GenAI4ELab/papercli-papers huggingface dataset:
The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering
Authors: Zhuowei Li; Haizhou Shi; Yunhe Gao; Di Liu; Zhenting Wang; Yuxiao Chen; Ting Liu; Long Zhao; Hao Wang; Dimitris N. Metaxas
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Vision-Language Models (LVLMs) can reason effectively over both textual and visual inputs, but they tend to hallucinate syntactically coherent yet visually ungrounded contents. In this paper, we investigate the internal dynamics of hallucination by examining the tokens logits rankings throughout the generation process, revealing three key patterns in how LVLMs process information: 
(1) *gradual visual information loss* -- visually grounded tokens gradually become less favored throughout generation, and 
(2) *early excitation* -- semantically meaningful tokens achieve peak activation in the layers earlier than the final layer.
(3) *hidden genuine information* -- visually grounded tokens though not being eventually decided still retain relatively high rankings at inference.
Based on these insights, we propose **VISTA** (**V**isual **I**nformation **S**teering with **T**oken-logit **A**ugmentation), a training-free inference-time intervention framework that reduces hallucination while promoting genuine information. VISTA works by combining two complementary approaches: reinforcing visual information in activation space and leveraging early layer activations to promote semantically meaningful decoding. 
Compared to existing methods, VISTA requires no external supervision and is applicable to various decoding strategies. 
Extensive experiments show that VISTA on average reduces hallucination by about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
Authors: Mislav Balunovic; Jasper Dekoninck; Nikola Jovanović; Ivo Petrov; Martin Vechev
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While Large Language Models (LLMs) demonstrate impressive performance in mathematics, existing math benchmarks come with significant limitations. Many focus on problems with fixed ground-truth answers, and are often saturated due to problem simplicity or the viability of guessing or memorization. Crucially, they capture only a narrow subset of relevant math problems. To address this research gap, we introduce MathConstruct, a new benchmark of 127 challenging problems sourced from various math competitions, which targets *constructive proofs*, a widely encountered problem type requiring the construction of mathematical objects with specific properties. These proofs are particularly suitable for LLM evaluation, as solution correctness can be easily verified. Our automated verifiers also enable MathConstruct to generate problem variations, used to evaluate robustness. State-of-the-art LLMs solve only 41\% of MathConstruct problems, highlighting its complexity and importance for LLM evaluation.
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
Authors: Tom Wollschläger; Jannes Elstner; Simon Geisler; Vincent Cohen-Addad; Stephan Günnemann; Johannes Gasteiger
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The safety alignment of large language models (LLMs) can be circumvented through adversarially crafted inputs, yet the mechanisms by which these attacks bypass safety barriers remain poorly understood. Prior work suggests that a *single* refusal direction in the model's activation space determines whether an LLM refuses a request. In this study, we propose a novel gradient-based approach to representation engineering and use it to identify refusal directions. Contrary to prior work, we uncover multiple independent directions and even multi-dimensional *concept cones* that mediate refusal. Moreover, we show that orthogonality alone does not imply independence under intervention, motivating the notion of *representational independence* that accounts for both linear and non-linear effects. Using this framework, we identify mechanistically independent refusal directions. We show that refusal mechanisms in LLMs are governed by complex spatial structures and identify functionally independent directions, confirming that multiple distinct mechanisms drive refusal behavior. Our gradient-based approach uncovers these mechanisms and can further serve as a foundation for future work on understanding LLMs.
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
Authors: Paris Giampouras; HanQin Cai; Rene Vidal
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we focus on a matrix factorization-based approach for robust recovery of low-rank  asymmetric matrices from corrupted measurements. We propose an  Overparameterized Preconditioned Subgradient Algorithm (OPSA) and provide, for the first time in the literature, linear convergence rates independent of the rank of the sought asymmetric matrix in the presence of gross corruptions. Our work goes beyond existing results in preconditioned-type approaches addressing their current limitation, i.e., the lack of convergence guarantees in the case of asymmetric matrices of unknown rank. By applying our approach to (robust) matrix sensing, we highlight its merits when the measurement operator satisfies a mixed-norm restricted isometry property. Lastly, we present extensive numerical experiments that validate our theoretical results and demonstrate the effectiveness of our approach for different levels of overparameterization and corruption from outliers.
