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

- Source: arXiv cs.RO
- Published: 2026-10-21T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/t7l0iluyew2r78w8wvzi76nat
- Original: https://arxiv.org/abs/2607.18840

## Summary

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.