Decoupled-WAM: Decoupling Imagination and Action for Efficient and Flexible World Action Models

World Action Models (WAMs) transfer temporal and physical priors from video generative models to robot control. As future-video generation becomes less central at inference time, the representation transferred from the video model to the action policy becomes a key architectural question. Existing WAMs typically pass video-side attention states through layer-wise interfaces, binding the Action Expert to the internals of a particular video model. We introduce Decoupled-WAM, which instead places the video-action boundary at a shared dense planning feature. The Video Expert combines a design-time subset of intermediate layer outputs into a fixed-shape representation, while every Action Expert block constructs its own visual keys and values to read that representation through gated cross-attention. This interface supports Action Expert capacity variation, early termination after the deepest selected video tap, and replacement of the Video Expert while retaining a trained Action Expert. On LIBERO, ten-layer and one-layer Action Experts achieve 98.5% and 98.2% success, respectively; the one-layer model operates at 40.5 ms per action query and 24.7 queries/s. It further reaches 75.3% on LIBERO-Plus, while the default model obtains 91.9% on RoboTwin 2.0. With the one-layer Action Expert frozen, a five-layer student Video Expert retains 94.7% LIBERO success compared with 98.2% for its paired teacher. Retention is substantially weaker under distribution shifts, showing that interface regression does not preserve all aspects of the teacher’s closed-loop behavior. These results characterize the capabilities and current limits of the shared video-action interface.