GenAI4ELab/papercli-papers huggingface dataset:
Measuring Physical-World Privacy Awareness of Large Language Models: An Evaluation Benchmark
Authors: Xinjie Shen; Mufei Li; Pan Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The deployment of Large Language Models (LLMs) in embodied agents creates an urgent need to measure their privacy awareness in the physical world. Existing evaluation methods, however, are confined to natural language based scenarios. To bridge this gap, we introduce EAPrivacy, a comprehensive evaluation benchmark designed to quantify the physical-world privacy awareness of LLM-powered agents. EAPrivacy utilizes procedurally generated scenarios across four tiers to test an agent's ability to handle sensitive objects, adapt to changing environments, balance task execution with privacy constraints, and resolve conflicts with social norms. Our measurements reveal a critical deficit in current models. The top-performing model, Gemini 2.5 Pro, achieved only 59\% accuracy in scenarios involving changing physical environments. Furthermore, when a task was accompanied by a privacy request, models prioritized completion over the constraint in up to 86\% of cases. In high-stakes situations pitting privacy against critical social norms, leading models like GPT-4o and Claude-3.5-haiku disregarded the social norm over 15\% of the time. These findings, demonstrated by our benchmark, underscore a fundamental misalignment in LLMs regarding physically grounded privacy and establish the need for more robust, physically-aware alignment. Datasets are available at https://github.com/Graph-COM/EAPrivacy
High-dimensional Mean-Field Games by Particle-based Flow Matching
Authors: Jiajia Yu; Junghwan Lee; Yao Xie; Xiuyuan Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mean-field games (MFGs) study the Nash equilibrium of systems with a continuum of interacting agents, which can be formulated as the fixed-point of optimal control problems. 
They provide a unified framework for a variety of problems, including both potential and non-potential games, with applications in areas such as generative modeling.
Despite their broad applicability, solving high-dimensional MFGs remains a significant challenge due to fundamental computational and analytical obstacles. 
In this work, we propose a particle-based deep Flow Matching (FM) method to tackle high-dimensional MFG computation.
In each iteration of our proximal fixed-point scheme, particles are updated using first-order information, and a flow neural network is trained to match the velocity of the sample trajectories. 
Theoretically, in the optimal control setting, we prove that our scheme converges to a stationary point sublinearly, and upgrade to linear (exponential) convergence under additional convexity assumptions.
Our proof uses FM to induce an Eulerian coordinate (density-based) from a Lagrangian one (particle-based), and this also leads to certain equivalence results between the two formulations for MFGs when the Eulerian solution is sufficiently regular.
Our method demonstrates promising experimental performance on MFGs in high dimensions.
Reducing Belief Deviation in Reinforcement Learning for Active Reasoning of LLM Agents
Authors: Deyu Zou; Yongqiang Chen; Jianxiang Wang; Garry YANG; Mufei Li; Qing Da; James Cheng; Pan Li; Yu Gong
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Active reasoning requires large language model (LLM) agents to interact with external sources and strategically gather information to solve problems in multiple turns. Central to this process is belief tracking: maintaining an accurate representation of the underlying state and uncertainty in understanding and solving the problem. However, due to limited reasoning capabilities, LLM-based agents often suffer belief deviation: their internal beliefs drift from the true problem state, leading to loss of state awareness and uninformative or repetitive actions. Once this happens, errors compound in the trajectories used for reinforcement learning (RL), leading to misattributed credits and limited exploration. To address this issue, we propose to track belief deviation and develop $\mathbf{T^3}$, a simple yet principled method that detects excessive deviation and truncates training trajectories to suppress uninformative tail effects. Hence, $\mathbf{T^3}$ preserves credits for informative prefixes and systematically improves policy optimization. Across 5 challenging tasks, $\mathbf{T^3}$ consistently
enhances training stability and yields performance gains of up to 30 points while cutting token cost by up to 34%. These results highlight belief control as a key principle for building robust LLM agents capable of active reasoning.
