NeurIPS 2026 论文列表

Reconstructing physical dynamics from sparse and irregular spatiotemporal observations is a fundamental challenge in scientific research. However, sparsity leaves large regions of the field unconstrained, making the inverse problem ill-posed: a model may fit the observed sensors while producing nonphysical off-sensor structures or unreliable predictions between support times. To address this challenge, we propose PhysFTM---a physics-informed functional Tucker method that parameterizes Tucker mode factors in reproducing kernel Hilbert space (RKHS) and enforces governing physical laws through finite-difference residuals at collocation points. These physical constraints are essential for credible reconstruction, since most query locations are never directly supervised by data. Specifically, PhysFTM first reconstructs continuous spatial fields at observed times via an alternating minimization scheme, with separate treatments for the Tucker core tensor and the RKHS-based factors. To further enable continuous temporal resolution, PhysFTM constructs a continuous trajectory in Tucker-core space, which is decoded by the learned spatial RKHS-FTM representation to recover the full spatiotemporal field at arbitrary query times. Experiments on Allen--Cahn and Navier--Stokes with varying observation ratios demonstrate that PhysFTM achieves superior reconstruction accuracy, improved physical consistency, and stronger continuous-time modeling capability.

Video diffusion models can generate realistic process videos, but using them as controllable surrogates for industrial systems remains difficult when key control variables are only partially observable and no simulator or differentiable physics model is available. We study this regime in inertial confinement fusion (ICF) target capsule polishing, where surface defects can degrade fusion yield. The dynamics are governed by the polishing speed $\omega_p$ and capsule-pad slip $S_C$. While capsule motion can be monitored from video, $S_C$ is only indirectly observable. Using only an analytical kinematic model, we adapt a frozen Stable Video Diffusion (SVD-xt) backbone via LoRA and condition the generation on Fourier-encoded physics parameters $(\omega_p, S_C)$. Two training objectives operate on latent frame-difference dynamics: (i) a batch level correlation loss aligning them with the analytical sliding speed, and (ii) a stratified ratio-matching loss that, conditioned on $\omega_p$, isolates slip structure from the dominant $\omega_p$-driven variance. We evaluate physics consistency by tracking capsule motion in generated videos, inverting the kinematic model, and measuring rank agreement under a physical validity gate. For speed, rank accuracy exceeds $0.97$ on real data; for slip, it reaches $0.735$ with Spearman $\ge 0.50$ on synthetic data. Overall, the results suggest a practical recipe for controllable industrial video surrogates when only low-dimensional analytical kinematics are available.

Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map prediction without running expensive high-fidelity simulations for every scene. However, generating high-quality training labels at scale is also difficult: the affordable labels come from finite-ray simulations, which are richer than low-fidelity inputs but carry residual Monte Carlo noise. We address this challenge with Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images. We prove that, under conditionally unbiased label noise, the model can learn stable propagation structure and outperform its own training labels. Experiments across diverse floorplans show that PU-HNO outperforms image-to-image baselines, wireless learning models, and monolithic neural operators across both image-quality and wireless deployment metrics.

Physical AI Smart Spaces is, to the best of our knowledge, the first benchmark to simultaneously provide large-scale, multi-class, and multi-camera 3D perception data for indoor smart spaces. It contains over 280 hours of synchronized 1080p footage captured by nearly 1,800 cameras in warehouses, hospitals, retail venues, and similar settings, together with automatic annotations for multi-camera identities, 2D bounding boxes, 3D bounding boxes, camera calibration, and depth where available. The benchmark spans Isaac Sim synthetic generation, Cosmos Transfer appearance augmentation, and real-world Sim2Real evaluation. For the real-world target, we include two warehouse deployments with time-synchronized streams, automatic VGGT-based calibration, and a 3D labeling interface that projects world-frame 3D boxes into each view for cross-camera verification. We describe the dataset scope, annotation and calibration schema, generation workflow, benchmark protocols, and official evaluation system, which standardizes submission format, and leaderboard reporting. A central contribution is a 3D instantiation of Higher Order Tracking Accuracy (HOTA), extending the usual 2D box-based tracking evaluation to 3D locations and 3D boxes. We further report empirical baselines from the AI City Challenge leaderboards, showing how methods evolve from person-only 3D location tracking to multi-class 3D box tracking under realistic smart-space constraints. The release is available at https://huggingface.co/datasets/nvidia/PhysicalAI-SmartSpaces.

