the model from 2B to 72B parameters, showcasing improved generalization capabilities with increased model size.
Robust Noise Attenuation via Adaptive Pooling of Transformer Outputs
Authors: Greyson Brothers
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: We investigate the design of pooling methods used to summarize the outputs of transformer embedding models, primarily motivated by reinforcement learning and vision applications. This work considers problems where a subset of the input vectors contains requisite information for a downstream task (signal) while the rest are distractors (noise). By framing pooling as vector quantization with the goal of minimizing signal loss, we demonstrate that the standard methods used to aggregate transformer outputs, AvgPool, MaxPool, and ClsToken, are vulnerable to performance collapse as the signal-to-noise ratio (SNR) of inputs fluctuates. We then show that an attention-based *adaptive pooling* method can approximate the signal-optimal vector quantizer within derived error bounds for any SNR. Our theoretical results are first validated by supervised experiments on a synthetic dataset designed to isolate the SNR problem, then generalized to standard relational reasoning, multi-agent reinforcement learning, and vision benchmarks with noisy observations, where transformers with adaptive pooling display superior robustness across tasks.
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
Authors: Thomas Schmied; Thomas Adler; Vihang Prakash Patil; Maximilian Beck; Korbinian Pöppel; Johannes Brandstetter; Günter Klambauer; Razvan Pascanu; Sepp Hochreiter
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling. Existing models are primarily based on the Transformer architecture, which results in powerful agents. However, due to slow inference times, Transformer-based approaches are impractical for real-time applications, such as robotics. Recently, modern recurrent architectures, such as xLSTM and Mamba, have been proposed that exhibit parallelization benefits during training similar to the Transformer architecture while offering fast inference. In this work, we study the aptitude of these modern recurrent architectures for large action models. Consequently, we propose a Large Recurrent Action Model (LRAM) with an xLSTM at its core that comes with linear-time inference complexity and natural sequence length extrapolation abilities. Experiments on 432 tasks from 6 domains show that LRAM compares favorably to Transformers in terms of performance and speed.
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Authors: Zhiyang Wang; Juan Cervino; Alejandro Ribeiro
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs. Despite their impressive performances in various graph learning tasks, the theoretical understanding of their generalization capability is still lacking. Previous GNN generalization bounds ignore the underlying graph structures, often leading to bounds that increase with the number of nodes – a behavior contrary to the one experienced in practice. In this paper, we take a manifold perspective to establish the statistical generalization theory of GNNs on graphs sampled from a manifold in the spectral domain. As demonstrated empirically, we prove that the generalization bounds of GNNs decrease linearly with the size of the graphs in the logarithmic scale, and increase linearly with the spectral continuity constants of the filter functions. Notably, our theory explains both node-level and graph-level tasks. Our result has two implications: i) guaranteeing the generalization of GNNs to unseen data over manifolds; ii) providing insights into the practical design of GNNs, i.e., restrictions on the discriminability of GNNs are necessary to obtain a better generalization performance. We demonstrate our generalization bounds of GNNs using synthetic and multiple real-world datasets.
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Authors: Xiang Fu; Brandon M Wood; Luis Barroso-Luque; Daniel S. Levine; Meng Gao; Misko Dzamba; C. Lawrence Zitnick
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate to improved results on downstream physical property prediction tasks. In this paper, we propose testing MLIPs on their practical ability to conserve energy during molecular dynamic simulations. If passed, improved correlations are found between test errors and their performance on physical property prediction tasks. We identify choices which may lead to models failing this test, and use these observations to improve upon highly-expressive models. The resulting model, eSEN, provides state-of-the-art results on a range of physical property prediction tasks, including materials stability prediction, thermal conductivity prediction, and phonon calculations.