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The results support frozen verification as a training signal for evidence selection, while showing that strict boundary precision remains comparatively weaker.
PhysMLLMs is a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability.
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