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This talk proposes EvoPhys-World, a five-dimensional world model that adds causal worldlines to three-dimensional space and time. Inspired by human motor control and multisensory feedback, it uses branching and collapsing worldlines, a dual world-engine and world-policy design, Phy-JEPA representation decoupling, and self-supervised 4D memory to learn physical intelligence from large-scale human-interaction video.
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