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
Authors: Sebastian Farquhar; Vikrant Varma; David Lindner; David Elson; Caleb Biddulph; Ian Goodfellow; Rohin Shah
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Future advanced AI systems may learn sophisticated strategies through reinforcement learning (RL) that humans cannot understand well enough to safely evaluate. We propose a training method which avoids agents learning undesired multi-step plans that receive high reward (multi-step "reward hacks") even if humans are not able to detect that the behavior is undesired. The method, Myopic Optimization with Non-myopic Approval (MONA), works by combining short-sighted optimization with far-sighted reward. We demonstrate that MONA can prevent multi-step reward hacking that ordinary RL causes, even without being able to detect the reward hacking and without any extra information that ordinary RL does not get access to. We study MONA empirically in three settings which model different misalignment failure modes including 2-step environments with LLMs representing delegated oversight and encoded reasoning and longer-horizon gridworld environments representing sensor tampering.
Fast and Provable Algorithms for Sparse PCA with Improved Sample Complexity
Authors: Jian-Feng Cai; Zhuozhi XIAN; Jiaxi Ying
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We explore the single-spiked covariance model within the context of sparse principal component analysis (PCA), which aims to recover a sparse unit vector from noisy samples. From an information-theoretic perspective, $O(k \log p)$ observations are sufficient to recover a $k$-sparse $p$-dimensional vector $\mathbf{v}$. However, existing polynomial-time methods require at least $O(k^2)$ samples for successful recovery, highlighting a significant gap in sample efficiency. To bridge this gap, we introduce a novel thresholding-based algorithm that requires only $\Omega(k \log p)$ samples, provided the signal strength $\lambda = \Omega(||\mathbf{v}||_\infty^{-1})$. We also propose a two-stage nonconvex algorithm that further enhances estimation performance. This approach integrates our thresholding algorithm with truncated power iteration, achieving the minimax optimal rate of statistical error under the desired sample complexity. Numerical experiments validate the superior performance of our algorithms in terms of estimation accuracy and computational efficiency.
When, Where and Why to Average Weights?
Authors: Niccolò Ajroldi; Antonio Orvieto; Jonas Geiping
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Averaging checkpoints along the training trajectory is a simple yet powerful approach to improve the generalization performance of Machine Learning models and reduce training time. Motivated by these potential gains, and in an effort to fairly and thoroughly benchmark this technique, we present an extensive evaluation of averaging techniques in modern Deep Learning, which we perform using AlgoPerf, a large-scale benchmark for optimization algorithms. We investigate whether weight averaging can reduce training time, improve generalization, and replace learning rate decay, as suggested by recent literature. Our evaluation across seven architectures and datasets reveals that averaging significantly accelerates training and yields considerable efficiency gains across all considered workloads, at the price of a minimal implementation and memory cost, while mildly improving generalization. Finally, we explore the relationship between averaging and learning rate annealing and show that combining the two achieves optimal performance.
Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity
Authors: Ningyuan Teresa Huang; Miguel Sarabia; Abhinav Moudgil; Pau Rodriguez; Luca Zappella; Federico Danieli
Venue: ICML 2025 (ICML 2025 poster)

Abstract: State-Space Models (SSMs), and particularly Mamba, have recently emerged as a promising alternative to Transformers. Mamba introduces input selectivity to its SSM layer (S6) and incorporates convolution and gating into its block definition. While these modifications do improve Mamba's performance over its SSM predecessors, it remains largely unclear how Mamba leverages the additional functionalities provided by input selectivity, and how these interact with the other operations in the Mamba architecture. In this work, we demystify the role of input selectivity in Mamba, investigating its impact on function approximation power, long-term memorization, and associative recall capabilities.
In particular: (i) we prove that the S6 layer of Mamba can represent projections onto *Haar wavelets*, providing an edge over its Diagonal SSM (S4D) predecessor in approximating discontinuous functions commonly arising in practice; (ii) we show how the S6 layer can dynamically counteract memory decay; (iii) we provide analytical solutions to the MQAR associative recall task using the Mamba architecture with different mixers --- Mamba, Mamba-2, and S4D. We demonstrate the tightness of our theoretical constructions with empirical results on concrete tasks. Our findings offer a mechanistic understanding of Mamba and reveal opportunities for improvement.
Progressive Tempering Sampler with Diffusion
Authors: Severi Rissanen; RuiKang OuYang; Jiajun He; Wenlin Chen; Markus Heinonen; Arno Solin; José Miguel Hernández-Lobato
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities. 
However, despite significant advancements, they still fall short of the state-of-the-art MCMC approach, Parallel Tempering (PT), when it comes to the efficiency of target evaluations. On the other hand, unlike a well-trained neural sampler, PT yields only dependent samples and needs to be rerun---at considerable computational cost---whenever new samples are required. To address these weaknesses, we propose the Progressive Tempering Sampler with Diffusion (PTSD), which trains diffusion models sequentially across temperatures, leveraging the advantages of PT to improve the training of neural samplers. We also introduce a novel method to combine high-temperature diffusion models to generate approximate lower-temperature samples, which are minimally refined using MCMC and used to train the next diffusion model. PTSD enables efficient reuse of sample information across temperature levels while generating well-mixed, uncorrelated samples. Our method significantly improves target evaluation efficiency,
outperforming diffusion-based neural samplers.
