A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models

• URL: http://arxiv.org/abs/2606.28757v1
• Date: Sat, 27 Jun 2026 06:13:35 GMT
• Title: A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models
• Abstract summary: We introduce CrashTwin, a physics-grounded evaluation framework designed to stress-test the physical trustworthiness of world models.<n>CrashTwin couples a diverse dataset of multi-agent collision scenarios, comprising 25K controllable synthetic 12K in-the-wild real-world collision sequences.<n>We propose a diagnostic suite that systematically evaluates three dimensions:-temporal consistency, momentum and kinetic energy conservation, and world-dynamics integrity.
• Score: 96.64934195520802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi-agent interactions, such as vehicle collisions. However, current evaluation paradigms index heavily on visual fidelity and semantic alignment, leaving a critical blind spot: they cannot reliably quantify whether generated dynamics actually obey the fundamental physical laws required for reliable simulation. Assessing this physical plausibility is inherently difficult due to a lack of physical metrics and the challenge of extracting metric-scale kinematics from uncalibrated video rollouts. To bridge this gap, we introduce CrashTwin, a physics-grounded evaluation framework designed to stress-test the physical trustworthiness of world models. CrashTwin couples a diverse dataset of multi-agent collision scenarios, comprising 25K controllable synthetic and 12K in-the-wild real-world collision sequences with a novel calibration-free reconstruction pipeline, enabling the recovery of 3D physical attributes directly from world model rollouts. We propose a diagnostic suite that systematically evaluates three dimensions: spatio-temporal consistency, momentum and kinetic energy conservation, and world-dynamics integrity. Extensive benchmarking of state-of-the-art models reveals a crucial insight: high perceptual quality frequently masks severe physical violations during complex interactions. By quantitatively exposing these failure modes, CrashTwin provides a vital diagnostic tool for developing physically grounded world models capable of reliable real-world simulation.

Related papers

• GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models [66.77745044586173]
  GAUGE is a real-world-grounded diagnostic benchmark for evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics.<n>We benchmark Isaac Sim, Genesis, and Newton on 14 task families using generalized trajectory errors, and evaluate 6 image-to-video models on 5 rigid-body tasks.<n>Our results reveal no uniformly faithful physics engine, with the largest discrepancies arising in impulsive contact, rapid textile motion, and volumetric deformation.
  arXiv  Detail & Related papers  (2026-08-06T12:19:38Z)
• PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics [6.7982839614843575]
  PhysCoRe is a physics-corrected residual world model that couples a differentiable Material Point Method simulator with two feed-forward neural networks.<n>A material refinement module infers per-particle elasticity from visual observations, grounding the simulator in object-specific physics.<n>A residual correction module, Residual from Dynamics (RfD), learns the discrepancy and predicts corrections to the simulator's internal dynamics.
  arXiv  Detail & Related papers  (2026-07-22T18:25:57Z)
• PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation [37.9748770367306]
  Video generation models can produce physically implausible manipulations, including inconsistent robot-object interactions.<n>We propose PhysisForcing, a training framework that strengthens physical consistency by focusing supervision on physics-informative regions.<n>Our experiments show that PhysisForcing consistently improves embodied video generation over strong baselines.
  arXiv  Detail & Related papers  (2026-06-26T14:30:18Z)
• PhysiFormer: Learning to Simulate Mechanics in World Space [56.16644631495]
  We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion.<n>It represents objects as 3D meshes expressed in world coordinates.<n>It substantially outperforms autoregressive baselines in trajectory accuracy, rigidity preservation, and momentum-based physical consistency.
  arXiv  Detail & Related papers  (2026-06-25T17:59:00Z)
• SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics [49.771521708359955]
  SurgVista is a surgical world model that mitigates spatial interaction incoherence and temporal fidelity collapse.<n>It consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity.
  arXiv  Detail & Related papers  (2026-06-18T07:47:28Z)
• PhyCo: Learning Controllable Physical Priors for Generative Motion [55.59209981836171]
  We present PhyCo, a framework that introduces continuous, interpretable, and physically grounded control into video generation.<n>Our approach integrates three key components: (i) a large-scale dataset of over 100K photorealistic simulation videos where friction, restitution, deformation, and force are systematically varied across diverse scenarios; (ii) physics-supervised fine-tuning of a pretrained diffusion model using a ControlNet conditioned on pixel-aligned physical property maps; and (iii) VLM-guided reward optimization, where a fine-tuned vision-language model evaluates generated videos with targeted physics queries and provides differentiable feedback.
  arXiv  Detail & Related papers  (2026-04-30T17:53:03Z)
• STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching [2.5845893156827158]
  We propose STRIDE, a dynamics learning framework that separates conservative rigid-body mechanics from uncertain, effectively non-conservative interaction effects.<n>We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1ruped, and the Unitree G1 humanoid.<n>Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines.
  arXiv  Detail & Related papers  (2026-03-09T15:15:21Z)
• Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning [11.05191602561784]
  Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic control.<n>We propose a novel physics-constrained Real-to-Sim pipeline that reconstructs physically consistent 3D scenes from single-view RGB-D data.
  arXiv  Detail & Related papers  (2026-02-13T05:24:58Z)
• Aligning Agentic World Models via Knowledgeable Experience Learning [68.85843641222186]
  We introduce WorldMind, a framework that constructs a symbolic World Knowledge Repository by synthesizing environmental feedback.<n>WorldMind achieves superior performance compared to baselines with remarkable cross-model and cross-environment transferability.
  arXiv  Detail & Related papers  (2026-01-19T17:33:31Z)
• PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models [100.65199317765608]
  Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation.<n>We introduce a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces.<n>We extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning.
  arXiv  Detail & Related papers  (2026-01-16T08:40:10Z)
• Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows [18.240532888032394]
  We present REALM (REalistic AI Learning for Multiphysics), a rigorous benchmarking framework designed to test neural surrogates on challenging, application-driven reactive flows.<n>We benchmark over a dozen representative surrogate model families, including spectral operators, convolutional models, Transformers, pointwise operators, and graph/mesh networks.<n>We identify three robust trends: (i) a scaling barrier governed jointly by dimensionality, stiffness, and mesh irregularity, leading to rapidly growing rollout errors; (ii) performance primarily controlled by architectural inductive biases rather than parameter count; and (iii) a persistent gap between nominal accuracy metrics and physically
  arXiv  Detail & Related papers  (2025-12-21T05:04:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.