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Ego-centric video scaling saturates agent modeling long before object interaction fidelity, revealing a fundamental bottleneck in current world model training paradigms.
Humanoid robots can now learn complex loco-manipulation tasks from sparse demonstrations by iteratively evolving their own training data and policies.
