NeurIPS 2026 Papers

Imitation learning (IL)—training an agent to replicate expert behavior from demonstrations—underpins applications from robotics to language model training. Standard approaches such as Behavior Cloning (BC) are known to suffer from compounding errors and performance plateaus, particularly when the learner cannot perfectly represent the expert’s policy (e.g., as is typical in distillation). Two interventions are widely understood empirically to improve performance: querying the expert interactively along the learner’s own trajectories, and using value function estimation en route to generating a policy. We investigate the nature of these improvements and their potentially surprising interplay. Our main finding is that expert interaction relaxes the representational demands on the learner: one only needs a model capable of realizing the expert’s value function, bypassing the (often stricter) requirement of realizing the expert’s policy itself. Concretely, we introduce OVI, an interactive IL algorithm that is statistically and computationally efficient whenever the learner can represent the expert’s value function. We complement this with a negative result showing that interaction is necessary: without significantly stronger representational assumptions than expert-value realizability alone, a broad class of value-based IL algorithms cannot succeed in the offline setting. These findings bear out empirically: OVI outperforms offline policy-based (BC), interactive policy-based (DAgger), and offline value-based IL methods, with the largest gains when the learner network is substantially less expressive than the expert’s.

Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, *e.g.,* band power, connectivity, complexity, and more. Modern EEG foundation models bypass this catalog, learn directly from raw signals via self-supervised pretraining, and match or outperform feature-engineered baselines on most clinical benchmarks. Whether the two representations align is an open question, which we decompose into three sub-questions: *what does the model learn*, *what does the model use*, and *how much can be explained*. We answer them with layer-wise ridge probing, LEACE-style cross-covariance subspace erasure, and a transparent classifier benchmarked against a random-feature baseline. The audit covers three foundation models (CSBrain, CBraMod, LaBraM), five clinical tasks (MDD, Stress, ISRUC-Sleep, TUSL, Siena), and a 6-family 63-feature lexicon. Of the $945$ (model, task, feature) units, $648$ ($68.6\%$) are representation-causal and $199$ ($21.1\%$) are encoded-only. Across tasks, $50$ features qualify as universal candidates with strong support (all three architectures RC) in two or more tasks. Frequency-domain features dominate, but the other five families each contribute substantial causal mass. Confirmed features recover, on average, $79.3\%$ of the foundation model's advantage over the random baseline, with a clean task gradient (MDD $\approx 0.99$ down to Stress $\approx 0.56$): tasks near ceiling are almost fully recovered by the lexicon, while harder tasks leave a non-trivial residual that pinpoints a concrete target for future concept discovery.

Despite significant advances in autonomous web navigation, current methods remain far from human-level performance in complex web environments. We argue that this limitation stems from Topological Blindness, where agents are forced to explore via trial-and-error without access to the global topological structure of the environment. To overcome this limitation, we introduce WebNavigator, which reframes web navigation from probabilistic exploration into deterministic retrieval and pathfinding. WebNavigator constructs Interaction Graphs via zero-token cost heuristic exploration offline and implements a Retrieve-Reason-Teleport workflow for global navigation online. WebNavigator achieves state-of-the-art performance on WebArena and Online-Mind2Web. On WebArena multi-site tasks, WebNavigator achieves a 72.9% success rate, more than doubling the performance of enterprise-level agents. This work reveals that Topological Blindness, rather than model reasoning capabilities alone, is an underestimated bottleneck in autonomous web navigation.

Accurate contact modeling is fundamental to understanding hand–object interaction, yet existing contact representations are typically restricted to object surfaces and rely on hand‑crafted rules to recover contact details, leading to severe penetrations and implausible results. To better exploit the rich detail in motion‑capture data, we introduce Volumetric Contact (VolCo), a representation that expands surface points to a set of 3D volumetric grids. VolCo encodes 3D contact that allows precise hand part recovery, and is organized in an inherent hierarchy: local contact details within each volume and global hand geometry across all volumes. Our framework, VolCoDiff, employs two modules to capture local and global features following this hierarchy. For local contact details, we use a 3D variational autoencoder to model the possible hand configurations conditioned on the local object signed distance field (SDF). For global hand geometry, we design a prior‑guided diffusion model that learns the distribution of compressed latent features aggregated from the volumetric grids. We evaluate our method on two benchmark datasets and demonstrate state‑of‑the‑art performance in penetration and stability, indicating the capability to generate tight grasps with much less severe penetrations.

