GenAI4ELab/papercli-papers huggingface dataset:
Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics
Authors: Aniketh Iyengar; Jiaqi Han; Pengwei Sun; Mingjian Jiang; Jianwen Xie; Stefano Ermon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pre-training for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 and DRUGS small-molecule datasets across unconditional generation, forward simulation, and interpolation tasks, and further extend our framework and analysis to tetrapeptide and protein monomer systems. Experimental results confirm that our approach excels in generating chemically realistic MD trajectories, as evidenced by remarkable improvements of accuracy in geometric, dynamical, and energetic measurements.
Sample-Efficient Distributionally Robust Multi-Agent Reinforcement Learning via Online Interaction
Authors: Zain Ulabedeen Farhat; Debamita Ghosh; George K. Atia; Yue Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Well-trained multi-agent systems can fail when deployed in real-world environments due to model mismatches between the training and deployment environments, caused by environment uncertainties including noise or adversarial attacks. Distributionally Robust Markov Games (DRMGs) enhance system resilience by optimizing for worst-case performance over a defined set of environmental uncertainties. However, current methods are limited by their dependence on simulators or large offline datasets, which are often unavailable. This paper pioneers the study of online learning in DRMGs, where agents learn directly from environmental interactions without prior data. We introduce the Multiplayer Optimistic Robust Nash Value Iteration (MORNAVI) algorithm and provide the first provable guarantees for this setting. Our theoretical analysis demonstrates that the algorithm achieves low regret and efficiently finds the optimal robust policy for uncertainty sets measured by Total Variation divergence and Kullback-Leibler divergence. These results establish a new, practical path toward developing truly robust multi-agent systems.
YuE: Scaling Open Foundation Models for Long-Form Music Generation
Authors: Ruibin Yuan; Hanfeng Lin; Shuyue Guo; Ge Zhang; Jiahao Pan; Yongyi Zang; Haohe Liu; Yiming Liang; Wenye Ma; Xingjian Du; Xeron Du; Zhen Ye; Tianyu Zheng; Zhengxuan Jiang; Yinghao Ma; Minghao Liu; Zeyue Tian; Ziya Zhou; Liumeng Xue; Xingwei Qu; Yizhi LI; Shangda Wu; Tianhao Shen; Ziyang Ma; Jun Zhan; Chunhui Wang; Yatian Wang; Xiaowei Chi; Xinyue Zhang; Zhenzhu Yang; XiangzhouWang; Shansong Liu; Lingrui Mei; Peng Li; Junjie Wang; Jianwei Yu; Guojian Pang; Xu Li; Zihao Wang; Xiaohuan Zhou; Lijun Yu; Emmanouil Benetos; Yong Chen; Chenghua Lin; Xie Chen; Gus Xia; Zhaoxiang Zhang; Chao Zhang; Wenhu Chen; Xinyu Zhou; Xipeng Qiu; Roger Dannenberg; Jiaheng Liu; Jian Yang; Wenhao Huang; Wei Xue; Xu Tan; Yike Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We tackle the task of long-form music generation, particularly the challenging \textbf{lyrics-to-song} problem, by introducing \textbf{YuE (乐)}, a family of open-source music generation foundation models. Specifically,
YuE scales to trillions of tokens and generates up to five minutes of music while maintaining lyrical alignment, coherent musical structure, and engaging vocal melodies with appropriate accompaniment. It achieves this through \textbf{track-decoupled next-token prediction} to overcome dense mixture signals, and \textbf{structural progressive conditioning} for long-context lyrical alignment. In addition, we redesign the \textbf{in-context learning} technique for music generation, enabling bidirectional content creation, style cloning, and improving musicality. Through extensive evaluation, we demonstrate that YuE matches or even surpasses some of the proprietary systems in musicality and vocal agility (as of 2025-01). We strongly encourage readers to \textbf{listen to our demo}\footnote{\url{https://map-yue.github.io/}}.
Towards Understanding Valuable Preference Data for Large Language Model Alignment
Authors: Zizhuo Zhang; Qizhou Wang; Shanshan Ye; Jianing Zhu; Jiangchao Yao; Bo Han; Masashi Sugiyama
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language model (LLM) alignment is typically achieved through learning from human preference comparisons, making the quality of preference data critical to its success. Existing studies often pre-process raw training datasets to identify valuable preference pairs using external reward models or off-the-shelf LLMs, achieving improved overall performance but rarely examining whether individual, selected data point is genuinely beneficial. We assess data quality through individual influence on validation data using our newly proposed truncated influence function (TIF), which mitigates the over-scoring present in traditional measures and reveals that preference data quality is inherently a property of the model. In other words, a data pair that benefits one model may harm another. This leaves the need to improve the preference data selection approaches to be adapting to specific models. To this end, we introduce two candidate scoring functions (SFs) that are computationally simpler than TIF and positively correlated with it. They are also model dependent and can serve as potential indicators of individual data quality for preference data selection. Furthermore, we observe that these SFs inherently exhibit errors when compared to TIF. To this end, we combine them to offset their diverse error sources, resulting in a simple yet effective data selection rule that enables the models to achieve a more precise selection of valuable preference data. We conduct experiments across diverse alignment benchmarks and various LLM families, with results demonstrating that better alignment performance can be achieved using less data, showing the generality of our findings and new methods. Our code is publicly available at~\url{https://github.com/tmlr-group/TIF_LossDiff-IRM}.
