NeurIPS 2026 Papers

Commercial egocentric devices capture human behavior in everyday settings, yet full-body motion reconstruction must generalize across diverse cameras and mountings. This is challenging because head trajectories under-determine body motion, while hand observations are intermittent and camera-dependent: a hand may disappear simply because it has left the camera's field of view. Existing methods treat such absences as missing data, relying on fixed visibility regimes or post-hoc optimization, and fail to generalize across devices.We present \modelname, a sequence-level diffusion framework for device-agnostic egocentric full-body motion reconstruction. Our key insight is that egocentric videos contain sequence-level evidence about both the wearer and the camera-induced visibility structure, enabling \modelname to infer consistent body shape and coherent motion from sparse head and hand cues. To prevent overfitting to a single camera setup, we further introduce geometry-aware visibility augmentation, which synthesizes visibility patterns from realistic variations in camera geometry. We also present OmniEgoDB, the first mocap benchmark with ground-truth motion captured across multiple consumer egocentric devices. Experiments on synthetic, real-device, and in-the-wild settings demonstrate superior motion reconstruction, robust cross-device generalization, and coherent in-the-wild results.

Dexterous grasping is the foundational primitive in embodied AI, demanding massive data to train robust models. As real-world data collection is expensive, simulation has become the mainstream paradigm. Yet, while cluttered scenes best reflect real-world applications, learning to grasp within them is bottlenecked by a critical scarcity of large-scale data. To resolve this, we curate high-quality 3D objects and supporting bases, proposing a scalable seed-and-filter strategy that bypasses sluggish scene-level optimization. This yields an unprecedented benchmark comprising over 2.6 million scenes and 0.4B scene-specific grasp ground truths, featuring diverse realistic layouts paired with rich semantic and geometric observations. Furthermore, we introduce the OmniDex model to overcome the grasp multimodality and last-millimeter precision errors plaguing current generative models. By coupling Soft Winner-Takes-All learning with human-inspired physical constraints during training, and utilizing physics-driven ranking, our approach achieves robust dexterous grasping without the latency of post-optimization. Experimental results show that OmniDex model achieves state-of-the-art performance and strong generalization across diverse scenes, views, and unseen objects.

Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a systematic understanding of vulnerabilities in omni-modal interactions remains lacking. To bridge this gap, we establish a modality-semantics decoupling principle and construct the AdvBench-Omni dataset, which reveals a significant vulnerability in OLLMs. Mechanistic analysis uncovers a Mid-layer Dissolution phenomenon driven by refusal vector magnitude shrinkage, alongside the existence of a modal-invariant pure refusal direction. Inspired by these insights, we extract a golden refusal vector using Singular Value Decomposition and propose OmniSteer, which utilizes lightweight adapters to modulate intervention intensity adaptively. Extensive experiments show that our method not only increases the Refusal Success Rate against harmful inputs from 69.9% to 91.2%, but also effectively preserves the general capabilities across all modalities.

Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruction-following capabilities. Despite this progress, existing approaches primarily focus on language and image modalities, which also remain the dominant modalities for human-AI interaction in current embedders. In this paper, we propose the first Omni-Interactive Universal Embedder (OmniUE), which not only learns a unified embedding space across text, video, and audio by leveraging intermediate-layer representations from dedicated learnable tokens, but also supports omni-interactive querying, enabling users to provide inputs in the form of text, visual regions of interest, and audio spans. Within OmniUE, visual and audio segmenters process diverse user interactions and integrate them with an omni-LLM to produce user-conditioned any-to-any embeddings via context aggregation. To evaluate OmniUE’s omni-interactive capabilities, we introduce OmniCHOIR, benchmarking models for omni-interactive compositional audio retrieval based on the given text, video, and audio as well as unimodal or multimodal interaction prompts. OmniUE consistently surpasses state-of-the-art baselines across diverse modalities, with average improvements of 10.5% on textual-interactive video benchmarks (MMEB-v2-video), 1.1% on audio tasks (MAEB), 83.7% on visual-interactive benchmarks (SCaR), and 24.1% on our omni-interactive OmniCHOIR benchmark We believe that jointly advancing omni-modal representation learning and omni-interactive querying paves the way toward universal embedders.

Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime $n \gg p$, where the number of graphs $n$ greatly exceeds the number of nodes per graph $p$. This overlooks biological domains such as omics, which operate in the opposite $n \ll p$ regime, characterized by large graphs of genes, transcripts, or proteins across few patient samples. This raises the question: \textit{how do GNNs perform in this low-sample, high-node omics setting?} We introduce \texttt{OgBench} (Omics-Graph Bench), the first benchmarking platform for graph-level prediction in the $n \ll p$ regime characteristic of omics data. We provide a standardized, end-to-end modular infrastructure from raw omics data to families of featured graphs with varied structural properties. We benchmark classical GNNs, as well as GNNs designed for large graphs and omics applications, alongside MLPs and machine learning baselines to establish reference performances. Our results show that widely used GNNs often do not outperform simple MLPs and classical baselines. These findings challenge the prevailing assumption that graph structure inherently adds value in this domain, fostering a critical reassessment of current learning paradigms. Ultimately, by exposing these limitations, OgBench provides the open-source ecosystem necessary for the community to develop and validate novel architectures explicitly tailored for biological graphs. The code is available at \url{https://anonymous.4open.science/r/ogbench-C1A9/README.md}.

Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky. However, progress in this direction remains difficult to measure due to the lack of a standardized offline RL benchmark for realistic multi-actuator, long-horizon plasma control problems in nuclear fusion. We introduce RL4F, an Offline Reinforcement Learning Benchmark for Plasma Control in Nuclear Fusion, providing closed-loop evaluation environments and baseline comparisons across four full-profile tracking tasks: rotation, density, temperature, and pressure. The dynamics function underlying the evaluation environment is built from historical discharge data from DIII-D, a real-world Tokamak. We evaluate a broad set of imitation learning and offline RL baselines under a unified protocol. We find that offline model-based RL methods obtain the best average performance on most objectives, although no single method dominates all tasks, highlighting the importance of dynamics modeling in complex, long-horizon plasma control tasks. To foster further research, we open-source the codebase, datasets, and evaluation framework, providing a benchmark not only for the fusion community but also for algorithm development in offline RL. Our data and code are available at https://anonymous.4open.science/r/Anonymous-B71F

Offline evaluation of agentic systems often collapses trajectories to terminal success, discarding information about partial progress and inducing widespread ties. We argue that this trajectory collapse creates substantial statistical inefficiency by reducing effective sample size and weakening the ability to distinguish systems. We propose preference-based trajectory evaluation, which compares trajectories directly through temporal preferences over progress and time-to-return profiles. Across diverse agentic and interactive benchmarks, standard success-based metrics produce tied comparisons on roughly 75\% of instances, whereas trajectory-aware preferences reduce ties to roughly 30\%, improving discriminative power, ranking stability, and data efficiency. Our results suggest that benchmark saturation, often portrayed as the result of poor data collection or problem difficulty, may also be explained by the choice of evaluation measure.

Recent advances in deep learning have inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling methods are ineffective at boldly exploring attractive regions of the materials space due to their maximum likelihood training. In this work, we offer an alternative CMD technique based on offline model-based optimization (MBO) that fuses direct optimization of a target material property into generation. To that end, we introduce a domain-specific model, CliqueFlowmer, that incorporates recent advances in clique-based MBO into transformer and flow generation. We validate this model's optimization abilities and show that materials it produces strongly outperform those from generative baselines. To support specialized materials discovery applications and broader interdisciplinary research, we will release our code and model weights upon publication.

Offline Inverse Reinforcement Learning (IRL) aims to recover a reward function and imitate expert behavior solely from offline demonstrations. While recent Offline IRL approaches employ approximate dynamics to mitigate distribution shift, their performance is constrained by the unstable minimax framework as well as simplified representation and utilization. To address these issues, we propose Offline Inverse Reinforcement Learning with Diffusion Planner (OIDP), which leverages diffusion models to achieve stable Offline IRL. OIDP formulates a model-based conservative Q-optimization through inverse soft Q-learning, which is provably concave in Q-space with a controllable performance gap. Accordingly, we introduce a unified diffusion planner that models dynamics, uncertainty estimator, and policy, it captures multimodal distributions, directly tightens the objective gap bound, and at execution performs trajectory-guided policy sampling using the internalized dynamics knowledge. Experimental results on D4RL benchmarks demonstrate that OIDP outperforms state-of-the-art offline approaches.

We study offline constrained reinforcement learning with general function approximation in discounted constrained Markov decision processes. Existing methods either require full data coverage for evaluating unsupported intermediate policies, are not oracle efficient, or requires the knowledge of data-generating distribution for policy extraction. We propose PDOCRL, an oracle-efficient primal-dual algorithm based on a decomposed linear-programming formulation. The decomposition makes the policy an explicit optimization variable, avoiding policy extraction through the unknown distribution $\mu_D$. We show that naive restricted saddle-point formulations may have spurious saddle points, so realizability of an optimal solution alone is insufficient. We then show that, under a stronger but explicit realizability assumption, every restricted saddle point is optimal, avoiding the regularization and auxiliary function classes used in prior LP-based analyses. These ingredients yield PDOCRL, an oracle-efficient primal-dual algorithm that computes a near saddle point of the empirical decomposed LP Lagrangian and returns a near-optimal, near-feasible policy with a $\widetilde{\mathcal O}(\epsilon^{-2})$ sample guarantee under partial coverage, without access to $\mu_D$ or a reference policy. Empirically, PDOCRL is competitive with strong baselines on standard offline constrained RL benchmarks.

