GenAI4ELab/papercli-papers huggingface dataset:
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Authors: Jh Yuan; Fangyuan Xie; Feiping Nie; Xuelong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and compared with other existing relaxations, it can flexibly incorporate class information. We prove that this relaxation forms a manifold, which we call the Relaxed Indicator Matrix Manifold (RIM manifold). Based on Riemannian geometry, we develop a Riemannian toolbox for optimization on the RIM manifold. Specifically, we provide several methods of Retraction, including a fast Retraction method to obtain geodesics. We point out that the RIM manifold is a generalization of the double stochastic manifold, and it is much faster than existing methods on the double stochastic manifold, which has a complexity of \( \mathcal{O}(n^3) \), while RIM manifold optimization is \( \mathcal{O}(n) \) and often yields better results. We conducted extensive experiments, including image denoising, with millions of variables to support our conclusion, and applied the RIM manifold to Ratio Cut, we provide a rigorous convergence proof and achieve clustering results that outperform the state-of-the-art methods. Our Code is presented in Appendix H.
STORK: Faster Diffusion and Flow Matching Sampling by Resolving both Stiffness and Structure-Dependence
Authors: Zheng Tan; Weizhen Wang; Andrea L. Bertozzi; Ernest K. Ryu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models (DMs) and flow-matching models have demonstrated remarkable performance in image and video generation. However, such models require a significant number of function evaluations (NFEs) during sampling, leading to costly inference. Consequently, quality-preserving fast sampling methods that require fewer NFEs have been an active area of research. However, prior training-free sampling methods fail to simultaneously address two key challenges: the stiffness of the ODE (i.e., the non-straightness of the velocity field) and dependence on the semi-linear structure of the DM ODE (which limits their direct applicability to flow-matching models). In this work, we introduce the Stabilized Taylor Orthogonal Runge–Kutta (STORK) method, addressing both design concerns. We demonstrate that STORK consistently improves the quality of diffusion and flow-matching sampling for image and video generation.
J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning
Authors: Chenxi Whitehouse; Tianlu Wang; Ping Yu; Xian Li; Jason E Weston; Ilia Kulikov; Swarnadeep Saha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The progress of AI is bottlenecked by the quality of evaluation, making powerful LLM-as-a-Judge models a core solution. The efficacy of these judges depends on their chain-of-thought reasoning, creating a critical need for methods that can effectively optimize this reasoning process. In this work, we introduce J1, a reinforcement learning framework for teaching LLM judges to think before making decisions. Our core contribution lies in converting all judgment tasks for nonverifiable and verifiable prompts into a unified format with verifiable rewards, enabling direct optimization of evaluation quality while mitigating positional bias. We then use RL to train thinking-judges at scales of 8B, 32B, and 70B and show that they obtain state-of-the-art performance across multiple benchmarks. In particular, J1-Qwen-32B, our multitasked pointwise and pairwise judge also outperforms o1-mini, o3, and a much larger 671B DeepSeek-R1 on some benchmarks, while only training on synthetic data. Through comprehensive ablations of pairwise, pointwise, and multitask J1 variants, we demonstrate the effectiveness of our approach across seed prompts, reward strategies, and training recipes. Qualitative analysis reveals that J1 develops systematic evaluation strategies, including dynamic criteria generation, reference answer creation, iterative self-correction of initial assessments, and feedback generation for low-quality responses.
GenFusion: Feed-forward Human Performance Capture via Progressive Canonical Space Updates
Authors: YoungJoong Kwon; Yao He; Hee Jung Choi; Chen Geng; Zhengmao Liu; Jiajun Wu; Ehsan Adeli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present a feed-forward human performance capture method that renders novel views of a performer from a monocular RGB stream. A key challenge in this setting is the lack of sufficient observations, especially for unseen regions. Assuming the subject moves continuously over time, we take advantage of the fact that more body parts become observable by maintaining a canonical space that is progressively updated with each incoming frame. This canonical space accumulates appearance information over time and serves as a context bank when direct observations are missing in the current live frame. To effectively utilize this context while respecting the deformation of the live state, we formulate the rendering process as probabilistic regression. This resolves conflicts between past and current observations, producing sharper reconstructions than deterministic regression approaches. Furthermore, it enables plausible synthesis even in regions with no prior observations. Experiments on both in-domain (4D-Dress) and out-of-distribution (MVHumanNet) datasets demonstrate the effectiveness of our approach.
Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Authors: Alper Kamil Bozkurt; Calin Belta; Ming Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Ensuring that reinforcement learning (RL) controllers satisfy safety and reliability constraints in real-world settings remains challenging: state-avoidance and constrained Markov decision processes often fail to capture trajectory-level requirements or induce overly conservative behavior. Formal specification languages such as linear temporal logic (LTL) offer correct-by-construction objectives, yet their rewards are typically sparse, and heuristic shaping can undermine correctness. We introduce, to our knowledge, the first end-to-end framework that integrates LTL with differentiable simulators, enabling efficient gradient-based learning directly from formal specifications. Our method relaxes discrete automaton transitions via soft labeling of states, yielding differentiable rewards and state representations that mitigate the sparsity issue intrinsic to LTL while preserving objective soundness. We provide theoretical guarantees connecting Büchi acceptance to both discrete and differentiable LTL returns and derive a tunable bound on their discrepancy in deterministic and stochastic settings. Empirically, across complex, nonlinear, contact-rich continuous-control tasks, our approach substantially accelerates training and achieves up to twice the returns of discrete baselines. We further demonstrate compatibility with reward machines, thereby covering co-safe LTL and LTLf without modification. By rendering automaton-based rewards differentiable, our work bridges formal methods and deep RL, enabling safe, specification-driven learning in continuous domains.
mR3: Multilingual Rubric-Agnostic Reward Reasoning Models
Authors: David Anugraha; Shou-Yi Hung; Zilu Tang; En-Shiun Annie Lee; Derry Tanti Wijaya; Genta Indra Winata
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Evaluation using Large Language Model (LLM) judges has been widely adopted in English and shown to be effective for automatic evaluation. However, their performance does not generalize well to non-English settings, and it remains unclear what constitutes effective multilingual training for such judges. In this paper, we introduce mR3, a massively multilingual, rubric-agnostic reward reasoning model trained on 72 languages, achieving the broadest language coverage in reward modeling to date. We present a comprehensive study of data and curriculum selection for training to identify effective strategies and data sources for building high-quality reward models, including support for reasoning in the target language. Our approach attains state-of-the-art performance on multilingual reward model benchmarks, surpassing much larger models (i.e., GPT-OSS-120B) while being up to 9x smaller, and its effectiveness is further confirmed through extensive ablation studies. Finally, we demonstrate the effectiveness of mR3 in off-policy preference optimization and validate the quality of its reasoning traces and rubric-based evaluations through human studies with 20 annotators across 12 languages, where mR3 models' reasoning is preferred, including for extremely low-resource languages that are entirely unseen during training. Our models, data, and code are available as open source at https://github.com/rubricreward/mr3.
From Cheap Geometry to Expensive Physics: A Physics-agnostic Pretraining Framework for Neural Operators
Authors: Zhizhou Zhang; Youjia Wu; Kaixuan Zhang; Yanjia Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Industrial design evaluation often relies on high-fidelity simulations of governing partial differential equations (PDEs). While accurate, these simulations are computationally expensive, making dense exploration of design spaces impractical. Operator learning has emerged as a promising surrogate for high-fidelity physical simulations, enabling fast prediction of partial differential equation (PDE) solutions. However, accuracy of neural operators is largely affected by the amount of training data, which must be generated by expensive numerical solvers. In practical industrial scenarios, there exists large collections of candidate geometries remain unsolved due to the high computation cost. These geometry-only samples contain no physical field labels and are therefore ignored in standard operator learning pipelines. In this work, we propose a general physics-agnostic pretraining framework to exploit this abundant geometric resource to improve the performance of neural operators. Specifically, we pretrain an autoencoder on a self-supervised proxy task to reconstruct geometry (e.g., via occupancy), learning an expressive latent representation without PDE supervision. Neural operators then leverage the pretrained latent embedding to learn more effectively from limited physics labels. An error decomposition analysis is provided to help understand the effectiveness of the physics-agnostic pretraining framework. Across four PDE datasets and three state-of-the-art transformer-based neural operators, our pretraining strategy consistently improves prediction accuracy. These results demonstrate that representations from physics-agnostic pretraining provide a powerful foundation for data-efficient operator learning. The code is publicly available at: https://github.com/zzzwoniu/Physics-agnostic-Operator-Pretraining.git