Provable Maximum Entropy Manifold Exploration via Diffusion Models
Authors: Riccardo De Santi; Marin Vlastelica; Ya-Ping Hsieh; Zebang Shen; Niao He; Andreas Krause
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Exploration is critical for solving real-world decision-making problems such as scientific discovery, where the objective is to generate truly novel designs rather than mimic existing data distributions.  In this work, we address the challenge of leveraging the representational power of generative models for exploration without relying on explicit uncertainty quantification. We introduce a novel framework that casts exploration as entropy maximization over the approximate data manifold implicitly defined by a pre-trained diffusion model. Then, we present a novel principle for exploration based on density estimation, a problem well-known to be challenging in practice. To overcome this issue and render this method truly scalable, we leverage a fundamental connection between the entropy of the density induced by a diffusion model and its score function. Building on this, we develop an algorithm based on mirror descent that solves the exploration problem as sequential fine-tuning of a pre-trained diffusion model. We prove its convergence to the optimal exploratory diffusion model under realistic assumptions by leveraging recent understanding of mirror flows. Finally, we empirically evaluate our approach on both synthetic and high-dimensional text-to-image diffusion, demonstrating promising results.
A Sample Efficient Conditional Independence Test in the Presence of Discretization
Authors: Boyang Sun; Yu Yao; Xinshuai Dong; Zongfang Liu; Tongliang Liu; Yumou Qiu; Kun Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Conditional independence (CI) test is a fundamental concept in statistics. In many real-world scenarios, some variables may be difficult to measure accurately, often leading to data being represented as discretized values. Applying CI tests directly to discretized data, however, can lead to incorrect conclusions about the independence of latent variables. To address this, recent advancements have sought to infer the correct CI relationship between the latent variables by binarizing the observed data. However, this process results in a loss of information, which degrades the test's performance, particularly with small sample sizes. Motivated by this, this paper introduces a new sample-efficient CI test that does not rely on the binarization process. We find that the relationship can be established by addressing an \textit{over-identifying} restriction problem with \textit{Generalized Method of Moments} (GMM). Based on this finding, we have designed a new test statistic, and its asymptotic distribution has been derived. 
Empirical results across various datasets show that our method consistently outperforms existing ones.
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective
Authors: yuanhong zhang; Muyao Yuan; Weizhan Zhang; Tieliang Gong; Wen Wen; Jiangyong Ying; Weijie Shi
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: The Segment Anything Model (SAM), a vision foundation model, exhibits impressive zero-shot capabilities in general tasks but struggles in specialized domains. Parameter-efficient fine-tuning (PEFT) is a promising approach to unleash the potential of SAM in novel scenarios. However, existing PEFT methods for SAM neglect the domain-invariant relations encoded in the pre-trained model. To bridge this gap, we propose InfoSAM, an information-theoretic approach that enhances SAM fine-tuning by distilling and preserving its pre-trained segmentation knowledge. Specifically, we formulate the knowledge transfer process as two novel mutual information-based objectives: (i) to compress the domain-invariant relation extracted from pre-trained SAM, excluding pseudo-invariant information as possible, and (ii) to maximize mutual information between the relational knowledge learned by the teacher (pre-trained SAM) and the student (fine-tuned model).  The proposed InfoSAM establishes a robust distillation framework for PEFT of SAM. Extensive experiments across diverse benchmarks validate InfoSAM's effectiveness in improving SAM family's performance on real-world tasks, demonstrating its adaptability and superiority in handling specialized scenarios. The code and models are available at https://muyaoyuan.github.io/InfoSAM_Page.
Schwarz–Schur Involution: Lightspeed Differentiable Sparse Linear Solvers
Authors: Yu Wang; Mazdak Abulnaga; Yaël Balbastre; Bruce Fischl
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Sparse linear solvers or generalized deconvolution are fundamental to science and engineering, applied in partial differential equations (PDEs), scientific computing, computer vision, and beyond. Indirect solvers possess characteristics that make them undesirable as stable differentiable modules; existing direct solvers, though reliable, are too expensive to be adopted in neural architectures. We substantially accelerate direct sparse solvers by up to three orders of magnitude, violating common assumptions that direct solvers are too slow. We "condense" a sparse Laplacian matrix into a dense tensor, a compact data structure that batch-wise stores the Dirichlet-to-Neumann matrices, reducing the sparse solving to recursively merging pairs of dense matrices that are much smaller. The batched small dense systems are sliced and inverted in parallel to take advantage of dense GPU BLAS kernels, highly optimized in the era of deep learning. Our method is efficient, qualified as a strong zero-shot baseline for AI-based PDE solving, and a reliable differentiable module integrable into machine learning pipelines.
Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
Authors: Yilun Kong; Guozheng Ma; Qi Zhao; Haoyu Wang; Li Shen; Xueqian Wang; Dacheng Tao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite recent advancements in offline multi-task reinforcement learning (MTRL) have harnessed the powerful capabilities of the Transformer architecture, most approaches focus on a limited number of tasks, with scaling to extremely massive tasks remaining a formidable challenge. In this paper,  we first revisit the key impact of task numbers on current MTRL method, and further reveal that naively expanding the parameters proves insufficient to counteract the performance degradation as the number of tasks escalates. Building upon these insights, we propose M3DT, a novel mixture-of-experts (MoE) framework that tackles task scalability by further unlocking the model’s parameter scalability.  Specifically, we enhance both the architecture and the optimization of the agent, where we strengthen the Decision Transformer (DT) backbone with MoE to reduce task load on parameter subsets, and introduce a three-stage training mechanism to facilitate efficient training with optimal performance. Experimental results show that, by increasing the number of experts, M3DT not only consistently enhances its performance as model expansion on the fixed task numbers, but also exhibits remarkable task scalability, successfully extending to 160 tasks with superior performance.
MITIGATING OVER-EXPLORATION IN LATENT SPACE OPTIMIZATION USING LES
Authors: Omer Ronen; Ahmed Imtiaz Humayun; Richard Baraniuk; Randall Balestriero; Bin Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We develop Latent Exploration Score (LES) to mitigate over-exploration in Latent Space Optimization (LSO), a popular method for solving black-box discrete optimization problems. LSO utilizes continuous optimization within the latent space of a Variational Autoencoder (VAE) and is known to be susceptible to over-exploration, which manifests in unrealistic solutions that reduce its practicality. LES leverages the trained decoder’s approximation of the data distribution, and can be employed with any VAE decoder–including pretrained ones–without additional training, architectural changes or access to the training data. Our evaluation across five LSO benchmark tasks and twenty-two VAE models demonstrates that LES always enhances the quality of the solutions while maintaining high objective values, leading to improvements over existing solutions in most cases. We believe that new avenues to LSO will be opened by LES’ ability to identify out of distribution areas, differentiability, and computational tractability.
Online Linear Classification with Massart Noise
Authors: Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We study the task of online learning in the presence of Massart noise. Specifically, instead of assuming that the online adversary chooses an arbitrary sequence 
of labels, we assume that the context $\boldsymbol{x}$ is selected adversarially but 
the label $y$ presented to the learner disagrees with the ground-truth label 
of $\boldsymbol{x}$ with unknown probability {\em at most} $\eta$. 
We focus on the fundamental 
class of $\gamma$-margin linear classifiers and 
present the first computationally efficient algorithm 
that achieves mistake bound $\eta T + o(T)$.   
We point out that the mistake bound achieved by our algorithm 
is qualitatively tight for 
computationally efficient algorithms; 
this follows from the fact that, even in the offline setting, 
achieving 0-1 error better than $\eta$ 
requires super-polynomial time 
under standard complexity assumptions.

We extend our online learning model to a $k$-arm contextual bandit setting where the rewards---instead of satisfying commonly used realizability assumptions---are consistent, 
in expectation, with some linear ranking function 
with weight vector $\boldsymbol{w}^\ast$.   
Given a list of contexts $\boldsymbol{x}_1,\ldots \boldsymbol{x}_k$, 
if $\boldsymbol{w}^*\cdot \boldsymbol{x}_i > \boldsymbol{w}^* \cdot \boldsymbol{x}_j$, the expected reward of action $i$
must be larger than that of $j$ by at least $\Delta$.  
We use our Massart online learner to design an efficient bandit algorithm 
that obtains expected reward at least 
$(1-1/k)~ \Delta T - o(T)$ bigger than choosing a random action at every round.
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
Authors: Pouria Fatemi; Ehsan Sharifian; Mohammad Hossein Yassaee
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Counterfactual explanations enhance interpretability by identifying alternative inputs that produce different outputs, offering localized insights into model decisions. However, traditional methods often neglect causal relationships, leading to unrealistic examples. While newer approaches integrate causality, they are computationally expensive. To address these challenges, we propose an efficient method called BRACE based on backtracking counterfactuals that incorporates causal reasoning to generate actionable explanations. We first examine the limitations of existing methods and then introduce our novel approach and its features. We also explore the relationship between our method and previous techniques, demonstrating that it generalizes them in specific scenarios. Finally, experiments show that our method provides deeper insights into model outputs.