Vision-Language Models (VLMs) have demonstrated impressive multimodal reasoning, yet their ability to truly internalize the underlying physical consistency of real-world dynamics remains an open question. Existing benchmark paradigms often suffer from fragmented evaluation, focusing on isolated cognitive stages while overlooking the inherent synergy between perception, reasoning, and physical judgment. The lack of a holistic perspective limits the ability to diagnose whether VLMs can reliably evaluate the physical authenticity of emerging generative models. To address these issues, we introduce \textbf{PhysVista}, a benchmark designed to evaluate physical intelligence in VLMs through a closed cognitive loop framework inspired by the human \textbf{seeing–reasoning–assessment} process. PhysVista restores this loop by jointly evaluating physical state perception, physical dynamics reasoning, and physical plausibility assessment. It further distinguishes \textbf{event-level} reasoning and \textbf{scale-level} reasoning to enable fine-grained analysis of physical understanding. In addition, PhysVista incorporates both real-world and AI-generated videos, allowing evaluation across diverse domains and emerging generative scenarios. Extensive experiments on a wide range of state-of-the-art VLMs reveal significant limitations in current models’ physical intelligence, particularly in fine-grained reasoning and plausibility assessment. Our findings highlight critical gaps between visual recognition and genuine physical understanding, and provide insights for developing future physically grounded VLMs.

Realistic physical interaction is a cornerstone of embodied intelligence, yet obtaining high-fidelity tactile data remains significantly more expensive than visual data. This scarcity necessitates Visual-to-Tactile synthesis to bridge the Sim-to-Real gap. However, existing data-driven approaches often treat this as a naive image-to-image translation task, neglecting fundamental contact mechanics. Moreover, the inherent spatial misalignment in real-world visual-tactile datasets frequently forces generative models to average out spatial uncertainties, resulting in blurry and textureless outputs. To address these limitations, we introduce PhysTacGen, a physics-aware generation framework that enforces consistency between visual appearance and tactile mechanics. Our system makes three technical contributions. Firstly, we propose Group Tactile Policy Optimization (GTPO), a reinforcement learning alignment strategy that refines Large Multimodal Models to infer latent physical properties by rewarding physics-consistent reasoning chains. Secondly, we introduce a Semantic-Driven Data Curation and Visual Prior pipeline, leveraging DINOv2 to filter spatially misaligned data and extracting pure depth maps to decouple macro-geometry from surface texture. Finally, we present a physics-driven generative architecture based on Stable Diffusion XL (SDXL). It synthesizes tactile images via a ControlNet conditioned on GTPO-generated physical descriptions and depth-augmented visual priors. Extensive experiments demonstrate that PhysTacGen achieves state-of-the-art perceptual quality and significantly enhances grasp force prediction for zero-shot Sim-to-Real transfer.

A critical observability gap arises in multiscale chaotic systems when coarse observations smooth out high-frequency extremes. For tropical cyclones, this makes standard reanalysis products insufficient for high-precision intensity prediction, as spectral truncation can mask peak winds by over $30\%$. In this work, we introduce Physics-enhanced Tropical Cyclone forecasting (PhysTC), a physics-enhanced benchmark framework for forecasting tropical cyclone track and intensity from globally consistent coarse observations. We curate a harmonized multi-basin dataset spanning 1950--2023, incorporating scale-robust physical predictors to mitigate resolution-induced bias. Building on this dataset, we propose the Physics-enhanced Tropical Cyclone Network (PhysTCN), a model that integrates synoptic spatial structure, storm-following temporal history, and physical consistency constraints to infer intensity-relevant dynamics beyond direct grid-space readout. PhysTCN significantly outperforms state-of-the-art baselines, with particularly strong gains in recovering extreme wind speeds that are underestimated in reanalysis data. This work provides a scalable framework for physically guided forecasting in data-sparse, cross-scale dynamical systems.