Bilateral Information-aware Test-time Adaptation for Vision-Language Models
Authors: Jingwei Sun; Jianing Zhu; Jiangchao Yao; Gang Niu; Masashi Sugiyama; Bo Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Test-time adaptation (TTA) fine-tunes models using new data encountered during inference, which enables the vision-language models to handle test data with covariant shifts. Unlike training-time adaptation, TTA does not require a test-distributed validation set or consider the worst-case distribution within a given tolerance. However, previous methods primarily focused on adaption-objective design, while the data tend to be fully utilized or simply filtered through a fixed low-entropy selection criteria. In this paper, we analyze the weakness of previous selection criterion and find that only selecting fixed proportion of low-entropy samples fails to ensure optimal performance across various datasets and can lead the model to becoming over-confident in wrongly classified samples, showing unexpected overfitting to atypical features and compromising effective adaptation. To improve upon them, we propose \textit{Bilateral Information-aware Test-Time Adaptation} (BITTA), which simultaneously leverages two distinct parts of the test inputs during adaptation. Specifically, a dynamic proportion of low-entropy samples are used to learn the core representation under covariant shifts, while high-entropy samples are adopted to unlearn atypical features. This dual approach prevents the model from undesired memorization and ensures  extensive optimal performance. Comprehensive experiments validate the effectiveness in various datasets and model architectures. The code is publicly available at: https://github.com/tmlr-group/BITTA.
Elastic Optimal Transport: Theory, Application, and Empirical Evaluation
Authors: Pei Yang; Yuhang Zhuang; Qi Tan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The classical optimal transport such as Kantorovich's optimal transport and partial optimal transport could be too restrictive in applications due to the full-mass or fixed-mass preservation constraints. To remedy this limitation, we propose elastic optimal transport (ELOT) which is distinctive from the classical optimal transport in its ability of adaptive-mass preserving. It aims to answer the problem of how to transport the probability mass adaptively between probability distributions, which is a fundamental topic in various areas of artificial intelligence. The strength of elastic optimal transport is its capability to transport adaptive-mass in the light of the geometry structure of the problem itself. As an application example in machine learning, we apply elastic optimal transport to both unsupervised domain adaptation and partial domain adaptation tasks. It adaptively transports masses from source domain to target domain by taking domain shift into consideration and respecting the ubiquity of noises or outliers in the data, in order to improve the generalization performance. The experiment results on the benchmarks show that ELOT significantly outperforms the state-of-the-art methods. As a powerful distribution matching tool, elastic optimal transport might be of interests to the broad areas such as artificial intelligence, healthcare, physics, operations research, urban science, etc. The source code is available in the supplementary material.
Information Shapes Koopman Representation
Authors: Xiaoyuan Cheng; Wenxuan Yuan; Yiming Yang; Yuanzhao Zhang; Sibo Cheng; Yi He; Zhuo Sun
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The Koopman operator provides a powerful framework for modeling dynamical systems and has attracted growing interest from the machine learning community. However, its infinite-dimensional nature makes identifying suitable finite-dimensional subspaces challenging, especially for deep architectures. We argue that these difficulties come from suboptimal representation learning, where latent variables fail to balance expressivity and simplicity. This tension is closely related to the information bottleneck (IB) dilemma: constructing compressed representations that are both compact and predictive. Rethinking Koopman learning through this lens, we demonstrate that latent mutual information promotes simplicity, yet an overemphasis on simplicity may cause latent space to collapse onto a few dominant modes. In contrast, expressiveness is sustained by the von Neumann entropy, which prevents such collapse and encourages mode diversity. This insight leads us to propose an information-theoretic Lagrangian formulation that explicitly balances this tradeoff. Furthermore, we propose a new algorithm based on the Lagrangian formulation that encourages both simplicity and expressiveness, leading to a stable and interpretable Koopman representation. Beyond quantitative evaluations, we further visualize the learned manifolds under our representations, observing empirical results consistent with our theoretical predictions. Finally, we validate our approach across a diverse range of dynamical systems, demonstrating improved performance over existing Koopman learning methods.
