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

Generative world models hold immense promise as scalablesimulators for autonomous systems, particularly for synthesizing rare butsafety-critical multi-agent interactions, such as vehicle collisions. How-ever, current evaluation paradigms index heavily on visual fidelity and se-mantic alignment, leaving a critical blind spot: they cannot reliably quan-tify whether generated dynamics actually obey the fundamental physicallaws required for reliable simulation. Assessing this physical plausibilityis inherently difficult due to a lack of physical metrics and the challengeof extracting metric-scale kinematics from uncalibrated video rollouts.To bridge this gap, we introduce CrashTwin, a physics-grounded eval-uation framework designed to stress-test the physical trustworthiness ofworld models. CrashTwin couples a diverse dataset of multi-agent colli-sion scenarios, comprising 25K controllable synthetic and 12K in-the-wildreal-world collision sequences with a novel calibration-free reconstruc-tion pipeline, enabling the recovery of 3D physical attributes directlyfrom world model rollouts. We propose a diagnostic suite that systemat-ically evaluates three dimensions: spatio-temporal consistency, momen-tum and kinetic energy conservation, and world-dynamics integrity. Ex-tensive benchmarking of state-of-the-art models reveals a crucial insight:high perceptual quality frequently masks severe physical violations dur-ing complex interactions. By quantitatively exposing these failure modes,CrashTwin provides a vital diagnostic tool for developing physicallygrounded world models capable of reliable real-world simulation. Codeand dataset are available at: https://github.com/phai-lab/CrashTwin.