GenAI4ELab/papercli-papers huggingface dataset:
How Far Is Video Generation from World Model: A Physical Law Perspective
Authors: Bingyi Kang; Yang Yue; Rui Lu; Zhijie Lin; Yang Zhao; Kaixin Wang; Gao Huang; Jiashi Feng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Scaling video generation models is believed to be promising in building world models that adhere to fundamental physical laws. However, whether these models can discover physical laws purely from vision can be questioned. 
A world model learning the true law should give predictions robust to nuances and correctly extrapolate on unseen scenarios.
In this work, we evaluate across three key scenarios: in-distribution, out-of-distribution, and combinatorial generalization.
We developed a 2D simulation testbed for object movement and collisions to generate videos deterministically governed by one or more classical mechanics laws.
We focus on the scaling behavior of training
diffusion-based video generation models to predict object movements based on initial frames.
Our scaling experiments show perfect generalization within the distribution, measurable scaling behavior for combinatorial generalization, but failure in out-of-distribution scenarios.
Further experiments reveal two key insights about the generalization mechanisms of these models: (1) the models fail to abstract general physical rules and instead exhibit "case-based" generalization behavior, i.e., mimicking the closest training example; (2) when generalizing to new cases, models are observed to prioritize different factors when referencing training data: color $>$ size $>$ velocity $>$ shape.
Our study suggests that scaling alone is insufficient for video generation models to uncover fundamental physical laws.
Joint Learning of Energy-based Models and their Partition Function
Authors: Michael Eli Sander; Vincent Roulet; Tianlin Liu; Mathieu Blondel
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Energy-based models (EBMs) offer a flexible framework for parameterizing probability distributions using neural networks.
However, learning EBMs by exact maximum likelihood estimation (MLE) is generally intractable, due to the need to compute the partition function.
In this paper, we propose a novel min-min formulation for approximately learning probabilistic EBMs in combinatorially-large discrete spaces, such as sets or permutations. Our key idea is to jointly learn both an energy model and its log-partition, parameterized as a neural network. Our approach not only provides a novel tractable objective criterion to learn EBMs by stochastic gradient descent (without relying on MCMC), but also a novel means to estimate the log-partition function on unseen data points.
On the theoretical side, we show that our approach recovers the optimal MLE solution when optimizing in the space of continuous functions.
Furthermore, we show that our approach naturally extends to the broader family of Fenchel-Young losses, allowing us to obtain
the first tractable method for optimizing the sparsemax loss in combinatorially-large spaces.
We demonstrate our approach on multilabel classification and label ranking.
GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras
Authors: Ekaterina Filimoshina; Dmitry Shirokov
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose, implement, and compare with competitors a new architecture of equivariant neural networks based on geometric (Clifford) algebras: Generalized Lipschitz Group Equivariant Neural Networks (GLGENN). These networks are equivariant to all pseudo-orthogonal transformations, including rotations and reflections, of a vector space with any non-degenerate or degenerate symmetric bilinear form. We propose a weight-sharing parametrization technique that takes into account the fundamental structures and operations of geometric algebras. Due to this technique, GLGENN architecture is parameter-light and has less tendency to overfitting than baseline equivariant models. GLGENN outperforms or matches competitors on several benchmarking equivariant tasks, including estimation of an equivariant function and a convex hull experiment, while using significantly fewer optimizable parameters.
Improved Online Confidence Bounds for Multinomial Logistic Bandits
Authors: Joongkyu Lee; Min-hwan Oh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regret. Recently, Lee & Oh (2024) established an online confidence bound for MNL models and achieved nearly minimax-optimal regret in MNL bandits. However, their results still depend on the norm-boundedness of the unknown parameter $B$ and the maximum size of possible outcomes $K$. To address this, we first derive an online confidence bound of $\mathcal{O}(\sqrt{d \log t} + B \sqrt{d} )$, which is a significant improvement over the previous bound of $\mathcal{O} (B \sqrt{d} \log t \log K )$ (Lee & Oh, 2024). This is mainly achieved by establishing tighter self-concordant properties of the MNL loss and applying Ville’s inequality to bound the estimation error. Using this new online confidence bound, we propose a constant-time algorithm, **OFU-MNL++**, which achieves a variance-dependent regret bound of $\mathcal{O} \Big( d \log T \sqrt{ \sum_{t=1}^T \sigma_t^2 } \Big) $ for sufficiently large $T$, where $\sigma_t^2$ denotes the variance of the rewards at round $t$, $d$ is the dimension of the contexts, and $T$ is the total number of rounds. Furthermore, we introduce a Maximum Likelihood Estimation (MLE)-based algorithm, **OFU-M$^2$NL**, which achieves an anytime $\operatorname{poly}(B)$-free regret of $\mathcal{O} \Big( d \log (BT) \sqrt{ \sum_{t=1}^T \sigma_t^2 } \Big) $.
