Article

WorldScape Policy 2.0: Steerable World Action Modeling via Reasoning-Augmented Memory and In-Context Learning

The paper proposes WorldScape Policy 2.0, a controllable World Action Model (WAM) with reasoning-augmented long short-term memory. Its causal short-term visual memory supplies recent observations as DiT prefill to preserve local interaction dynamics, while its long-term event memory organizes historical VLM outputs into global-history, local-active, and event-boundary representations for progress-aware retrieval and to generate implicit subgoal conditions.

Part of the event: Proposing the WorldScape Policy 2.0 world action model

This page provides a summary and source link only.