The recent trend of integrating autonomous agents into operating systems, supported by the Model Context Protocol (MCP) tools and Deep Searching, has driven research on efficient assistants for daily office tasks like document writing, spreadsheet analysis, and presentation generation. However, training these agents still faces several challenges: the overemphasis on question-answering tasks at the expense of multi-turn generative tasks; the absence of a training paradigm that jointly enhances coding and tool-calling capabilities; the difficulty of evaluating tool efficiency and artifact quality in the presence of drifting user intents. To address these problems, we deploy a unified multi-turn agent framework to collect real-world user interactions, creating \textit{OfficeInstruct}: a comprehensive dataset of 199k high-quality trajectories across Doc, Spreadsheet, and PPT, with an average of 56.2 steps. To ensure data quality, we propose a graph-based evidence evaluation method that models interaction histories as directed graphs, filtering out redundant tool cycles, execution errors, and hallucinations. Experiments demonstrate that our \textit{OfficeAgent} 8B and 14B models surpass open-source baselines on in-domain and out-of-distribution benchmarks, with the 32B variant attaining a 96.19\% success rate on the newly proposed \textit{OfficeAgentBench}, surpassing proprietary models like Claude-4.5-Sonnet and Gemini-3-Pro.

Off-policy reinforcement learning (RL) tries to learn the value of a target policy while data comes from a different behavior policy. This often requires trading off bias for variance or stability. One way to trade these off is to linearly interpolate between the behavior and target policies, but this represents only one point in a broader space of interpolation mechanisms. We introduce \emph{excursion policies}--a structured alternative where the agent follows the target policy for a multi-step ``excursion'' before reverting to the behavior policy. We derive dynamic programming results for these policies, establishing the theoretical basis for their use in RL. We further develop provably sound off-policy temporal-difference (TD) algorithms for excursion value estimation. We find that excursion policies provide an efficiency-stability trade-off comparable to mixture policies in continuous control benchmarks, and our analysis illustrates conceptual differences in their behavior.

Evaluating large language models (LLMs) via online human feedback is prohibitively expensive and time-consuming. Off-policy evaluation (OPE) addresses this by estimating LLM policy performance from offline data. However, traditional OPE estimators such as Inverse Propensity Score (IPS) suffer from high variance. Marginalized IPS (MIPS) addresses this variance issue by reweighting the IPS weights via condensed semantic embeddings. However, MIPS assumes embeddings contain sufficient reward-predictive information, an assumption that off-the-shelf embeddings often fail to satisfy. To address this, in this paper, we propose Semantic Bottleneck Embedding (SBE), which learns representations for marginalized reweighting via a conditional information bottleneck, compressing reward-irrelevant information while preserving reward-predictive content to achieve both bias and variance reduction. Our objective targets minimizing the derived mean squared error upper bound of the resulting MIPS estimator. Empirically, evaluation on the HelpSteer2 and UltraFeedback datasets across Qwen, Gemma, and Llama policies show SBE-MIPS reduces MSE over prior baselines.

Large language models are increasingly used as human simulators for interactive evaluation and social simulation, yet current post-training often produces homogeneous, overly helpful systems unlike real people, creating a behavioral Sim2Real gap that prompting alone cannot close. We present Odyssim, a large-scale open study of behavioral foundation models organized around SOUL, whose five capability Axes (CONV, SS, COG, ROLE, EVAL) jointly guide data curation and evaluation. We release three artifacts: (1) SOULIndex, a 23-task evaluation suite for human-behavior simulation; (2) OdysSim, a corpus of 21.4M interactions, approximately 10B tokens, from 63 public sources, unified into a conversational format and retrofitted with back-generated social contexts; and (3) an end-to-end recipe combining midtraining, task-specific RL with VFRL, and expert distillation into a single deployable model. On SOULIndex, OdysSim-8B achieves an average score of 62.4, comparable to frontier models such as GPT-5.5 (62.6), ranks first on 8 of 23 tasks, and outperforms prior open behavioral-simulation baselines by 14.2 points on average. We also identify and mitigate reward-hacking failures in judge-based behavioral RL, including judge manipulation and short-response collapse. We release the corpus, benchmark, recipes, and checkpoints.

Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, quantile risk control extends this framework to quantile-based risk measures. However, existing methods either suffer from excessive conservatism or lack rigorous finite-sample guarantees. To address these limitations, we introduce Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity. Our key idea is to formulate risk control as a finite-occupancy problem by partitioning the loss space with the ordered calibration losses. Specifically, we estimate the distribution of test losses across the resulting bins and upper-bound the risk by the maximum loss attained within each bin. We then select the parameter $\lambda$ such that this upper bound does not exceed a predefined threshold $\alpha$ with high probability $1-\delta$. Theoretically, we establish a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_p(n^{-1/2})$. Extensive experiments demonstrate the effectiveness of our method, reducing the risk gap by up to 78.64\% on common benchmarks.

Occupancy world models use historical occupancy states to forecast future 3D scenes, but their robustness under corrupted temporal inputs remains poorly understood. Existing evaluations primarily emphasize clean forecasting accuracy and provide limited evidence about how errors enter, persist, and propagate through the occupancy perception-forecasting chain. This paper introduces OccStress, a robustness stress-testing benchmark for the occupancy forecasting chain. OccStress contains 61 corruption categories and 80k+ anchors. OccStress covers both 3D occupancy perception and 4D occupancy forecasting through two complementary tracks. This design separates realistic pipeline errors from the intrinsic sensitivity of 4D forecasting models to corrupted occupancy states. OccStress further defines temporal injection protocols to test whether errors in the current state, recent history, or earlier history affect future forecasts differently. OccStress provides aggregate metrics to evaluating robustness along occupancy forecasting chain. Experiments on OccStress reveal that current occupancy models are substantially affected by both upstream prediction errors and direct state corruptions, and that clean performance alone is an insufficient indicator of temporal robustness. Dataset: https://hf.co/datasets/OccStress/OccStress. Code: https://anonymous.4open.science/r/OccStress.

Electromagnetic signal modeling is increasingly moving toward self-supervised pretraining on heterogeneous I/Q observations. However, an observed waveform is jointly shaped by signal generation, propagation environments, and acquisition processes, which mix reusable structural regularities with observation-specific variations. Existing methods usually pursue unified pretraining with reconstruction objectives on mixed observations. This heterogeneity creates two key mismatches for reconstruction pretraining: (1) appearance mismatch, induced by propagation-dependent waveform variations; and (2) temporal mismatch, induced by acquisition-dependent sampling coordinates. These mismatches make observation-specific appearances and sampling coordinates easier to reconstruct than stable signal structures, inducing observational shortcuts. We identify this failure mode as observation-biased pretraining. To address this, we propose Structural Pretraining with Time Alignment for heterogeneous EM signals. It mitigates temporal mismatch with RoPE-fs, which maps token positions to sampling-rate-normalized physical-time positions, and reduces reliance on appearance shortcuts by replacing waveform reconstruction with masked prediction of discrete structural units. These units are organized into atomic structural units and emergent units that capture variable-length compositional patterns beyond fixed patch boundaries. Across diverse EM benchmarks, our method improves both full-supervision and few-shot adaptation over existing EM pretraining baselines, showing that the learned representations better preserve structural consistency when observations drift in waveform appearance and temporal scale.

Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with time-varying interventions, observability of the latent dynamics from observed data is necessary for identifying dynamic treatment effects, linking control-theoretic observability to causal identifiability, even when hidden confounders affect both treatments and outcomes. We derive a continuous-time adjustment formula expressing potential outcome dis- tributions under treatment trajectories via the measurement model, latent dynamics, and the filtering distribution over latent states given observed histories. We propose Observable Neural ODEs (ObsNODEs), Neural ODE models in ob- servable normal form for causal forecasting. ObsNODEs learn continuous-time dynamics with states reconstructible from observations, enabling outcome predic- tion under alternative treatment paths. Experiments on synthetic cancer data, semi-synthetic data based on MIMIC-IV, and real-world sepsis data show strong performance over recent sequence models.

Irregular data assimilation is difficult because sparse, off-grid observations do not constrain the same state corrections from one cycle to the next. As the observation geometry changes, the set of supported correction directions changes as well. Many learning-based DA methods infer updates in a fixed latent or control space, even when the current observation layout supports a different set of correction directions. To address this mismatch, we introduce Observability-Constrained DataAssimilation with Control-Space Inference (ObsConDA), which determines those supported correction directions from the current background and observation geometry and infers only their control coefficients. On real surface-station assimilation, ObsConDA gives the best held-out station accuracy and the best full-field reconstruction among the tested classical and learned baselines. The same advantage carries to matched synthetic observations, explicit geometry shifts, and synthetic dynamical systems, supporting the view that the usable correction directions must change from cycle to cycle. Ablations show that the gain comes from adapting those directions themselves rather than from adding a latent corrector alone.