NeurIPS 2026 Papers

Skin-friction drag induced by wall-bounded turbulent flows accounts for a substantial fraction of energy consumption across commercial aerospace, wind energy, and marine transport. Its active reduction is one of the highest-value targets in engineering fluid dynamics. Deep reinforcement learning (DRL) has emerged as the leading approach for real-time flow control, yet its performance ceiling is set not by algorithmic capability but by reward structure, the naive scalar objective does not optimally reflect the underlying physics. Policy-DRIFT bypasses this ceiling by relocating reward information from policy gradients to generative model inference: a conditional flow matching model (CFM) constructs a physically-grounded manifold of realisable flow states spanning multiple control regimes, Terminal Reward Guidance (TRG) steers samples toward reward-maximising targets at inference, and a lightweight DRL policy, structurally decoupled from reward quality, tracks these full-field targets via root-mean-squared error (RMSE) minimisation. The test case is turbulent channel flow simulated using direct numerical simulation (DNS) at friction Reynolds number of $\mathrm{Re}_\\tau = 180$, which is the canonical benchmark for wall-bounded turbulence. Policy-DRIFT achieves $49\\%$ drag reduction approaching the theoretical upper bound, which is $\\approx 16\\%$ higher than the DRL benchmark, while consuming 37$\\times$ less actuation energy. Our approach combines generative methods with active flow control, marking a paradigm shift towards controlling complex physical systems efficiently.

Polarization imaging provides rich physical cues beyond RGB imaging, yet acquiring such information typically requires specialized polarization-sensitive hardware that increases system cost and deployment complexity. Recent methods have proposed to infer polarization information directly from standard RGB images, providing a hardware-free alternative. While useful for describing relative polarimetric structure, these representations do not fully assess whether estimated images preserve the radiometric scale and intensity-dependent physical information needed for full Stokes reconstruction. To address this limitation, we introduce PolarScale, a physics-grounded benchmark for radiometrically consistent RGB-to-Stokes estimation. Instead of treating polarization inference from RGB images as conventional image-to-image translation, PolarScale evaluates how scale-independent descriptors, scale-dependent Stokes components, and radiometric scale jointly support physically meaningful Stokes reconstruction. Building upon this formulation, we propose three prediction strategies, namely direct, joint, and decoupled prediction, to study how scale-independent and scale-dependent polarimetric cues should be modeled across representative restoration-based and generative frameworks. Our analysis reveals that restoration-based frameworks outperform generative ones for radiometrically consistent polarization estimation (e.g., 28.61 vs. 26.14 dB PSNR). Furthermore, we find that jointly modeling scale-independent and scale-dependent parameters yields more stable physical representations than direct scale-dependent prediction (e.g., 28.61 vs. 26.81 dB PSNR). Overall, PolarScale provides a physics-grounded benchmark foundation for evaluating whether vision models can recover radiometrically consistent polarimetric signals from standard RGB images.

In many real-world applications, it is undesirable to drastically change the problem solution after a small perturbation in the input as unstable outputs can lead to costly transaction fees, privacy and security concerns, reduced user trust, and lack of replicability. Despite the widespread application of graph algorithms, many classical algorithms are not robust to small input disturbances. Towards addressing this issue, we study the pointwise Lipschitz continuity of graph algorithms, a notion of stability introduced by Kumabe and Yoshida (2023, FOCS’23) and further studied in related settings (Kumabe and Yoshida, 2024, ICALP’24), (Kumabe and Yoshida, 2025, SODA’25), (Gima et al., 2025, ESA’25). Our main result is a linear programming (LP) based minimum S-T cut algorithm with a provably optimal Lipschitz constant, as witnessed by an accompanying lower bound. As a direct corollary, we give the first dynamic minimum S-T cut algorithm with non-trivial recourse bound. At the core of our techniques is a novel framework for analyzing the Lipschitz constant of regularized LP relaxations. Our framework crucially unlocks the use of weighted regularizers, which could not be analyzed through previous methods and leads to polynomial improvements in the Lipschitz constant compared to what is achievable through previous techniques. To demonstrate the flexibility of our methods, we also design an LP-based b-matching algorithm that improves on the state-of- the-art Lipschitz constant in certain input regimes when b ≡ 1. Moreover, our algorithm cleanly extends to the general case when b ≥ 1, whereas prior works are specialized to the case of b ≡ 1.

The latest advances of diffusion models in biomolecular structure prediction are still based on distance-based supervised training without adequately accounting for physically plausible interactions needed for real-world drug discovery. We introduce $\mathit{\boldsymbol{\pi}\text{-Fold}}$, a novel physics-informed policy-based folding approach that augments reinforcement learning with physics-aware scoring functions for training biomolecular diffusion models. Specifically, we fine-tune AlphaFold3-based models using Group Relative Policy Optimization and explicit physics-based rewards such as Rosetta all-atom energy, AutoDock Vina binding affinity, and ligand strain energy. Crucially, we demonstrate that these physics-based priors translate to enhanced performance on critical downstream prediction tasks. $\mathit{\boldsymbol{\pi}\text{-Fold}}$ achieves state-of-the-art results on SKEMPI for protein--protein mutation effects, CASP16 and OpenFE for protein--ligand binding affinities, Run-N-Poses for ligand geometric validity and interface quality across diverse structural fidelity metrics. On FoldBench, $\mathit{\boldsymbol{\pi}\text{-Fold}}$ achieves the best overall MolProbity score of 1.30, halving steric clashes and nearly eliminating rotamer outliers. Human evaluation further confirms that $\mathit{\boldsymbol{\pi}\text{-Fold}}$ generates structures that are meaningful for downstream drug discovery applications. Our code is available at \href{https://anonymous.4open.science/r/pifold-45B8/README.md}{github.com/anonymous/pifold}.

