DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models

arXiv:2607.25918v1 Announce Type: new Abstract: World-Action Models (WAMs) augment robot policies with future visual prediction, but it remains unclear what the visual modality should learn for control. While photorealistic future prediction provides dense supervision, it also incurs substantial computation and can allocate capacity to texture, illumination, and background variations that are only weakly related to action selection. Recent efficient WAM variants suggest that the main benefit of ...