STaC-WAM: Asymmetric World-Action Models via Spatio-Temporal Action-Conditioning

World Action Models (WAMs) improve robot policies by coupling action gener- ation with predictive visual modeling. However, efficient inference remains chal- lenging. Implicit WAMs avoid test-time future-video generation by using a pre- dictive video backbone, but they still lack an explicit mechanism for transforming predictive visual representations into compact action-conditioning features. Video features encode high-dimensional visual dynamics, including object interactions, motion patterns, and local details, whereas action generation only requires a subset relevant for control. Without such an interface, the action expert must implicitly learn this transformation through extra capacity, making action generation com- putationally expensive. We introduce STaC-WAM, an asymmetric implicit WAM that couples a large predictive video backbone with a lightweight action expert through a Spatio-Temporal Action-Conditioning (STaC) bridge. Instead of di- rectly exposing the action expert to deep video representations, STaC compresses multi-layer video features into compact action-conditioning representations while preserving object-level structure and fine-grained visual details. Our experiments suggest a broader design principle for WAMs with predictive visual backbones: visual modeling and action generation need not scale symmetrically. With the STaC bridge, a 6-layer Action DiT outperforms the 30-layer Fast-WAM baseline on LIBERO, improving success rate from 97.6% to 98.85% while increasing infer- ence speed by 2.10x, with pronounced gains on LIBERO-Long. Evaluations on simulated and real-world tasks further demonstrate the robustness of this asym- metric design. Ablations show that Temporal Similarity Alignment provides the largest gain, while Object Assignment Alignment and the Object-Guided Dual- Path Decoder provide complementary benefits under aggressive compression.