We propose a physics-informed method for solving high-dimensional optimal control problems that requires only control labels, not value function labels. A neural network parameterizes the value function, and the control policy is derived from its gradient. The network is trained by minimizing a weighted sum of the standard Hamilton–Jacobi–Bellman (HJB) residual and the mismatch between the network’s control output and control labels, which are precomputed by a state-dependent Riccati equation (SDRE) solver. Theoretically, with the help of the viscous HJB equation, we establish error bounds for the value function and its gradient, providing a rigorous performance guarantee for the resulting policy. Numerical experiments show that the learned model accurately approximates the optimal value function, closely matches the cost-to-go of the SDRE reference, and yields a neural controller that is significantly faster while exhibiting excellent stability.

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.

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.

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

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.

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.

Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and generalized to new tasks. While foundational for image and video generation, the application of tokens to physical simulation remains nascent. Because existing tokenizers are designed for the perceptual requirements of natural images, they struggle with scientific data, which exhibits large dynamic ranges and requires exact preservation of physical and spectral properties. In this work, we investigate the performance of a suite of image tokenizers across metrics designed to measure PDE fidelity. Observing that these baselines struggle to simultaneously capture fine geometric details and precise physical magnitudes, we propose Phaedra, a novel tokenizer inspired by classical shape-gain quantization and the paradigm of basis functions coupled with continuous coefficients. Phaedra acts as a highly effective nonlinear compression algorithm, massively reducing dataset footprints while maintaining physical fidelity. We demonstrate that Phaedra consistently improves reconstruction across diverse PDE datasets, generalizes robustly to unseen PDE types and real-world Earth observation data, and provides immediate accuracy gains in initial downstream operator learning and masked autoencoding tasks.

Physics-informed neural networks (PINNs) train a single neural approximation by minimizing multiple physics- and data-derived losses, but the gradients of these losses often interfere and can stall optimization. Existing remedies typically treat this pathology either through scalar loss balancing or full-parameter-space gradient surgery, leaving it unclear which intervention is most appropriate. We show that PINN gradient conflict is not a uniform failure mode with one universal remedy. Instead, we identify distinct PINN gradient-conflict regimes, each associated with a different intervention class. Persistent directional conflict may require separate loss-indexed parameter subspaces, magnitude imbalance often favors scalar reweighting, and low or transient conflict may require no extra mitigation. To select between scalar reweighting and a lightweight architectural intervention, we propose a diagnostic-first framework. It profiles a 1000-step unmodified PINN run and, when intervention is warranted, uses one low-rank adapter per loss to create explicit loss-indexed parameter subspaces attached to a shared PINN trunk, providing each loss with a direct gradient pathway. Across more than 60 PDE configurations, including forward, inverse, multi-physics, parameter-varying, and high-dimensional problems up to 50D, persistent directional conflict dominates standard forward $K=3$ benchmarks and a natural $K=4$ thermoelastic system, where adapters combined with reweighting yield significant improvements. In contrast, $K=3$ inverse problems and natural $K=5$ and $K=6$ multi-physics systems are largely magnitude-dominated and often favor reweighting alone, while full-parameter-space gradient surgery can fail on heterogeneous parameter spaces. A regime-transition theorem and a blockwise neural tangent kernel analysis explain why gains from these separate adapter parameter subspaces arise selectively in persistent-conflict regimes.

Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems which are responsible for deciding which collision events to store impose strict latency and accuracy constraints. Transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, but their quadratic self-attention cost places them orders of magnitude beyond the trigger budget. Existing efficient variants reduce this cost by compressing the attention matrix or restricting it to ordered local windows, at the price of the explicit particle-particle interactions that drive substructure identification. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within this compute budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks (hls4ml, JetClass, Top Tagging, and Quark-Gluon). Our code is available at https://anonymous.4open.science/r/PHAT-JeT-540B/README.md

Solubility depends on intermolecular interactions between solutes and solvents and is central to drug discovery, chemical and materials process engineering. Its prediction requires out-of-distribution (OOD) generalization to unseen solutes, solvents, and solvent mixtures, where supervised methods suffer from data scarcity, and existing self-supervised pretraining methods do not explicitly learn chemically meaningful relations. Learning chemically meaningful relational information without labels can alleviate data scarcity and improve OOD generalizations, but remains challenging. We introduce Prior Relational Information for Self-supervised Modeling (PRISM) and apply it to solubility OOD generalizations. PRISM is a label-free pretraining framework that converts expert-defined chemical priors into relational teachers. For solubility prediction, it trains four encoders to preserve the population-level relational information from priors capturing hydrophobic, electrostatic, polarizability, and substructural effects, using Kullback-Leibler (KL) divergence to match pairwise distance-based distributions during pretraining. For scaffold, solute, and solvent OOD generalizations, PRISM matches or surpasses supervised and self-supervised baselines. For mixed-solvent OOD generalizations, freezing the four PRISM encoders and training a light fusion-and-composition head outperforms the strongest supervised baselines and greatly reduces prediction variations. Ablations and control studies confirm that gains stem from chemically meaningful relational structures baked into the encoders by PRISM. We expect this strategy of turning relational priors into self-supervision signals to extend beyond solubility prediction to domains where chemically or biologically meaningful relational priors are available.