Taekoan Yoo, Wonkyung Jung, Kyunghun Kim, Kyeongbo Kong · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31429 · open-access PDF
World action models increasingly rely on pretrained video models assumed to capture physical dynamics for prediction, planning, and action generation. Existing evaluations assess plausibility, probe representations, or measure predictive surprise, but rarely identify which history-conditioned physical expectations conflict with observations. We introduce PhysTrace, a training-free framework aligning MLLM-generated, history-conditioned expectations with observed video windows to temporally localize physical violations and provide interpretable descriptions without task-specific probes or classi
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