While diffusion models have revolutionized image generation and editing, achieving physically realistic object removal remains a challenge. Physically realistic object removal posits that if an object ceases to exist, all its directly associated transient physical effects must also vanish. However, existing methods either confine the removal strictly to the masked region or limit "side effects" to simple shadows and reflections, ignoring complex interactions like caustics, emitted light, and volumetric media. Furthermore, they struggle with transparent objects, destroying the underlying background semantics. To bridge these gaps, we formalize the task of physically realistic object removal and propose PhysRemover, a unified framework addressing both the elimination of objects with their physical effects and the handling of transparent materials. Specifically, we incorporate an In-Context Contrastive Guidance mechanism guided by both masked and unmasked image, coupled with a Learnable Removal Trigger. This design effectively resolves the conflict between erasing the object and its physical side effects, and preserving the underlying background. Furthermore, we introduce PhyTrace, a large-scale image dataset capturing diverse physical effects, along with two benchmarks tailored for the proposed task of physically realistic object removal. Additionally, we develop a novel metric, PhysRM, for evaluating object removal quality, establishing a new standard for physically realistic image editing. Extensive experiments demonstrate that PhysRemover effectively eliminates objects and their physical side effects while faithfully preserving the background when removing transparent objects, outperforming existing methods in these challenging settings.

Manipulation planners depend on accurate predicates about physical state---is an object occluded? is a container open? is a path blocked?---but such predicates are hard to read off an image. Contrastively-trained vision--language models capture semantic content but collapse to near-zero binary F1 on these predicates, and even GPT-4o few-shot falls below random on multiple-choice physical-state questions. We present \ours, which predicts physical-state scene graphs from a single image and a natural-language goal. A frozen CLIP ViT, adapted with LoRA, feeds a heterogeneous graph of object, relation, and learnable memory tokens into a Latent Graph Reasoning Transformer (\lgrt); training combines supervised, counterfactual-margin, contrastive, and calibration losses with offline counterfactual-pair augmentation. On \pacbench, \ours reaches $\mu$-F1$\,{=}\,0.651$ and $99.3\%$ MCQ accuracy, a $+0.170$ absolute $\mu$-F1 gain over the strongest no-graph supervised baseline, with $99.6\%$ counterfactual directional consistency. Component ablations attribute $+0.426$ $\mu$-F1 to the \lgrt and $+0.392$ to the counterfactual loss. A \manipbench-trained variant scores $74.4\%$ MCQ against $30.0\%$ for the best zero-shot VLM on that benchmark's 4-choice format.

We present PhysFormer, a physics-grounded diffusion transformer for generating 4D multi-object mesh dynamics directly in world coordinates. Rather than predicting future frames in pixel space or rolling out next-step system states autoregressively, PhysFormer models physical evolution as full-trajectory coordinate diffusion. Given initial per-vertex positions, velocities, and material conditions, it generates future mesh vertex trajectories in a single denoising process, producing physically plausible object-object and object-environment interactions without hard-coded constraints, simulator priors, or learned shape latents. PhysFormer adopts a DiT-style backbone with factorized temporal, spatial, and object-level attention to capture coherent structure across time, vertices, and objects. Trained on over 100k collision-rich simulated trajectories, it models rigid and deformable multi-object dynamics, generalizes to unseen real-world geometries and larger object counts, and substantially outperforms autoregressive baselines in trajectory accuracy, rigidity preservation, and momentum-based physical consistency. Our results position coordinate-space diffusion as a promising step toward view-invariant, geometry-level world models for robotics, graphics, and physical design.

Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present \textsc{PhysElite}, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. \textsc{PhysElite} contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese--English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7\% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. The benchmark results and dataset will be publicly available.

Modern dehazing networks are often computationally expensive and overfit to synthetic haze, limiting real-time deployment. This underscores an urgent demand for efficient, high-fidelity restoration methods that can operate on resource-constrained edge devices in practical, dynamic environments. We introduce PhysDNet, a lightweight multimodal architecture that fuses NIR imagery with sparse LiDAR depth and is trained with a physics-constrained dual-head objective. Differentiating through a fixed Koschmieder inversion induces density-adaptive gradient amplification, acting as an implicit resource allocation mechanism that concentrates learning on heavily degraded regions without increasing inference cost. PhysDNet delivers superior restoration with minimal capacity and enhanced training efficiency, facilitating real-time deployment on edge devices without increasing inference overhead. On the Seeing Through Fog benchmark, PhysDNet outperforms competitors by 1.67 dB at 5.2x lower compute and recovering 54.9% of fog-induced detection loss. Ablations and cross-domain evaluations show that physics-guided training improves generalization including under non-homogeneous haze that violates the Koschmieder assumption. PhysDNet further demonstrates compatibility with fixed-function NPUs, running at 29/45/99 FPS at <4W across L/M/S variants. Code and pre-trained models will be made publicly available.