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Authors: Mouxiang Chen; Lefei Shen; Zhuo Li; Xiaoyun Joy Wang; Jianling Sun; Chenghao Liu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time series domain, the proposed VisionTS could achieve better zero-shot forecast performance than existing TSF foundation models. With fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting that visual models may offer a "free lunch" for TSF and highlight the potential for future cross-modality research. Our code is available in the https://github.com/Keytoyze/VisionTS.
Distributed Differentially Private Data Analytics via Secure Sketching
Authors: Jakob Burkhardt; Hannah Keller; Claudio Orlandi; Chris Schwiegelshohn
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce the *linear-transformation model*, a distributed model of differentially private data analysis. Clients have access to a 
trusted platform capable of applying a public matrix to their inputs. Such computations can be securely distributed across multiple servers using simple and efficient secure multiparty computation techniques. The linear-transformation model serves as an intermediate model between the highly expressive *central model* and the minimal *local model*. In the central model, clients have access to a trusted platform capable of applying any function to their inputs. However, this expressiveness comes at a cost, as it is often expensive to distribute such computations, leading to the central model typically being implemented by a single trusted server. In contrast, the local model assumes no trusted platform, which forces clients to add significant noise to their data. The linear-transformation model avoids the single point of failure for privacy present in the central model, while also mitigating the high noise required in the local model. We demonstrate that linear transformations are very useful for differential privacy, allowing for the computation of linear sketches of input data. These sketches largely preserve utility for tasks such as private low-rank approximation and private ridge regression, while introducing only minimal error, critically independent of the number of clients.
Learning curves theory for hierarchically compositional data with power-law distributed features
Authors: Francesco Cagnetta; Hyunmo Kang; Matthieu Wyart
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent theories suggest that Neural Scaling Laws arise whenever the task is linearly decomposed into units that are power-law distributed. Alternatively, scaling laws also emerge when data exhibit a hierarchically compositional structure, as is thought to occur in language and images. To unify these views, we consider classification and next-token prediction tasks based on probabilistic context-free grammars—probabilistic models that generate data via a hierarchy of production rules. For classification, we show that having power-law distributed production rules results in a power-law learning curve with an exponent depending on the rules’ distribution and a large multiplicative constant that depends on the hierarchical structure. By contrast, for next-token prediction, the distribution of production rules controls the fine details of the learning curve, but not the exponent describing the large-scale behaviour.
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
Authors: Chenbei Lu; Laixi Shi; Zaiwei Chen; Chenye Wu; Adam Wierman
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Factored Markov Decision Processes (FMDPs) offer a promising framework for overcoming the curse of dimensionality in reinforcement learning (RL) by decomposing high-dimensional MDPs into smaller and independently evolving components. Despite their potential, existing studies on FMDPs face three key limitations: reliance on perfectly factorizable models, suboptimal sample complexity guarantees for model-based algorithms, and the absence of model-free algorithms. To address these challenges, we introduce approximate factorization, which extends FMDPs to handle imperfectly factored models. Moreover, we develop a model-based algorithm and a model-free algorithm (in the form of variance-reduced Q-learning), both achieving the first near-minimax sample complexity guarantees for FMDPs. A key novelty in the design of these two algorithms is the development of a graph-coloring-based optimal synchronous sampling strategy. Numerical simulations based on the wind farm storage control problem corroborate our theoretical findings.
Combinatorial Reinforcement Learning with Preference Feedback
Authors: Joongkyu Lee; Min-hwan Oh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we consider combinatorial reinforcement learning with preference feedback, where a learning agent sequentially offers an action—an assortment of multiple items—to a user, whose preference feedback follows a multinomial logistic (MNL) model. This framework allows us to model real-world scenarios, particularly those involving long-term user engagement, such as in recommender systems and online advertising. However, this framework faces two main challenges: (1) the unknown value of each item, unlike traditional MNL bandits that only address single-step preference feedback, and (2) the difficulty of ensuring optimism while maintaining tractable assortment selection in the combinatorial action space with unknown values. In this paper, we assume a contextual MNL preference model, where the mean utilities are linear, and the value of each item is approximated by a general function. We propose an algorithm, MNL-VQL, that addresses these challenges, making it both computationally and statistically efficient. As a special case, for linear MDPs (with the MNL preference feedback), we establish the first regret lower bound in this framework and show that MNL-VQL achieves near-optimal regret. To the best of our knowledge, this is the first work to provide statistical guarantees in combinatorial RL with preference feedback.
Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads
Authors: Siqi Kou; Jiachun Jin; Zhihong Liu; Chang Liu; Ye Ma; Jian Jia; Quan Chen; Peng Jiang; Zhijie Deng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce Orthus, a unified multimodal model that excels in generating interleaved images and text from mixed-modality inputs by simultaneously handling discrete text tokens and continuous image features under the \textbf{AR} modeling principle. The continuous treatment of visual signals minimizes the information loss while the fully AR formulation renders the characterization of the correlation between modalities straightforward. Orthus leverages these advantages through its modality-specific heads---one regular language modeling (LM) head predicts discrete text tokens and one diffusion head generates continuous image features. We devise an efficient strategy for building Orthus---by substituting the Vector Quantization (VQ) operation in the existing unified AR model with a soft alternative, introducing a diffusion head, and tuning the added modules to reconstruct images, we can create an Orthus-base model effortlessly (e.g., within 72 A100 GPU hours). Orthus-base can further embrace post-training to craft lengthy interleaved image-text, reflecting the potential for handling intricate real-world tasks. For visual understanding and generation, Orthus achieves a GenEval score of 0.58 and an MME-P score of 1265.8 using 7B parameters, outperforming competing baselines including Show-o and Chameleon.
Defending LVLMs Against Vision Attacks Through Partial-Perception Supervision
Authors: Qi Zhou; Dongxia Wang; Tianlin Li; Yun Lin; Yang Liu; Jin Song Dong; Qing Guo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent studies have raised significant concerns regarding the vulnerability of Large Vision Language Models (LVLMs) to maliciously injected or perturbed input images, which can mislead their responses. Existing defense methods show that such vision attacks are sensitive to image modifications especially cropping, using majority voting across responses of modified images as corrected responses. However, these modifications often result in partial images and distort the semantics, which reduces response quality on clean images after voting. Instead of directly using responses from partial images for voting, we investigate using them to supervise (guide) the LVLM's responses to the original images at inference time. We propose a black-box, training-free method called **DPS (Defense through Partial-Perception Supervision)**. In this approach, the model is prompted using the responses generated by a model that perceives only a partial image.
With DPS, the model can adjust its response based on partial image understanding when under attack, while confidently maintaining its original response for clean input. Empirical experiments show our method outperforms the baseline, cutting the average attack success rate by 76.3\% across six datasets on three popular models.
A Variational Perspective on Generative Protein Fitness Optimization
Authors: Lea Bogensperger; Dominik Narnhofer; Ahmed Allam; Konrad Schindler; Michael Krauthammer
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landscape, along with the discrete nature of protein sequences, pose significant challenges when trying to determine the gradient towards configurations with higher fitness. We introduce *Variational Latent Generative Protein Optimization* (VLGPO), a variational perspective on fitness optimization. Our method embeds protein sequences in a continuous latent space to enable efficient sampling from the fitness distribution and combines a (learned) flow matching prior over sequence mutations with a fitness predictor to guide optimization towards sequences with high fitness. VLGPO achieves state-of-the-art results on two different protein benchmarks of varying complexity. Moreover, the variational design with explicit prior and likelihood functions offers a flexible plug-and-play framework that can be easily customized to suit various protein design tasks.
Stay Hungry, Keep Learning: Sustainable Plasticity for Deep Reinforcement Learning
Authors: Huaicheng Zhou; Zifeng Zhuang; Donglin Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The integration of Deep Neural Networks in Reinforcement Learning (RL) systems has led to remarkable progress in solving complex tasks but also introduced challenges like primacy bias and dead neurons. Primacy bias skews learning towards early experiences, while dead neurons diminish the network's capacity to acquire new knowledge. Traditional reset mechanisms aimed at addressing these issues often involve maintaining large replay buffers to train new networks or selectively resetting subsets of neurons. However, these approaches either incur prohibitive computational costs or reset network parameters without ensuring stability through recovery mechanisms, ultimately impairing learning efficiency. In this work, we introduce the novel concept of neuron regeneration, which combines reset mechanisms with knowledge recovery techniques. We also propose a new framework called Sustainable Backup Propagation(SBP) that effectively maintains plasticity in neural networks through this neuron regeneration process. The SBP framework achieves whole network neuron regeneration through two key procedures: cycle reset and inner distillation. Cycle reset involves a scheduled renewal of neurons, while inner distillation functions as a knowledge recovery mechanism at the neuron level. To validate our framework, we integrate SBP with Proximal Policy Optimization (PPO) and propose a novel distillation function for inner distillation. This integration results in Plastic PPO (P3O), a new algorithm that enables efficient cyclic regeneration of all neurons in the actor network. Extensive experiments demonstrate the approach effectively maintains policy plasticity and improves sample efficiency in reinforcement learning.