The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think
Authors: Seongyun Lee; Seungone Kim; Minju Seo; Yongrae Jo; Dongyoung Go; Hyeonbin Hwang; Jinho Park; Xiang Yue; Sean Welleck; Graham Neubig; Moontae Lee; Minjoon Seo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long chain-of-thought (CoT) is an essential ingredient in effective usage of modern large language models, but our understanding of the reasoning strategies underlying these capabilities remains limited. While some prior works have attempted to categorize CoTs using predefined strategy types, such approaches are constrained by human intuition and fail to capture the full diversity of model behaviors. In this work, we introduce the CoT Encyclopedia, a bottom-up framework for analyzing and steering model reasoning. Our method automatically extracts diverse reasoning criteria from model-generated CoTs, embeds them into a semantic space, clusters them into representative categories, and derives contrastive rubrics to interpret reasoning behavior. Human evaluations show that this framework produces more interpretable and comprehensive analyses than existing methods. Moreover, we show that this understanding translates into measurable improvements on both problem-solving and safety benchmarks. We can predict which strategy a model is likely to use and guide it toward more effective alternatives. Finally, we show that training data format (e.g., free-form vs. multiple-choice) impacts reasoning far more than data domain, highlighting the importance of format-aware model design. In short, the CoT Encyclopedia turns reasoning from a black box into a controllable asset, enabling LLMs that think more clearly, perform more reliably, and act more safely.
Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances
Authors: Khai Nguyen; Hai Nguyen; Nhat Ho
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein (SW) distances. Specifically, we leverage both standard SW distances, which provide lower bounds, and lifted SW distances, which provide upper bounds, as predictors of the true Wasserstein distance. To ensure parsimony, we introduce two linear models: an unconstrained model with a closed-form least-squares solution, and a constrained model that uses only half as many parameters. We show that accurate models can be learned from a small number of distribution pairs. Once estimated, the model can predict the Wasserstein distance for any pair of distributions via a linear combination of SW distances, making it highly efficient.  Empirically, we validate our approach on diverse tasks, including Gaussian mixtures, point-cloud classification, and Wasserstein-space visualizations for 3D point clouds. Across various datasets such as MNIST point clouds, ShapeNetV2, MERFISH Cell Niches, and scRNA-seq, our method consistently provides a better approximation of Wasserstein than the state-of-the-art method, Wasserstein Wormhole, and classical methods, particularly in low-data regimes. To illustrate its robustness, we also experiment the method with intra- and inter-class settings. Finally, we demonstrate that \emph{RG} can accelerate Wasserstein Wormhole training, yielding \emph{RG-Wormhole}.
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
Authors: Ali Behrouz; Meisam Razaviyayn; Peilin Zhong; Vahab Mirrokni
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Designing efficient and effective architectural backbones has been in the core of research efforts to enhance the capability of foundation models. Inspired by the human cognitive phenomenon of attentional bias—the natural tendency to prioritize certain events or stimuli—we reconceptualize neural architectures, including Transformers, Titans, and modern linear recurrent neural networks as associative memory modules with attentional bias. We define and formalize the concept of attentional bias as the internal memory objective deep learning architectures. We show that existing deep learning architectures leverage the same attentional bias based on $L_2$ loss function. Going beyond $L_2$ loss function, we present a set of alternative attentional bias configurations along with their effective approximations. We then reinterpret forgetting mechanisms in modern deep learning architectures as a form of retention regularization. Building upon these insights, we present Miras, a general framework to design deep learning architectures based on the choice of attentional bias objective, retention gate, associative memory architecture, and memory learning algorithm. Our experiments show different designs yield models with varying strengths. Furthermore, our special instances of Miras achieve exceptional performance in language modeling, commonsense reasoning, recall intensive, and time series tasks, outperforming Transformers and other modern linear recurrent models.