Speaker identification benchmarks evaluate how accurately a model can predict a ground-truth speaker label for a given utterance, treating voice identity as a property of the recording's producer. However, these benchmarks do not test whether a model aligns with human perception of voice identity. Whether a human would hear two voices and interpret them as the same speaker or not has consequences for intellectual property rights, cybersecurity, and everyday life. We make two contributions. First, we introduce the Voice Identity Perception benchmark (VIPBench), built from the identity judgments of English-speaking crowdworkers on stratified celebrity voice pairs. VIPBench consists of 124,876 same/different identity judgments from 1,290 participants on 9,800 voice pairs across 100 speakers, spanning real recordings, AI voice clones generated by a state-of-the-art Text-to-Speech (TTS) system, and continuously morphed voices. Our second contribution is to define four evaluation tasks: predicting the listener same-speaker agreement rate; binary same/different classification against the majority listener vote, evaluated for both ranking and calibration; alignment between the model's and listeners' speaker-similarity structures; and whether a predictor fit on real speech still works on voice clones and morphs. We report baselines for ten publicly available speech representations. We find that the perception target re-orders model rankings relative to metadata: supervised embeddings (trained on metadata speaker labels) still outperform self-supervised models (which learn voice structure without identity supervision), and no model reaches the noise ceiling. VIPBench enables systematic evaluation of speaker representations against human voice-identity perception across real, cloned, and morphed speech, motivating speaker models trained on perceptual judgments directly.

Clustered Federated Learning (CFL) mitigates statistical heterogeneity by grouping clients with similar data distributions. However, existing CFL frameworks predominantly rely on proxy metrics, such as gradient or data similarities, which collapse fine-grained class-level similarity into macroscopic client-level scores. This coarse-grained aggregation inherently masks critical class-wise conflicts, inadvertently exposing the federation to severe negative transfer. In this paper, we propose a fundamental paradigm shift from heuristic similarity to *explicit value attribution*. We introduce **FedCap**, a novel utility-driven CFL framework that explicitly quantifies class-wise marginal utilities to capture the true generalization impact of local updates. Guided by this attribution mechanism, FedCap employs a conflict-aware clustering algorithm to systematically eliminate intra-cluster negative transfer, while orchestrating safe, supply-demand-guided representation sharing across clusters. Crucially, we provide rigorous theoretical guarantees demonstrating the robustness of our underlying attribution mechanism against four major types of distribution shifts. Extensive experiments across diverse heterogeneity settings show that FedCap consistently achieves state-of-the-art accuracy. The code is available for anonymous access at https://anonymous.4open.science/r/FedCap-605E.

We introduce $\textbf{UniCon3R}$, a unified feed-forward framework for online human-scene 4D reconstruction from monocular video. Current feed-forward human-scene reconstruction methods suffer from artifacts, where bodies float above the ground or penetrate parts of the scene. A key reason is the lack of effective interaction modelling between the human and the environment. Our goal is to exploit contact between the human and the scene during inference to actively improve the human mesh reconstruction. To that end, we explicitly model interaction by inferring 4D contact from the human pose and scene geometry and use the contact as a corrective cue for generating the pose. This enables UniCon3R to jointly recover scene geometry and spatially aligned 4D humans within the scene. Experiments on standard human-centric video benchmarks show that UniCon3R outperforms state-of-the-art baselines on physical plausibility and global human motion estimation while preserving fast, feed-forward inference speeds. The results validate our central claim: contact serves as a powerful internal prior, thus establishing a new paradigm for physically grounded joint human-scene reconstruction. Source code and models will be released upon acceptance.