Learning Latent Graph Structures and their Uncertainty
Authors: Alessandro Manenti; Daniele Zambon; Cesare Alippi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph neural networks use relational information as an inductive bias to enhance prediction performance. Not rarely, task-relevant relations are unknown and graph structure learning approaches have been proposed to learn them from data. Given their latent nature, no graph observations are available to provide a direct training signal to the learnable relations. Therefore, graph topologies are typically learned on the prediction task alongside the other graph neural network parameters.
In this paper, we demonstrate that minimizing point-prediction losses does not guarantee proper learning of the latent relational information and its associated uncertainty. Conversely, we prove that suitable loss functions on the stochastic model outputs simultaneously grant solving two tasks: (i) learning the unknown distribution of the latent graph and (ii) achieving optimal predictions of the target variable. 
Finally, we propose a sampling-based method that solves this joint learning task. Empirical results validate our theoretical claims and demonstrate the effectiveness of the proposed approach.
X-Hacking: The Threat of Misguided AutoML
Authors: Rahul Sharma; Sumantrak Mukherjee; Andrea Sipka; Eyke Hüllermeier; Sebastian Josef Vollmer; Sergey Redyuk; David Antony Selby
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Explainable AI (XAI) and interpretable machine learning methods help to build trust in model predictions and derived insights, yet also present a perverse incentive for analysts to manipulate XAI metrics to support pre-specified conclusions. This paper introduces the concept of X-hacking, a form of p-hacking applied to XAI metrics such as Shap values. We show how easily an automated machine learning pipeline can be adapted to exploit model multiplicity at scale: searching a set of ‘defensible’ models with similar predictive performance to find a desired explanation. We formulate the trade-off between explanation and accuracy as a multi-objective optimisation problem, and illustrate empirically on familiar real-world datasets that, on average, Bayesian optimisation accelerates X-hacking 3-fold for features susceptible to it, versus random sampling. We show the vulnerability of a dataset to X-hacking can be determined by information redundancy among features. Finally, we suggest possible methods for detection and prevention, and discuss ethical implications for the credibility and reproducibility of XAI.
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
Authors: Yuxuan Zhou; Xien Liu; Chenwei Yan; Chen Ning; Xiao Zhang; Boxun Li; Xiangling Fu; Shijin Wang; Guoping Hu; Yu Wang; Ji Wu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored. Inspired by Bloom's Taxonomy, we propose a multi-cognitive-level evaluation framework for assessing LLMs in the medical domain in this study. The framework integrates existing medical datasets and introduces tasks targeting three cognitive levels: preliminary knowledge grasp, comprehensive knowledge application, and scenario-based problem solving. Using this framework, we systematically evaluate state-of-the-art general and medical LLMs from six prominent families: Llama, Qwen, Gemma, Phi, GPT, and DeepSeek. Our findings reveal a significant performance decline as cognitive complexity increases across evaluated models, with model size playing a more critical role in performance at higher cognitive levels. Our study highlights the need to enhance LLMs' medical capabilities at higher cognitive levels and provides insights for developing LLMs suited to real-world medical applications.
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
Authors: Shuo Wang; Bokui Wang; Zhixiang Shen; Boyan Deng; zhao kang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental challenge arises from the substantial differences in graph topologies across domains. Additionally, real-world graphs are often sparse and prone to noisy connections and adversarial attacks. To address these issues, we propose the Multi-Domain Graph Foundation Model (MDGFM), a unified framework that aligns and leverages cross-domain topological information to facilitate robust knowledge transfer. MDGFM bridges different domains by adaptively balancing features and topology while refining original graphs to eliminate noise and align topological structures. To further enhance knowledge transfer, we introduce an efficient prompt-tuning approach. By aligning topologies, MDGFM not only improves multi-domain pre-training but also enables robust knowledge transfer to unseen domains. Theoretical analyses provide guarantees of MDGFM's effectiveness and domain generalization capabilities. Extensive experiments on both homophilic and heterophilic graph datasets validate the robustness and efficacy of our method.