Guaranteed Simply Connected Mesh Reconstruction from an Unorganized Point Cloud
Authors: Liyan Chen; Jingyi Li; Qixing Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce an approach that reconstructs a closed surface mesh from a noisy point cloud, where the topology of surface is guaranteed to be simply connected, i.e., homeomorphic to a topological 2-sphere. This task enjoys a wide range of applications, e.g., 3D organ and vessel reconstruction from CT scans. Central to our approach is a robust module that takes a collection of oriented triangles in a 3D triangulation as input and outputs a simply connected volumetric mesh whose boundary approximates the input triangles. Starting from a 3D Delaunay triangulation of the input point cloud and initial triangle orientations obtained through a spectral approach, our approach alternates between applying the module to obtain a reconstruction and using that reconstruction to reorient the input triangles.  Experimental results on real and synthetic datasets demonstrate the effectiveness of our approach.
Transformers with Endogenous In-Context Learning: Bias Characterization and Mitigation
Authors: Haotian Wang; Hao Zou; Haoxuan Li; Haoang Chi; Yang Shi; Yuanxing Zhang; Wenjing Yang; Xinwang Liu; Zhouchen Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In-context learning (ICL) enables pre-trained transformers (TFs) to perform few-shot learning across diverse tasks, fostering growing research into its underlying mechanisms. However, existing studies typically assume a causally-sufficient regime, overlooking spurious correlations and prediction bias introduced by hidden confounders (HCs). As HC commonly exists in real-world cases, current ICL understandings may not align with actual data structures. To fill this gap, we contribute the pioneer theoretical analysis towards a novel problem setup termed as ICL-HC, which offers understanding the effect of HC on the pre-training of TFs and the following ICL prediction. Our theoretical results entail that pre-trained TFs exhibits certain prediction bias with proportional to the confounding strength. To migrate such prediction bias, we further propose a gradient-free debiasing method named Double-Debiasing (DDbias) by collecting and prompting with extremely few unconfounded examples, correcting pre-trained TFs with unbiased ICL predictions. Extensive experiments on regression tasks across diverse designs of the TF architectures and data generation protocols verify both our theoretical results and the effectiveness of the proposed DDbias method.
REMem: Reasoning with Episodic Memory in Language Agent
Authors: Yiheng Shu; Saisri Padmaja Jonnalagedda; Xiang Gao; Bernal Jiménez Gutiérrez; Weijian Qi; Kamalika Das; Huan Sun; Yu Su
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans excel at remembering concrete experiences along spatiotemporal contexts and performing reasoning across those events, i.e., the capacity for episodic memory. In contrast, memory in language agents remains mainly semantic, and current agents are not yet capable of effectively recollecting and reasoning over interaction histories. We identify and formalize the core challenges of episodic recollection and reasoning from this gap, and observe that existing work often overlooks episodicity, lacks explicit event modeling, or overemphasizes simple retrieval rather than complex reasoning. We present REMem, a two-phase framework for constructing and reasoning with episodic memory: 1) Offline indexing, where REMem converts experiences into a hybrid memory graph that flexibly links time-aware gists and facts. 2) Online inference, where REMem employs an agentic retriever with carefully curated tools for iterative retrieval over the memory graph. Comprehensive evaluation across four episodic memory benchmarks shows that REMem substantially outperforms state-of-the-art memory systems such as Mem0 and HippoRAG 2, showing 3.4% and 13.4% absolute improvements on episodic recollection and reasoning tasks, respectively. Moreover, REMem also demonstrates more robust refusal behavior for unanswerable questions.