Code-reasoning agents trained from rollouts typically rely on costly verifiers — expert annotation, hand-written unit tests, or learned reward models — each of which scales poorly across domains. We argue that in domains with closed-form physical laws, the laws themselves form a free, dense, automatic class of reward signals. We instantiate this for physics via PhysAgentGym, an agentic code- execution gym in which a language model receives a physics scenario, writes Python that simulates it, and emits a trajectory scored by a rule-based verifier that compiles natural-language standards into executable trajectory predicates. Verifier signals are label-free: physical laws supply the ground truth without any per-task annotation. Across four physics subcategories totaling 1,320 closed-form- simulator task instances, two frontier models, Claude Sonnet 4.5 and OpenAI GPT-5, achieve nearly-identical aggregate scores (0.944 and 0.945 at N=30 each), with per-cell agreement within 0.5 pp, providing independent evidence the verifier is well-calibrated rather than arbitrary. We filter 491 successful frontier trajectories on two in-distribution subcategories into an SFT corpus and train Qwen-2.5-Coder {1.5B, 3B, 7B} with QLoRA. The trained 7B adapter reaches mean = 0.942 on the four-subcategory test set, matching both frontier models within 0.3 pp on aggregate at approximately 50×lower inference cost. It scores identically to both Sonnet and GPT-5 on all three in-distribution subcategories and ties them within 0.8 pp on out-of-distribution Damping, despite never seeing damping data in training. We further show that combined sparse-and-dense reward filtering beats sparse-only filtering by 1.4–8.7 pp, that smaller models exhibit fundamental seed brittleness which disappears at 3B and above, and that scale enables the OOD generalization that 1.5B and 3B do not provide. The method is portable to any domain admitting closed-form physical laws

We introduce PhyTS, a benchmark suite of precision scientific time series datasets for machine learning, spanning experiments in gravitational-wave detection, dark matter searches, neutrino mass determination, and exoplanet discovery. Despite their diverse scientific goals, these domains share a common challenge: recovering weak, structured signals and estimating underlying physical parameters from noise-dominated measurements. Unlike standard sequence modeling benchmarks such as audio and speech, these data exhibit non-Gaussian and nonstationary noise, long-range temporal correlations, detector-specific systematics, irregular sampling, and signals that are sparse, weak, or only partially modeled. As a result, they provide a challenging testbed for evaluating whether modern AI methods can support downstream scientific inference. We provide standardized tasks, data splits, and evaluation protocols for denoising, signal recovery, and parameter inference across physics domains, along with baseline results. By unifying diverse weak-signal inference problems under a common framework, this benchmark aims to enable reproducible evaluation and accelerate the development of more robust, interpretable, and physically grounded methods for scientific time series analysis.

Physics-based digital twins aim to predict the dynamics of real-world objects under interaction, enabling real-to-sim-to-real applications in robotics. Current approaches reconstruct such twins as explicit physical models (such as spring-mass systems) to predict the dynamics, but the resulting models often inherit the resolution of the visual reconstruction rather than being reduced to the physical complexity required to reproduce task-relevant dynamics. This mismatch introduces redundant topology, making repeated forward-dynamics rollouts unnecessarily expensive. To address this challenge, we present PhySPRING, an fully differentiable GNN-based method to reduce complexity in spring--mass digital twins. PhySPRING jointly learns a hierarchy of coarsened graph topologies and their mechanical parameters from observations. At each reduction level, PhySPRING merges nodes with similar learned dynamic responses to optimize the topology, while maintaining every reduced layer as an explicit spring--mass system. On the PhysTwin benchmark, PhySPRING improves dense reconstruction and prediction accuracy over PhysTwin, while reduced models retain stable physical and visual fidelity with up to a 2.30$\times$ speed-up. We further demonstrate the effectiveness of PhySPRING in a Real2Sim robot policy-evaluation pipeline, where the reduced models are substituted zero-shot into ACT and $\pi_0$ evaluations, maintaining comparable manipulation success rates across downsampling levels while improving action-sampling effectiveness. Together, PhySPRING enables efficient and structure-preserving spring--mass reduction without sacrificing fidelity or robotic utility.

Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language models, which can often be myopic to physical dynamics, or fine-tuned evaluators trained on human annotations, which overfit to dataset-specific cues and fail to generalize. A key challenge is that existing supervision sources provide either relative ordering or absolute scores, but not both reliably and consistently across varied settings. To this end, we introduce PhyProbe, an evaluator that extracts features from a frozen pretrained spatio-temporal encoder and maps them to a scalar physical consistency violation score via a lightweight scoring head. PhyProbe is trained through a unified objective combining pairwise ranking, regression on noisy scalar annotations, and anchor-based calibration over a curated set of heterogeneous supervision sources. Experiments show that PhyProbe significantly outperforms prior methods on pairwise benchmarks spanning real–generated and generated–generated pairs under varying correspondence, including challenging settings where existing approaches degrade toward random performance. PhyProbe achieves strong correlation with human judgments, with close agreement between rank-based and linear metrics, indicating both reliable ordering and calibrated scores. Further, despite being trained solely for physical consistency, PhyProbe also performs competitively on human preference benchmarks: consistent with the observation that physics violations are entangled with broader quality degradations.

Generating realistic human motion is a central yet unsolved challenge in video generation. Reinforcement learning (RL)-based post-training has emerged as a promising direction, yet its success critically depends on the quality of the reward signal. Existing video rewards primarily rely on 2D perceptual signals, without explicitly modeling the 3D body state, contact, and dynamics underlying articulated human motion, and often assign high scores to videos with floating bodies or physically implausible movements. To address this, we propose PhyMotion, a structured, fine-grained motion reward that grounds recovered 3D human trajectories in a physics simulator and evaluates motion quality along multiple dimensions of physical feasibility. Concretely, we recover SMPL body meshes from generated videos, retarget them onto a humanoid in the MuJoCo physics simulator, and evaluate the resulting motion along three axes: kinematic plausibility, contact and balance consistency, and dynamic feasibility. Each component provides a continuous and interpretable signal tied to a specific aspect of motion quality, allowing the reward to capture which aspects of motion are physically correct or violated. Experiments show that PhyMotion achieves stronger correlation with human judgments than existing reward formulations. When used for RL-based post-training, it consistently improves motion realism across both autoregressive and bidirectional video generators under both automatic metrics and blind human evaluation. Ablations further show that the three axes provide complementary supervision signals, while the reward preserves overall video generation quality with only modest training overhead.

Recovering dynamic 3D scene geometry, appearance, and the physical motion states that govern scene evolution from multi-view videos is important yet challenging. A key difficulty is that appearance can be deceptive. When trained primarily with rendering supervision, existing methods may fit the observed appearance well by exploiting appearance shortcuts, rather than recovering the true evolving geometry and motion. This issue is especially severe in scenes with fast or locally complex motion, where inaccurate trajectories can still produce plausible images over the observed frames, but lead to unreliable reconstruction and discontinuous future prediction. In this paper, we propose PhyMo, a framework for learning evolving dynamics from multi-view videos. We introduce a Motion-Aware Trajectory Representation that drives the model to recover accurate motion from deformation, rather than relying on appearance shortcuts to explain observed motion. On top of this, we propose a Continuous Motion Evolution module with bidirectional kinematic continuity constraint to promote temporally consistent evolution of displacement, velocity, and acceleration. Together, these designs yield geometrically faithful interpolation and continuous motion extrapolation. Experiments on four dynamic scene benchmarks demonstrate state-of-the-art performance.

Recently, Muon and related spectral optimizers have demonstrated strong empirical performance as scalable stochastic methods, often outperforming Adam. Yet their behaviour remains poorly understood. We analyze stochastic spectral optimizers, including Muon, on a high-dimensional matrix-valued least squares problem. We derive explicit deterministic dynamics that provide a tractable framework for studying learning behaviour with a focus on (stochastic) SignSVD and (stochastic) SignSGD, the latter serving as a proxy for Adam. Our analysis shows that for large batch size, SignSVD performs a square-root preconditioning with respect to the data covariance spectrum, while for small batch size smaller eigenmodes behave like SGD, slowing down convergence. We contrast with SignSGD which for generic covariance performs no preconditioning and has no transition, leading to different optimal learning rates and convergence characteristics. The two methods match up to a constant factor with isotropic data, but behave differently with anisotropic data. An analysis of a power law covariance model with data exponent $\alpha$ and target exponent $\beta$, and show there are three phases in the $(\alpha,\beta)$ plane: one where SignSGD is uniformly favored, one where SignSVD is uniformly favored, and a third where the two methods exhibit a trade-off in performance.