Hubble: a Model Suite to Advance the Study of LLM Memorization
Authors: Johnny Wei; Ameya Godbole; Mohammad Aflah Khan; Ryan Yixiang Wang; Xiaoyuan Zhu; James Flemings; Nitya Kashyap; Krishna P. Gummadi; Willie Neiswanger; Robin Jia
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We present Hubble, a suite of fully open-source large language models (LLMs) for the scientific study of LLM memorization. Hubble models come in standard and perturbed variants: standard models are pretrained on a large English corpus, and perturbed models are trained in the same way but with controlled insertion of text (e.g., book passages, biographies, and test sets) designed to emulate key memorization risks. Our core release includes 8 models---standard and perturbed models with 1B or 8B parameters, pretrained on 100B or 500B tokens---establishing that memorization risks are determined by the frequency of sensitive data relative to size of the training corpus (i.e., a password appearing once in a smaller corpus is memorized better than the same password in a larger corpus). Our release also includes 6 perturbed models with text inserted at different pretraining phases, showing that sensitive data without continued exposure can be forgotten. These findings suggest two best practices for addressing memorization risks: to dilute sensitive data by increasing the size of the training corpus, and to order sensitive data to appear earlier in training. Beyond these general empirical findings, Hubble enables a broad range of memorization research; for example, analyzing the biographies reveals how readily different types of private information are memorized. We also demonstrate that the randomized insertions in Hubble make it an ideal testbed for membership inference and machine unlearning, and invite the community to further explore, benchmark, and build upon our work.
EdgeCape: Edge Weight Prediction For Category-Agnostic Pose Estimation
Authors: Or Hirschorn; Shai Avidan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Category-Agnostic Pose Estimation (CAPE) localizes keypoints across diverse object categories with a single model, using one or few annotated support images. Recent works have shown that using a pose-graph (i.e., treating keypoints as nodes in a graph rather than isolated points) helps handle occlusions and break symmetry. However, these methods assume a given pose-graph with equal-weight edges, leading to suboptimal results. We introduce EdgeCape, a novel framework that overcomes these limitations by predicting the graph's edge weights in order to optimize localization. To further leverage structural (i.e., graph) priors, we propose integrating Markov Attention Bias, which modulates the self-attention interaction between nodes based on the number of hops between them. We show that this improves the model’s ability to capture global spatial dependencies. Evaluated on the MP-100 benchmark, which includes 100 categories and over 20K images, EdgeCape achieves state-of-the-art results in the 1-shot and 5-shot settings, significantly improving keypoint localization accuracy.
Text2Interact: High-Fidelity and Diverse Text-to-Two-Person Interaction Generation
Authors: Qingxuan Wu; Zhiyang Dou; chuan guo; Yiming Huang; Qiao Feng; Bing Zhou; Jian Wang; Lingjie Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling human–human interactions from text remains challenging because it requires not only realistic individual dynamics but also precise, text-consistent spatiotemporal coupling between agents. Currently, progress is hindered by 1) limited two-person training data, inadequate to capture the diverse intricacies of two-person interactions; and 2) insufficiently fine-grained text-to-interaction modeling, where language conditioning collapses rich, structured prompts into a single sentence embedding. To address these limitations, we propose our Text2Interact framework, designed to generate realistic, text-aligned human–human interactions through a scalable high-fidelity interaction data synthesizer and an effective spatiotemporal coordination pipeline. First, we present InterCompose, a scalable synthesis-by-composition pipeline that aligns LLM-generated interaction descriptions with strong single-person motion priors. Given a prompt and a motion for an agent, InterCompose retrieves candidate single-person motions, trains a conditional reaction generator for another agent, and uses a neural motion evaluator to filter weak or misaligned samples—expanding interaction coverage without extra capture. Second, we propose InterActor, a text-to-interaction model with word-level conditioning that preserves token-level cues (initiation, response, contact ordering) and an adaptive interaction loss that emphasizes contextually relevant inter-person joint pairs, improving coupling and physical plausibility for fine-grained interaction modeling. Extensive experiments show consistent gains in motion diversity, fidelity, and generalization, including out-of-distribution scenarios and user studies. Code will be released at github.com/Qingxuan-Wu/Text2Interact.