DRBench: A Realistic Benchmark for Enterprise Deep Research
Authors: Amirhossein Abaskohi; Tianyi Chen; Miguel Muñoz-Mármol; Curtis Fox; Amrutha Varshini Ramesh; Étienne Marcotte; Xing Han Lù; Nicolas Chapados; Spandana Gella; Christopher Pal; Alexandre Drouin; Issam H. Laradji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, "What changes should we make to our product roadmap to ensure compliance with this standard?") that require identifying supporting facts from both the public web and private company knowledge base. Each task is grounded in realistic user personas and enterprise context, spanning a heterogeneous search space that includes productivity software, cloud file systems, emails, chat conversations, and the open web. Tasks are generated through a carefully designed synthesis pipeline with human-in-the-loop verification, and agents are evaluated on their ability to recall relevant insights, maintain factual accuracy, and produce coherent, well-structured reports. We release 100 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance. We demonstrate the effectiveness of DRBench by evaluating diverse DR agents across open- and closed-source models (such as GPT, Llama, and Qwen) and DR strategies, highlighting their strengths, weaknesses, and the critical path for advancing enterprise deep research. Code and data are available at https://github.com/ServiceNow/drbench.
Consistent Text-to-Image Generation via Scene De-Contextualization
Authors: Song Tang; Peihao Gong; Kunyu LI; Kai Guo; Boyu Wang; Mao Ye; Jianwei Dr. Zhang; Xiatian Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phenomenon called identity (ID) shift. Previous methods have tackled this issue, but typically rely on the unrealistic assumption of knowing all target scenes in advance. This paper reveals that a key source of ID shift is the native correlation between subject and scene context, called scene contextualization, which arises naturally as T2I models fit the training distribution of vast natural images. We formally prove the near-universality of this scene-subject correlation and derive theoretical bounds on its strength. On this basis, we propose a novel, efficient, training-free prompt embedding editing approach, called Scene De-Contextualization (SDeC), that imposes an inversion process of T2I’s built-in scene contextualization. Specifically, it identifies and suppresses the latent scene-subject correlation within the ID prompt’s embedding by quantifying SVD directional stability to re-weight the corresponding eigenvalues adaptively. Critically, SDeC allows for per-scene use (one prompt per scene) without requiring prior access to all target scenes. This makes it a highly flexible and general solution well-suited to real-world applications where such prior knowledge is often unavailable or varies over time. Experiments demonstrate that SDeC significantly enhances identity preservation while maintaining scene diversity.
Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
Authors: Preetum Nakkiran; Arwen Bradley; Adam Golinski; Eugene Ndiaye; Michael Kirchhof; Sinead Williamson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) often lack meaningful confidence estimates for the semantic content of their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LLMs are remarkably well-calibrated: they can meaningfully assess confidence in various open-ended question-answering tasks, despite training only on next-token prediction. To formalize this phenomenon, we introduce "$B$-calibration," a notion of calibration parameterized by the choice of equivalence classes. Our main theoretical contribution establishes a mechanism for why semantic calibration emerges in base LLMs, leveraging a recent connection between calibration and local loss optimality. This theoretical mechanism leads to a testable prediction: base LLMs will be semantically calibrated when they can easily predict their own distribution over semantic answer classes before generating a response. We state three implications of this prediction, which we validate through experiments: (1) Base LLMs are semantically calibrated across question-answering tasks, (2) instruction-tuning procedures systematically break this calibration, and (3) chain-of-thought reasoning breaks calibration (intuitively because models cannot predict their final answers before completing their generation). To our knowledge, our work provides the first principled explanation of when and why semantic calibration emerges in LLMs.
