arXiv: cs.AI preprintsInternational6 November 2026
PhysTrace: Interpretable Temporal Evaluation of Physical Consistency in Video Generative Models
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arXiv:2610.31429v2 Announce Type: replace Abstract: World action models increasingly rely on pretrained diffusion/flow-based video generators assumed to capture physical dynamics for prediction, planning, and action generation. Existing evaluations assess plausibility, probe representations, or measure predictive surprise, yet physical consistency is history-dependent: preceding events constrain what can physically occur later. We introduce PhysTrace, a training-free framework that uses MLLMs to formulate history-conditioned physical expectations and tracks their agreement with observed video
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