What 30,000 Hours of Ego-centric Video Does Not Teach
World models based on ego-centric human video are limited by scaling data; improving agent fidelity does not equally improve object fidelity. The authors introduce object-centric adaptive noise scheduling to supervise model learning, which raises the ceiling for manipulating objects. This gain transfers to humanoid modeling, but the fundamental gap remains due to irreducible uncertainty about physical properties.