Humanoid whole-body loco-manipulation requires scalable and physically reliable demonstrations, yet existing human-object interaction datasets lack direct contact supervision and egocentric observability, often resulting in physically inconsistent interactions. We present DeXHOI, a multimodal egocentric HOI benchmark for whole-body dexterous interaction, capturing synchronized body motion, object trajectories, tactile sensing, and egocentric RGB-D across diverse spatial layouts and long-horizon activities. To improve physical consistency, we introduce a tactile-guided refinement pipeline that leverages measured pressure to correct hand-object interactions while preserving global motion fidelity. We further establish an Egocentric HOI Recovery Benchmark for contact-aware reconstruction from first-person observations, along with EgoTacti, a tactile prediction model. Together, EgoTacti provides a scalable and physically grounded foundation for learning humanoid whole-body dexterous interaction, pointing toward low-cost egocentric data collection for embodied intelligence.

Computer Use Agents (CUAs) can act through both atomic GUI actions ($\textit{e.g., click, type}$) and high-level tool calls ($\textit{e.g., API-based file operations}$), but they are often confused by this hybrid action space: they do not know when to continue with GUI actions and when to switch to tools, and finally fail to select the optimal execution path. We view this orchestration problem as GUI-Tool path selection: deciding when the agent should continue with GUI actions and when it should switch to tool calls to form an effective execution trajectory. This difficulty stems from two issues. First, high-quality interleaved GUI-Tool trajectories are scarce, and collecting real tool trajectories is expensive and brittle. Second, existing supervision provides limited guidance for GUI-Tool path selection, as most methods focus on step-level action imitation or final task completion and offer little trajectory-level feedback on whether GUI-Tool switching leads to a more effective execution path. In this paper, we propose $\textbf{ToolCUA}$, an end-to-end agent designed to learn optimal GUI-Tool path selection through a staged training paradigm. We first introduce an $\textbf{Interleaved GUI-Tool Trajectory Scaling Pipeline}$ that repurposes abundant static GUI trajectories and synthesizes a grounded library of tools, making it possible to scale diverse GUI-Tool trajectories without manual engineering or real tool-trajectory collection.Based on this data, we perform Tool-Bootstrapped GUI RFT, which combines warmup SFT with single-turn RL to improve decisions at critical GUI-Tool switching points. Finally, we further optimize ToolCUA with $\textbf{Online Agentic RL}$ in a high-fidelity GUI-Tool environment, using a Tool-Efficient Path Reward that encourages both appropriate tool use and shorter execution paths. Experiments on OSWorld-MCP show that ToolCUA achieves 46.85% accuracy and outperforms the baseline by over 60% relatively, establishing a new state of the art among models of comparable scale. It also improves by 3.9% over GUI-only settings, demonstrating effective GUI-Tool orchestration. The results further suggest that training in a hybrid action space is a promising paradigm for real-world digital agents.

Humans systematically misrepresent probability in a stereotyped inverse-S pattern. It has been documented for decades, but its origin remains unexplained. We propose a Bayesian encoding–decoding account in which probabilities are represented by noisy internal signals and decoded by Bayes-risk minimization. For bounded probability stimuli, we show that distortion decomposes into boundary regression, likelihood repulsion, and prior attraction, yielding a key prediction: the classic inverse-S-shaped weighting pattern implies a U-shaped allocation of encoding precision with greater sensitivity near 0 and 1. Across judgment of relative frequency, lottery pricing, and risky choice, this U-shape is recovered from data without imposing any functional form on the encoding, and our framework outperforms deterministic weighting functions, bounded log-odds models, uniform-encoding Bayesian accounts, and matched efficient-coding models on held-out data. In a new dot probability estimation experiment with bimodal stimulus statistics, the recovered prior tracks the new distribution while the recovered encoding remains U-shaped. Together, these results identify the inverse-S-shaped probability weighting function as the joint product of a stable U-shaped encoding and a flexible prior, integrated by optimal Bayesian decoding.

Long-horizon RTL agents often collapse under context pollution, lacking the active, compartmentalized debugging strategies of human engineers. We introduce OneVeriAgent, a human-like framework modeled directly on expert cognitive workflows. First, mimicking how engineers probe waveform viewers, Agentic Temporal Exploration (ATE) enables dynamic, targeted signal queries, distilling dense simulator dumps into sparse, causal traces. Second, reflecting how designers compartmentalize tasks, a task-scoped Orchestrator enforces strict context isolation, preventing raw tool outputs from polluting the global reasoning trace. On the CVDP benchmark, OneVeriAgent achieves 95.8\% Pass@1 with Claude Opus 4.6, matching state-of-the-art proprietary swarm. Finally, we propose Tool-Validated Self-Distillation (TVSD) to bridge the open-source capability gap. By fine-tuning on self-generated, syntactically valid trajectories without external correctness oracles, TVSD lifts Gemma4-31B-Instruct's Pass@1 from 61.1\% to 74.8\%, demonstrating that fully open-weight RTL agents are viable.

We argue that sport represents the next grand challenge for Artificial Intelligence, a domain rich enough to expose the limits of current approaches and structured enough to drive rigorous scientific progress in understanding and modelling human physical skill. Despite rapid advances in language, vision, and robotic manipulation, the physical intelligence required to learn and adapt complex motor skills remains poorly understood and largely unaddressed. We identify two intertwined gaps: the absence of models for human physical skill, and the absence of the scientific agenda needed to build them. We propose concrete directions for how the community can begin to close both.

Recent 3D indoor scene generation methods often produce layouts with limited spatial feasibility, where pathways are blocked and objects are difficult to access or use, restricting their applicability in virtual reality and embodied environments. To address this challenge, this paper presents a new paradigm for 3D indoor scene generation that explicitly models interaction feasibility through a carefully designed geometric abstraction, termed the Interaction-Oriented Human Proxy (IOHP). The IOHP serves as a compact yet spatially expressive representation for human interaction occupancy, and can be directly inferred by large language models without task-specific training. Building on IOHP, we formulate layout optimization as a unified objective that resolves geometric conflicts between the proxy and scene elements, while using the proxy as a bridge to preserve functional relationships among furniture, thereby improving overall usability and accessibility. Benefiting from this design, our method eliminates reliance on data-driven human preference learning and complex layout-planning pipelines, producing accessible, usable, and spatially coherent scenes in an efficient and stable manner. Extensive experiments demonstrate that our approach significantly improves layout quality and spatial plausibility, highlighting the role of human-centric interaction modeling in 3D scene generation.

Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap, we run a series of classic memory experiments from psychology on both humans and language models. Across tasks, we find that out-of-the-box language models exhibit better memory than humans, even when prompted to imitate human behavior. We then show that better prompting strategies and the use of a compactor can cause language models to forget content in a more human-like way. Using these methods, we show preliminary evidence that language models with human-like memory constraints can function as more effective user simulators in a downstream education task. Finally, we release human reference data and benchmarks to support future work on simulating human memory with language models.

AI agents deployed on the open web are becoming more capable of performing actions that may breach contracts and violate computer security laws. In this work, we introduce $\text{ScrapeBench}$, the first agentic benchmark for evaluating the legal compliance of frontier AI agents with website scraping policies, including terms of service (ToS) and $\texttt{robots.txt}$ directives. Across widely used closed-weight and open-weight AI agents, we find that most agents exhibit low compliance rates (< 5\%) when instructed to scrape websites in our sample of 1,626 popular websites that explicitly prohibit scraping. In addition, we observe that even where AI agents actively check the applicable ToS and $\texttt{robots.txt}$, they continue to exhibit low compliance rates. Conversely, in our sample of 269 websites that do not prohibit scraping, we observe that agents regularly refuse to scrape despite it being lawful, such as Claude Opus 4.6 exhibiting an over-refusal rate of 98.8%. Lastly, our human baseline experiments ($n$ = 180 participants) suggest that humans exhibit compliance rates comparable to AI agents but over-refuse at much higher rates. We release our anonymized code and data in this repository: https://anonymous.4open.science/r/scrapebench-75D6

Building humanoid robots that perform generalizable whole-body loco-manipulation in the real world remains a fundamental challenge: existing approaches either rely on heavy task-specific reward engineering, rigidly replay reference motions that fail to generalize, or depend on costly teleoperation that limits scalability. While human videos capture diverse human behaviors, the motion priors inferred from them are inherently imperfect, suffering from occlusion, contact artifacts, and retargeting errors that render them unsuitable for direct policy learning. To this end, we present SUGAR, a data-driven framework that converts diverse human videos into deployable humanoid loco-manipulation skills, without any task-specific reward engineering or reference-motion conditioning at inference. SUGAR proceeds in three coupled stages: First, a fully automated pipeline extracts scalable kinematic interaction priors including human-object motion trajectories and contact labels from diverse human videos. Second, a privileged physics-based refiner utilizes a unified mimic-style reward and a progressive state pool to transform imperfect kinematic interaction priors into physically feasible, high-fidelity skills. Third, the refined skills are distilled into a hierarchical policy that comprises a task-guided planner and a command tracker for autonomous task execution. We evaluate our method on six representative loco-manipulation tasks in both simulation and real-world humanoid hardware. SUGAR substantially outperforms reference-tracking baselines, and its performance scales clearly with the amount of human video data. It also achieves zero-shot real-world transfer with reliable closed-loop execution, autonomous failure recovery and stable long-horizon performance under external perturbations. Project Page: https://sugar-humanoid.github.io/

**This position paper argues that the current frontier of expert-LLM collaboration is doubly jagged: it is uneven, not only because of jagged AI capabilities but also due to jagged AI risks**. As LLM systems improve, incompetence-driven risks (e.g., misinformation) may decline, but adversarial risks can persist or even increase, leading to the likely persistence of a jagged risk–reward frontier. To show jaggedness, we introduce the first occupation-level quantification of risk-aware productivity gains for expert–LLM collaboration over a realistic task distribution, using standard O\*NET computer-occupation tasks. For each task, we leverage six frontier models (e.g., GPT-5, Claude Opus) to produce structured risk–reward ratings and use 6 human experts to verify a subset for reliability, achieving high agreement. Risk captures the possible increase in individual, organizational, or societal harm due to employing LLMs. Reward captures possible productivity gains, i.e., time/cost savings at a fixed quality target after accounting for expert oversight to detect and mitigate risks. We aggregate task-level ratings into occupation-level scores. Our analysis shows that risk varies more than reward, yielding vast differences in risk–reward tradeoff: In safety-critical roles (e.g., *Information Security Engineers*), gains are offset by risk, whereas in less safety-critical roles (e.g., *Web Developers*), gains often substantially exceed risks. At the task-level, we identify high-reward and medium-risk tasks as the "Sweet spot" for expert-LLM collaboration. The identified sweet spot is evident in real-world deployments as the majority of Claude conversations assigned to O*NET tasks (from the recent Anthropic Economic Index) are high-reward and medium-risk, whereas high-risk tasks are minimally represented, supporting both our methodology and position.

Reconstructing multi-camera videos is a fundamental task in autonomous driving. However, recent learned methods either rely on costly attention-based networks for global geometry modeling or require complex post-hoc alignment for chunked predictions, limiting their scalability to long multi-view rig sequences. We propose RigRecon, a generalized rig-aware reconstruction framework that enables efficient cross-view interaction in a low-resolution space while asynchronously decoding high-resolution depth maps in a frame-wise manner. Input images are first down-sampled and processed by a transformer backbone to capture coarse global structure, which is then provided to a decoder to reconstruct frame-wise high-resolution depth maps. By fully leveraging calibration and low-resolution feature interaction, RigRecon achieves consistent-scale geometry and accurate pose estimation with significantly reduced memory cost. It achieves state-of-the-art performance on 3D reconstruction, depth estimation, and camera pose estimation, and can efficiently process videos with over 1000 images in a single feedforward pass.

Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional white-box interpretability methods are inapplicable to closed-source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black-box, model-agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI-human semantic alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,289 valid responses across 307 complex natural scenes and 1,306 high-fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an overreliance (relative to humans) on large objects (size bias), objects at the center of the image (center bias), and high saliency objects. In contrast, models rely less on people in the scenes than our human participants to describe the images. A model’s size bias is a primary driver explaining variations in model-human semantic divergence.