AlphaFlow: Understanding and Improving MeanFlow Models
Authors: Huijie Zhang; Aliaksandr Siarohin; Willi Menapace; Michael Vasilkovsky; Sergey Tulyakov; Qing Qu; Ivan Skorokhodov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: MeanFlow has recently emerged as a powerful framework for few-step generative modeling trained from scratch, but its success is not yet fully understood. In this work, we show that the MeanFlow objective naturally decomposes into two parts: trajectory flow matching and trajectory consistency. Through gradient analysis, we find that these terms are strongly negatively correlated, causing optimization conflict and slow convergence. Motivated by these insights, we introduce $\alpha$-Flow, a broad family of objectives that unifies trajectory flow matching, Shortcut Model, and MeanFlow under one formulation. By adopting a curriculum strategy that smoothly anneals from trajectory flow matching to MeanFlow, $\alpha$-Flow disentangles the conflicting objectives, and achieves better convergence. When trained from scratch on class-conditional ImageNet-1K 256×256 with vanilla DiT backbones, $\alpha$-Flow consistently outperforms MeanFlow across scales and settings. Our largest $\alpha$-Flow-XL/2+ model achieves new state-of-the-art results using vanilla DiT backbones, with FID scores of 2.58 (1-NFE) and 2.15 (2-NFE). The source code and pre-trained checkpoints are available on \url{https://github.com/snap-research/alphaflow}.
From Embedding to Control: Representations for Stochastic Multi-Object Systems
Authors: Xiaoyuan Cheng; Yiming Yang; Wei Jiang; Chenyang Yuan; Zhuo Sun; Yukun Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper studies how to achieve accurate modeling and effective control in stochastic nonlinear dynamics with multiple interacting objects. However, non-uniform interactions and random topologies make this task challenging. We address these challenges by proposing Graph Controllable Embeddings (GCE), a general framework to learn stochastic multi-object dynamics for linear control. Specifically, GCE is built on Hilbert space embeddings, allowing direct embedding of probability distributions of controlled stochastic dynamics into a reproducing kernel Hilbert space (RKHS), which enables linear operations in its RKHS while retaining nonlinear expressiveness. We provide theoretical guarantees on the existence, convergence, and applicability of GCE. Notably, a mean field approximation technique is adopted to efficiently capture inter-object dependencies and achieves provably low sample complexity. By integrating graph neural networks, we construct data-dependent kernel features which are capable of adapting to dynamic interaction patterns and generalizing to even unseen topologies with only limited training instances. GCE scales seamlessly to multi-object systems of varying sizes and topologies. Leveraging the linearity of Hilbert spaces, GCE also supports simple yet effective control algorithms for synthesizing optimal sequences. Experiments on physical systems, robotics, and power grids validate GCE and demonstrate consistent performance improvement over various competitive embedding methods in both in-distribution and few-shot tests.
From Verifiable Dot to Reward Chain: Harnessing Verifiable Reference-based Rewards for Reinforcement Learning of Open-ended Generation
Authors: Yuxin Jiang; Yufei Wang; Qiyuan Zhang; Xingshan Zeng; Liangyou Li; Jierun Chen; Chaofan Tao; Haoli Bai; Lifeng Shang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning with verifiable rewards (RLVR) succeeds in reasoning tasks (e.g., math and code) by checking the final verifiable answer (i.e., a verifiable dot signal). However, extending this paradigm to open-ended generation is challenging because there is no unambiguous ground truth. Relying on single-dot supervision often leads to inefficiency and reward hacking. To address these issues, we propose reinforcement learning with verifiable reference-based rewards (RLVRR). Instead of checking the final answer, RLVRR extracts an ordered linguistic signal from high-quality references (i.e, reward chain). Specifically, RLVRR decomposes rewards into two dimensions: content, which preserves deterministic core concepts (e.g., keywords), and style, which evaluates adherence to stylistic properties through LLM-based verification. In this way, RLVRR combines the exploratory strength of RL with the efficiency and reliability of supervised fine-tuning (SFT). Extensive experiments on more than 10 benchmarks with Qwen and Llama models confirm the advantages of our approach. RLVRR (1) substantially outperforms SFT trained with ten times more data and advanced reward models, (2) unifies the training of structured reasoning and open-ended generation, and (3) generalizes more effectively while preserving output diversity. These results establish RLVRR as a principled and efficient path toward verifiable reinforcement learning for general-purpose LLM alignment.