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Fixing world-action models to a single next-state representation fundamentally bottlenecks policy learning; supervising across dynamically sampled geometric, semantic, and visual projections of the same future allows robots to directly absorb dynamics from heterogeneous human video.
Different views of the same problem reveal hidden reasoning paths, enabling VLMs to achieve unprecedented accuracy in multimodal reasoning tasks.