Spatial Structure and Selective Text Jointly Facilitate Image Clustering
Authors: Zizheng Jiu; Feijiang Li; Jieting Wang; Yuhua Qian; Lu Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image clustering is a fundamental task in visual machine learning. A key research direction in this field is the incorporation of prior knowledge. Recently, such prior knowledge has evolved from internal compactness constraints to external textual guidance. In particular, the introduction of textual modalities through CLIP has demonstrated impressive performance. However, CLIP is designed primarily for image–text alignment and may not be sufficient to capture clustering structures. Moreover, existing approaches often assume that textual features are universally beneficial, overlooking their varying suitability for different datasets. To address these issues, we propose using spatial structure and selective text jointly to facilitate image clustering (SATC). Specifically, we design a graph attention network (GAT)-based encoder to capture relational dependencies among image patches, thereby extracting spatial features to facilitate clustering. In addition, we introduce a textual feature selector that uses the potential clustering compactness of textual features as the selection criterion and adaptively integrates them into the clustering process. Theoretical guidance is provided for this selector. Finally, the cluster assignment is produced through tri-modal mutual distillation. Extensive experiments on 18 benchmark datasets demonstrate the effectiveness of SATC. The experimental results further verify the rationality of the textual feature selector. **Project Page:** 👉 https://zizhjiu.github.io/SATC/
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
Authors: Michael Amir; Matteo Bettini; Amanda Prorok
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of teams in robotics, nature, and society often depends on the division of labor among diverse specialists; however, a principled explanation for when such diversity surpasses a homogeneous team is still missing. Focusing on multi-agent task allocation problems, we study this question from the perspective of reward design: what kinds of objectives are best suited for heterogeneous teams? We first consider an instantaneous, non-spatial setting where the global reward is built by two generalized aggregation operators: an inner operator that maps the N agents’ effort allocations on individual tasks to a task score, and an outer operator that merges the M task scores into the global team reward. We prove that the curvature of these operators determines whether heterogeneity can increase reward, and that for broad reward families this collapses to a simple convexity test. Next, we ask what incentivizes heterogeneity to emerge when embodied, time-extended agents must learn an effort allocation policy. To study heterogeneity in such settings, we use multi-agent reinforcement learning (MARL) as our computational paradigm, and introduce Heterogeneity Gain Parameter Search (HetGPS), a gradient-based algorithm that optimizes the parameter space of underspecified MARL environments to find scenarios where heterogeneity is advantageous.  Across different environments, we show that HetGPS rediscovers the reward regimes predicted by our theory to maximize the advantage of heterogeneity, both validating HetGPS and connecting our theoretical insights to reward design in MARL. Together, these results help us understand when behavioral diversity delivers a measurable benefit.
Ice Cream Doesn’t Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
Authors: Jin Du; Li Chen; Xun Xian; An Luo; Fangqiao Tian; Ganghua Wang; Charles Doss; Xiaotong Shen; Jie Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reliable causal inference is essential for making decisions in high-stakes areas like medicine, economics, and public policy. However, it remains unclear whether large language models (LLMs) can handle rigorous and trustworthy \textit{statistical causal inference}. Current benchmarks usually involve simplified tasks. For example, these tasks might only ask LLMs to identify semantic causal relationships or draw conclusions directly from raw data. As a result, models may overlook important statistical pitfalls, such as Simpson’s paradox or selection bias. This oversight limits the applicability of LLMs in the real world.
To address these limitations, we propose \textbf{CausalPitfalls}, a comprehensive benchmark designed to rigorously evaluate the capability of LLMs in overcoming common causal inference pitfalls. Our benchmark features structured challenges across multiple difficulty levels, each paired with grading rubrics. This approach allows us to quantitatively measure both causal reasoning capabilities and the reliability of LLMs' responses. We evaluate models using two protocols: (1) direct prompting, which assesses intrinsic causal reasoning, and (2) code-assisted prompting, where models generate executable code for explicit statistical analysis. Additionally, we validate the effectiveness of this judge by comparing its scoring with assessments from human experts.
Our results reveal significant limitations in current LLMs when performing statistical causal inference. The CausalPitfalls benchmark provides essential guidance and quantitative metrics to advance the development of trustworthy causal reasoning systems. Our code is publicly available at   \href{https://github.com/dudududuu/CausalPitfalls}{CausalPitfalls}}.
Grounding Computer Use Agents on Human Demonstrations
Authors: Aarash Feizi; Shravan Nayak; Xiangru Jian; Kevin Qinghong Lin; Kaixin Li; Rabiul Awal; Xing Han Lù; Johan Obando-Ceron; Juan A. Rodriguez; Nicolas Chapados; David Vazquez; Adriana Romero-Soriano; Reihaneh Rabbany; Perouz Taslakian; Christopher Pal; Spandana Gella; Sai Rajeswar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents.