NeurIPS 2026 论文列表

We give a computer-assisted counterexample to the open question posed by Rudin, Schapire, and Daubechies in COLT 2012, of whether exhaustive AdaBoost always converges to a finite cycle. The construction is based on a block-product gadget whose two factors share an exact period-2 orbit for their 5-step branch maps, but whose linearized return maps have dominant eigenvalues with an irrational logarithmic ratio. This irrationality forces the burst-winner sequence to have an irrational asymptotic frequency, precluding eventual periodicity. All assertions are certified by exact rational arithmetic in two independent computer algebra systems. We also document the collaborative workflow that led to the construction, in which one model produced the key technical arguments; another model summarized and critiqued different versions of that work, proposing new directions to pursue; and the authors orchestrated the process by selecting directions to prioritize, resolving ambiguities, heavily refining each argument, and verifying the final solution. We present this as one of the first in-depth case studies of LLM-assisted mathematical research in which a long-standing open problem from theoretical machine learning is resolved, and detail the advantages and challenges of such research.

Recovering 4D human-object interaction (HOI) from monocular video is a key step toward scalable 3D content creation, embodied AI, and simulation-based learning. Recent methods can reconstruct temporally coherent human and object trajectories, but these trajectories often remain visual artifacts while failing to preserve stable contact, functional manipulation, or physical plausibility when used as reference motions for humanoid-object simulation. This reveals a fundamental interaction gap: HOI reconstruction should not stop at tracking a human and an object, but should recover the relation that makes their motion a coherent interaction. We introduce **HA-HOI**, a framework for reconstructing physically plausible 4D HOI animation from in-the-wild monocular videos. Instead of treating the human and object as independent entities in an ambiguous monocular 3D space, we propose a \emph{human-first, object-follow} formulation. The human motion is recovered as the interaction anchor, and the object is reconstructed, aligned, and refined relative to the human action. The resulting kinematic trajectory is then projected into a physics-based humanoid-object simulation, where it acts as a teacher trajectory for stable physical rollout. Across benchmark and in-the-wild videos, **HA-HOI** improves human-object alignment, contact consistency, temporal stability, and simulation readiness over prior monocular HOI reconstruction methods. By moving beyond visually plausible trajectory recovery toward physically grounded interaction animation, our work takes a step toward turning general monocular HOI videos into scalable demonstrations for humanoid-object behavior.

The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively: embeddings can be translated across models through a universal latent space without paired data. We ask whether an analogous geometry can be recovered across human brains. Using fMRI data from the Natural Scenes Dataset, we propose a self-supervised encoder that learns subject-specific embeddings from brain data alone by exploiting repeated stimulus presentations. We show that these independently learned spaces can be translated across subjects using unsupervised orthogonal rotations, without paired cross-subject samples or intermediate model representations. Synchronizing pairwise rotations into a single shared latent space further improves cross-subject retrieval, indicating that subject-specific spaces are mutually compatible with a common coordinate system. These results provide evidence for a shared neural geometry in the human visual cortex: subject-specific fMRI representations are approximately isometric across individuals and can be translated through purely geometric transformations.

Protein--nucleic-acid recognition underlies aptamer discovery and nucleic-acid therapeutics, yet computational models often trade mechanistic interpretability for predictive flexibility. Hybrid interpretable--latent architectures risk fallback takeover, where the latent branch silently becomes the true predictor while the explicit pathway remains decorative. We introduce PIGRAM, a patch--motif grammar model that decomposes proteins into local residue patches and nucleic-acid partners into secondary-structure motifs, learning a sign-separated grammar over explicit physicochemical and geometric attributes to preserve rare favorable interactions. Heterogeneous rule families are selectively refined into a coarse-to-fine atlas, and a frozen-grammar residual Transformer provides sparse, bounded corrections without displacing the grammar as the primary predictor. On a strict sequence-pair-disjoint benchmark, PIGRAM achieves a Pearson correlation of 0.494, approaching the unconstrained Transformer (0.511) while exposing signed rules, fine subtypes, and route-gated corrections for each prediction. The learned rules recover bidirectional patch--motif mechanisms, and residual corrections concentrate selectively on grammar-hard regions. In fluorescence ELISA experiments, suppressive-rule relief improved GFP binding, whereas favorable-rule disruption weakened NELF binding, providing direct experimental support for grammar-guided design. Together, these results show that interpretable grammar can serve as both a predictive scaffold and an experimentally actionable design principle for aptamer optimization.

Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction. Recent works proposed protocols have achieved the optimal Heisenberg-limited scaling learning ansatz-free Hamiltonians from their real-time evolutions without fully specifying interaction structures. However, these protocols relies on both deep circuits with interleaving probes and control, and extremely short time resolution, making them difficult to implement on near- and intermediate-term in situ quantum experiments. In this work, we propose a computational efficient, *control-free*, and *ancilla-free* algorithm that uses only *Pauli product state preparation and measurement*, and learns an ansatz-free Hamiltonian $H$ with $||H||\leq\Lambda$ in total evolution time of $\Theta(\tfrac{\Lambda}{\epsilon^2}\log(\tfrac{\Lambda}{\epsilon}))$. The evolution time cost of our algorithm is *optimal* for any control-free protocols as we further prove a lower bound of $\Omega(\tfrac{\Lambda}{\epsilon^2}\log(\tfrac{\Lambda}{\epsilon}))$. Technically, our method introduces a randomized-sampling framework that combines band-limited kernel-based time sampling with a displacement sieve for Hamiltonian structure learning. The characteristic probe time resolution depends only on $\Lambda$ instead of $\varepsilon$, which makes our protocol especially appealing in the high-precision regime for sensing and calibration applications.

Brain–computer interfaces (BCIs) must continually adapt as neural signals drift, yet labeled calibration data is expensive to collect at time of use. Recent systems address this challenge by updating neural decoders using language-model–pseudo-labels, but they treat the pseudo-label as ground truth. We reinterpret recalibration as approximate Bayesian filtering: the decoder parameters are the latent states, and language models provide observations in the form of unnormalized potentials. Guided by this analysis, we introduce \textbf{B-CORP} (Bayesian Continual Online Recalibration with Pseudo-labels). B-CORP replaces hard pseudo-labels with a soft top-$K$ marginalization, increases the number of iterations in the coordinate ascent on the joint likelihood, and models parameter drift with a prior on latent dynamics. Across synthetic drifting regression tasks and real BCI benchmarks, including handwriting and speech BCI datasets, B-CORP attains word error rates consistently under 3\% on a difficult handwriting BCI dataset. B-CORP improves almost 30\% versus the state-of-the-art, while retaining performance even with poor pseudo-labels where naive methods fail. More broadly, our approach connects pseudo-label recalibration with online Bayesian inference, providing a principled foundation for problems requiring long-term recalibration with access to black box predictive models, such as LLMs, for use as strong priors over the labels.

Computer-using agents are increasingly expected to complete realistic user goals rather than isolated GUI actions. However, existing evaluations are fragmented across web, mobile, and desktop settings, making it difficult to measure whether the same agent can sustain long-horizon workflows across heterogeneous software platforms while preserving final-state correctness. We introduce OS-Omni, a 1,200-task cross-platform benchmark and execution environment for evaluating computer-using agents across Windows, Ubuntu, macOS, iOS, Android, and Web environments. OS-Omni provides reproducible initialization, platform-specific adapters, trajectory logging, and executable final-state evaluators, and includes controlled service-backed applications for workflows involving account, cart, budget, calendar, message, and other persistent state. Human studies show that OS-Omni tasks require 67.54 interaction steps, substantially exceeding OSWorld references of 15.0 steps. Throughout our experiments, the strongest evaluated agent, GPT-5.5, reaches 47.1% average success, trailing the 88.1% human reference by 41.0 points; on hard workflows, it drops from 77.8% success on easy tasks to 16.4%, while humans remain at 76.3%. These results indicate that current agents remain far from platform-robust, long-horizon computer use, especially when success requires sustained state tracking and durable final-state changes.

A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning. Existing comparisons, however, often confound the learning objective with architecture, scale, and training data, leaving open whether the objective itself drives alignment. We address this confound using Joint Energy-Based Models (JEMs), which interpolate continuously between discriminative and generative training within a fixed architecture. By varying a single mixing coefficient, we isolate the effect of the learning objective and evaluate the resulting models across six human-alignment benchmarks spanning perceptual similarity, gloss perception, human response uncertainty, robustness, shape–texture cue conflict, and diagnostic feature attribution. Across this diverse suite, human alignment is consistently maximized at intermediate points of the generative–discriminative continuum, rather than at either endpoint. Hybrid JEMs combine the categorical structure induced by discriminative learning with the sensitivity to input structure induced by generative learning, yielding more human-like behavior across multiple levels of vision. These results suggest that the generative–discriminative dichotomy is the wrong axis for understanding human-aligned vision: alignment emerges not from choosing one objective over the other, but from balancing both.

Mutual adaptation is a central challenge in human–AI teaming, as humans naturally adjust their strategies in response to an AI agent's behavior. Existing approaches attempt to approximate human behavior by diversifying training partners; however, these partners are typically static and fail to capture the adaptive nature of human teammates. When agents are trained jointly in standard multi-agent settings, they often converge to opaque coordination strategies that work only with their co-trained partners, leading to poor generalization. To model adaptive human behavior, we formulate human–AI teaming as an Interactive Partially Observable Markov Decision Process (I-POMDP). We propose NestRL, a nested training regime that learns the solution to a finite-level I-POMDP by training agents at each level against adaptive agents from the level below. This exposes agents to adaptive behavior while preventing emergence of opaque coordination strategies. We provide theoretical analysis showing that NestRL agents avoid convergence to partner-specific strategies, and validate this empirically in the Overcooked domain against state-of-the-art baselines. NestRL achieves higher task performance with both unseen adaptive agents and real human teammates, while exhibiting significantly greater adaptability over the course of interaction.

Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical execution. Replicating this seamless combination of capabilities remains elusive in current artificial intelligence and robotic systems. Concurrently, understanding the biological mastery of these movements is hindered because complex muscle coordination is rarely measured in vivo due to the limitations of physical equipment. To bridge this fundamental gap in understanding, $\textit{MyoChallenge}$ at NeurIPS 2025 established a pioneering benchmark for motor control intelligence in sports, leveraging high-fidelity musculoskeletal models within physics simulation combined with machine learning-driven algorithms. The competition introduces two distinct tracks emphasizing either upper or lower limbs control: a table tennis rally task utilizing a biomechanic upper limb composed of an arm with a hand (MyoArm) and a trunk (MyoTorso); and a soccer penalty kick using a biomechanic model of legs (MyoLeg) and a trunk (MyoTorso). Marking the fourth iteration of the MyoChallenge series, this event attracted almost 70 teams and over 560 submissions globally, uniting a diverse community ranging from physicians and neuroscientists to machine learning experts. The competition facilitated the development of several state-of-the-art control algorithms for a musculoskeletal system capable of sports agility, leveraging techniques such as physics-based motion planners, on-policy behaviour cloning, hierarchical planning, and muscle synergies that significantly surpassed our provided baseline performance. By integrating standardized tasks and physiologically realistic models into the open-source framework of MyoSuite, MyoChallenge'25 serves as a reproducible and reusable testbed to accelerate interdisciplinary research across machine learning, biomechanics, sports science, and neuroscience. Project page: https://www.myosuite.org//myochallenge/myochallenge-2025

Humanoid robots capable of versatile whole-body interaction with everyday objects represent a central goal of embodied intelligence. Existing approaches often rely on motion-reference inputs or task-specific rewards, coupling policies to particular motion scripts, object geometries, and contact timings. This limits geometric generalization and flexible interaction control, where simple commands specify motion intent while object geometry determines how contact should be executed. We introduce LessMimic, a framework for versatile motion-free-at-inference humanoid–object interaction. At inference, a single whole-body policy is steered by root commands and an interaction-type flag instead of motion-reference inputs, and is conditioned on a compact interaction representation built from short histories of distance-field-derived surface distances and local surface directions. This geometry-conditioned interface enables command-steered control while adapting contact behavior to local object geometry. Through visual distillation, LessMimic further enables egocentric-depth-based deployment for MoCap-free sensing. Across object scales from 0.4× to 1.6×, LessMimic maintains robust performance over four interaction tasks, including PickUp, SitStand, Push, and Carry, where motion-conditioned baselines degrade sharply away from the training scale. Beyond single-task evaluation, LessMimic further supports sequential composition of heterogeneous interaction skills, attaining 62.1% success on five-task sequences and retaining 23.5% success at 15 task instances. By grounding interaction control in local geometry rather than motion references, LessMimic provides a path toward geometry-generalizable, command-steerable humanoid–object control with heterogeneous skill composition.

Understanding human behavior while interacting with the surrounding world is crucial for many applications of embodied AI. First-person videos are particularly informative for this problem, as they well capture how activities reshape the scene over time. However, existing approaches often rely on implicit visual or language-aligned representations, disregarding structured reasoning over the scene dynamic. In this paper we argue that explicit, compositional and editable representations of human-environment interactions can play a crucial role for rich grounded activity understanding. To this end, we introduce SG-Ego, a large scale annotation set extending Ego4D with spatio-temporal scene graphs, where relations triplets are consolidated over time into explicit time-evolving descriptions of the scene state. To effectively reason over this representation, we propose GLEN, a graph-based model that operates over scene graph sequences to both align them with textual actions and model their temporal evolution. In addition, we formulate the activity-driven graph-edit forecasting (A-GEF) problem, a novel task that casts scene dynamics as a sequence of structured transformations conditioned on ongoing actions, enabling explicit reasoning about how scenes change over time. We demonstrate the effectiveness of our approach across multiple downstream tasks, spanning retrieval benchmarks as EgoMCQ and the more complex compositional EgoCVR, as well as long-horizon reasoning benchmarks as EXPLORE-Bench and the newly introduced A-GEF. GLEN achieves competitive or state-of-the-art performance compared to raw video baselines and it excels in reasoning settings, typically addressed only with MLLMs, while enabling controllable and structured predictions of scene dynamics driven by human activities. We believe our results establish spatio-temporal scene graphs, together with models that reason over them, as strong compositional and interpretable representations for egocentric video understanding and potentially beyond. Code will be released upon acceptance.

Intracortical brain–computer interfaces (iBCIs) can restore movement and communication, but neural drift can degrade performance across recording sessions. Because recalibration adds user burden, there is a need for decoders that adapt from minimal calibration data. Although session-specific input layers are a common way to handle cross-session drift, they can underperform in the extreme few-shot regime because the shared decoder is trained downstream of well-estimated session-specific layers, but must rely at deployment on a new-session layer fit from only a few calibration trials. To avoid this dependence, we propose a two-stage approach: first, train a single GRU decoder on pooled held-in sessions without session-specific input layers, encouraging the backbone to handle cross-session variability directly; second, freeze this backbone and meta-learn a lightweight alignment layer for rapid calibration. We evaluate on FALCON H2, an official human handwriting iBCI benchmark designed for few-shot cross-session decoding, and achieve 7.41\% word error rate using only three released calibration sentences per held-out session. These results suggest that, under extreme calibration limits, learning a transferable backbone before adding session-specific adaptation can be more effective than jointly training flexible session-specific layers.

Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding, human motion, and embodiment-specific action. We introduce MoT-HRA, a hierarchical vision-language-action framework that learns human-intention priors from large-scale human demonstrations. We first curate HA-2.2M, a 2.2M-episode action-language dataset reconstructed from heterogeneous human videos through hand-centric filtering, spatial reconstruction, temporal segmentation, and language alignment. On top of this dataset, MoT-HRA factorizes manipulation into three coupled experts: a vision-language expert predicts an embodiment-agnostic 3D trajectory, an intention expert models MANO-style hand motion as a latent human-motion prior, and a fine expert maps the intention-aware representation to robot action chunks. A shared-attention trunk and read-only key-value transfer allow downstream control to use human priors while limiting interference with upstream representations. Experiments on hand motion generation, simulated manipulation, and real-world robot tasks show that MoT-HRA improves motion plausibility and robust control under distribution shift.

We present a system for learning generalizable hand-object tracking controllers purely from synthetic data, without requiring any human demonstrations. Our approach makes two key contributions: (1) HOP, a Hand-Object Planner, which can synthesize diverse hand-object trajectories; and (2) HOT, a Hand-Object Tracker that bridges synthetic-to-physical transfer through reinforcement learning and interaction imitation learning, delivering a generalizable controller conditioned on target hand-object states. Our method extends to diverse object shapes and hand morphologies. Through extensive evaluations, we show that our approach enables dexterous hands to track challenging, long-horizon sequences including object re-arrangement and agile in-hand reorientation. These results represent a significant step toward scalable foundation controllers for manipulation that can learn entirely from synthetic data, breaking the data bottleneck that has long constrained progress in dexterous manipulation.

The emergence of large language models (LMs) as versatile problem-solving systems marks a paradigm shift comparable to the advent of digital computers. Traditional computer science provides theoretical foundations for understanding computational processes through complexity theory, algorithms, and formal methods, this position paper argues that language models require their own distinct theoretical framework, termed as Computer Science of Language Models (**CSLM**). We identify seven fundamental differences between traditional computing and language model: 1) the generating-solving-verifying triad versus the solving-verifying dichotomy, 2) token-based rather than operation-based computation, 3) probabilistic reliability with hallucinations rather than deterministic correctness, 4) native multimodal understanding beyond pixel-level processing, 5) general-purpose capabilities versus problem-specific algorithms, 6) essential two-stage training-inference processes, and 7) inherent knowledge. We argue these differences necessitate new theoretical frameworks, complexity measures, and evaluation methodologies. Then, we define the **token complexity** as the core concept in CSLM and discuss connections to existing research in scaling laws, memory systems, tool use, and fine-tuning, while identifying critical open questions about LM-complete problems, reliability bounds, emergence phenomena, and multimodal complexity. Drawing parallels to complexity theory's 50-year development, we present a 5-year research roadmap (2026-2030) to build the mature CSLM. CSLM promises to transform LM development from empirical scaling to principled engineering grounded in rigorous theory and strengthen our understanding about both the problems, LMs and their relationships.

Recent advances in robotics foundation models have shown promising multi-task generalization, yet their reliance on specialized embodiment-specific data and model fundamentally prevents them from exploiting the far richer physical interaction knowledge present in abundant human manipulation videos. Existing approaches that leverage human videos suffer from misaligned representations that preclude explicit unified interaction modeling over human video and robot demonstration, failing to capture unified physical interaction dynamics essential for cross-domain transfer. We address this by constructing a unified representation that aligns human videos and robot tasks through scene point clouds with hand-to-gripper mapping via spatial tracking and hand pose estimation. Leveraging this representation, we propose a structured graph transformer that explicitly models spatial, semantic, and intentional interaction entities through graph attention mechanisms. The modeled multi-level interaction features then enable interaction-aligned cross-domain learning, transferring manipulation knowledge from human videos to robot tasks via discrete interaction abstraction and hierarchical distribution alignment. Experiments on RLBench, ManiSkill2, and real-world robot demonstrate state-of-the-art performance on diverse benchmarks with fewer demonstrations and effectiveness of our designed modules. Remarkably, our method achieves zero-shot transfer on robot tasks when trained exclusively on human video, providing strong evidence for effective human-to-robot transfer through interaction alignment.

Recognizing human value preferences is crucial for building reliable AI agents in user-centric fields like psychological counseling and strategic negotiation. Existing benchmarks rely on explicit questionnaires, assuming (1) users are always cooperative and willing to self-report; (2) value preferences remain static. However, humans may conceal true preferences if they feel probed, and may adopt distinct value preferences depending on the context. Consequently, explicit queries are insufficient for predicting value preferences reliably. To address this, we introduce Implicit Value Probing (IVP), a benchmark simulating realistic scenarios where agents implicitly infer value preferences through strategic, multi-turn conversations. IVP grounds these interactions using a diverse set of Persona Agents, covering a wide spectrum of personalities and values. We evaluate mainstream LLMs on IVP, including Qwen3, DeepSeek, Doubao, and GPT-5, observing substantial performance gaps. We further develop Value-Prober-8B, a specialized model internalizing structured cognitive reasoning via distillation. It employs strategic questioning to elicit concealed value preferences. Experimental results demonstrate that Value-Prober-8B can effectively balance probing accuracy with social acceptability, achieving capabilities comparable to frontier models.

Modern AI agents can plan, reflect, reason, and act over long-horizon digital tasks. Even so, they cannot plan to steer a social interaction in real time. Doing so requires anticipating how latent factors like trust and resistance will evolve under the agent's actions, fast enough to plan within a conversational turn. Generative world models approach this by narrating possible futures, but autoregressive text generation is both too slow for real-time planning and fundamentally lossy. Representation-predictive methods can be superior, but are underexplored for interaction. We build LID-Bench, the first controlled testbed with oracle latent states for interaction dynamics, enabling systematic comparison of generative and representation-predictive world models against known dynamics. A bottleneck decomposition on three generative models spanning 117M--350M parameters reveals that all learn interaction dynamics internally, with text-rendering signal losses of 89--100\% observed by horizon $k{=}5$. Our representation-predictive model (Social-JEPA, 125M params, 500K trainable) bypasses text entirely, forecasting latent dynamics $2.8$--$3.9\times$ more accurately while performing rollouts $1{,}059$--$2{,}314\times$ faster---making real-time planning within conversational turn-taking latencies feasible.