Attribution-Guided Decoding
Authors: Piotr Komorowski; Elena Golimblevskaia; Reduan Achtibat; Thomas Wiegand; Sebastian Lapuschkin; Wojciech Samek
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The capacity of Large Language Models (LLMs) to follow complex instructions and generate factually accurate text is critical for their real-world application. However, standard decoding methods often fail to robustly satisfy these requirements, while existing control techniques frequently degrade general output quality.
In this work, we introduce Attribution-Guided Decoding (AGD), an interpretability-based decoding strategy. 
Instead of directly manipulating model activations, AGD considers a set of high-probability output token candidates and selects the one that exhibits the highest attribution
to a user-defined Region of Interest (ROI). This ROI can be flexibly defined over different parts of the model's input or internal components, allowing AGD to steer generation towards various desirable behaviors.
We demonstrate AGD's efficacy across three challenging domains.
For instruction following, we show that AGD significantly boosts adherence (e.g., improving the overall success rate on Llama 3.1 from 66.0\% to 79.1\%).
For knowledge-intensive tasks, we show that guiding generation towards usage of internal knowledge components or contextual sources can reduce hallucinations and improve factual accuracy in both closed-book and open-book settings.
Furthermore, we propose an adaptive, entropy-based variant of AGD that mitigates quality degradation and reduces computational overhead by applying guidance only when the model is uncertain.
Our work presents a versatile, more interpretable, and effective method for enhancing the reliability of modern LLMs.
VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Authors: Lingjie Jiang; Shaohan Huang; Xun Wu; Yixia Li; Guanhua Chen; Dongdong Zhang; Furu Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal inputs remains limited. In this work, we introduce VisCodex, a unified framework that seamlessly merges vision and coding language models to empower MLLMs with strong multimodal code generation abilities. Leveraging a task vector-based model merging technique, we integrate a state-of-the-art coding LLM into a strong vision-language backbone, while preserving both visual comprehension and advanced coding skills.

To support training and evaluation, we introduce the Multimodal Coding Dataset (MCD), a large-scale and diverse collection of 598k samples, including high-quality HTML code, chart image-code pairs, image-augmented StackOverflow QA, and algorithmic problems. Furthermore, we propose InfiBench-V, a novel and challenging benchmark specifically designed to assess models on visually-rich, real-world programming questions that demand a nuanced understanding of both textual and visual contexts.

Extensive experiments show that VisCodex achieves state-of-the-art performance among open-source MLLMs and approaches proprietary models like GPT-4o, highlighting the effectiveness of our model merging strategy and new datasets.
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Authors: Runqi Qiao; Qiuna Tan; Peiqing Yang; Yanzi Wang; Xiaowan Wang; Enhui Wan; Guanting Dong; Shiqiang Lang; Sitong Zhou; Yida Xu; Yuchen Zeng; Jie Wang; Chong Sun; Chen Li; Honggang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities across various tasks but still struggle with complex mathematical reasoning. Prior work has mainly focused on dataset construction and method optimization, while often overlooking two critical aspects: comprehensive knowledge-driven design and model-centric data space modeling. We introduce WE-MATH 2.0, a unified system that integrates a structured mathematical knowledge hierarchy, model-centric data space modeling, and a reinforcement learning (RL)-based training paradigm to enhance the mathematical reasoning abilities of MLLMs. Our contributions are fourfold: (1) MathBook Knowledge System: a five-level hierarchy covering 491 knowledge points and 1,819 fundamental principles; (2) MathBook-Standard and MathBook-Pro: datasets that ensure broad conceptual coverage and robust training through dual expansion, a three-dimensional difficulty space, and seven progressive variants per problem; (3) MathBook-RL: a two-stage RL framework including Cold-Start Fine-Tuning to align models with knowledge-oriented chain-of-thought reasoning, and Progressive Alignment RL leveraging average-reward learning with dynamic data scheduling for progressive difficulty alignment; (4) MathBookEval: a benchmark covering all 491 knowledge points with diverse reasoning step distributions. Experimental results show that MathBook-RL achieves competitive performance on four widely used benchmarks and demonstrates strong results on MathBookEval, suggesting promising generalization in mathematical reasoning.
Federated Learning of Quantile Inference under Local Differential Privacy
Authors: Leheng Cai; Qirui Hu; Shuyuan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (SGD), whose local gradients are perturbed via a randomized mechanism with global parameters, making the procedure tolerant of communication and storage constraints without compromising statistical efficiency. Although the quantile loss and its corresponding gradient do not satisfy standard smoothness conditions typically assumed in existing literature, we establish asymptotic normality for our estimator as well as a functional central limit theorem. The proposed method accommodates data heterogeneity and allows each server to operate with an individual privacy budget. Furthermore, we construct confidence intervals for the target value through a self‐normalization approach, thereby circumventing the need to estimate additional nuisance parameters. Extensive numerical experiments and real data application validate the theoretical guarantees of the proposed methodology.
Sobolev Gradient Ascent for Optimal Transport: Barycenter Optimization and Convergence Analysis
Authors: Kaheon Kim; Bohan Zhou; Changbo Zhu; Xiaohui Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper introduces a new constraint-free concave dual formulation for the Wasserstein barycenter. Tailoring the vanilla dual gradient ascent algorithm to the Sobolev geometry, we derive a scalable Sobolev gradient ascent (SGA) algorithm to compute the barycenter for input distributions supported on a regular grid. Despite the algorithmic simplicity, we provide a global convergence analysis that achieves the same rate as the classical subgradient descent methods for minimizing nonsmooth convex functions in the Euclidean space. A central feature of our SGA algorithm is that the computationally expensive $c$-concavity projection operator enforced on the Kantorovich dual potentials is unnecessary to guarantee convergence, leading to significant algorithmic and theoretical simplifications over all existing primal and dual methods for computing the exact barycenter. Our numerical experiments demonstrate the superior empirical performance of SGA over the existing optimal transport barycenter solvers.
FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning
Authors: LIANG HU; Jianpeng Jiao; Jiashuo Liu; Dongyuan Mutu; Yanle Ren; Zhoufutu Wen; Kaiyuan Zhang; Xuanliang Zhang; Xiang Gao; Tianci He; FEI HU; Yali Liao; Zaiyuan Wang; Jingkai Liu; Sun Daibin; Ziqing Zeng; Zhiyuan Zeng; Chenghao Yang; Qianyu Yang; Mingren Yin; Ge Zhang; Xinyi zhang; Xiying ZHAO; Zhu Zhenwei; Hongseok Namkoong; Wenhao Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Search has emerged as core infrastructure for LLM-based agents and is widely viewed as critical on the path toward more general intelligence. Finance is a particularly demanding proving ground: analysts routinely conduct complex, multi-step searches over time-sensitive, domain-specific data, making it ideal for assessing both search proficiency and knowledge-grounded reasoning. Yet no existing open financial datasets evaluate data searching capability of end-to-end agents, largely because constructing realistic, complicated tasks requires deep financial expertise and time-sensitive data is hard to evaluate. We present FinSearchComp, the first fully open-source agent benchmark for realistic, open-domain financial search and reasoning. FinSearchComp comprises three tasks, Time-Sensitive Data Fetching, Simple Historical Lookup, and Complex Historical Investigation, closely reproducing real-world financial analyst workflows. To ensure difficulty and reliability, we engage $70$ professional financial experts for annotation and implement a rigorous multi-stage quality-assurance pipeline. The benchmark includes $635$ questions spanning global and Greater China markets, and we evaluate $21$ models (products) on it. Grok 4 (web) tops the global subset, approaching expert-level accuracy. DouBao (web) leads on the Greater China subset. Experimental analyses show that equipping agents with web search and financial plugins substantially improves results on FinSearchComp, and the country origin of models and tools impact performance significantly. By aligning with realistic analyst tasks and providing end-to-end evaluation, FinSearchComp offers a professional, high-difficulty testbed for complex financial search and reasoning.
Controllable Sequence Editing for Biological and Clinical Trajectories
Authors: Michelle M Li; Kevin Li; Yasha Ektefaie; Ying Jin; Yepeng Huang; Shvat Messica; Tianxi Cai; Marinka Zitnik
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conditional generation models for longitudinal sequences can produce new or modified trajectories given a conditioning input. However, they often lack control over when the condition should take effect (timing) and which variables it should influence (scope). Most methods either operate only on univariate sequences or assume that the condition alters all variables and time steps. In scientific and clinical settings, interventions instead begin at a specific moment, such as the time of drug administration or surgery, and influence only a subset of measurements while the rest of the trajectory remains unchanged. CLEF learns temporal concepts that encode how and when a condition alters future sequence evolution. These concepts allow CLEF to apply targeted edits to the affected time steps and variables while preserving the rest of the sequence. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health, and sales, comparing against 9 state-of-the-art baselines. CLEF improves immediate sequence editing accuracy by 16.28% (MAE) on average against their non-CLEF counterparts. Unlike prior models, CLEF enables one-step conditional generation at arbitrary future times, outperforming their non-CLEF counterparts in delayed sequence editing by 26.73% (MAE) on average. We test CLEF under counterfactual inference assumptions and show up to 62.84% (MAE) improvement on zero-shot conditional generation of counterfactual trajectories. In a case study of patients with type 1 diabetes mellitus, CLEF identifies clinical interventions that generate realistic counterfactual trajectories shifted toward healthier outcomes.