李飞飞押注的空间智能：生成的世界，如何「经得起折腾」？

TL;DR - The article argues that useful AI-generated 3D worlds require a persistent, verifiable state layer—not just strong rendering—to remain coherent through actions and edits. This state-centric architecture could turn spatial generation from impressive demos into reliable systems that simulate, plan, validate, learn, and roll back changes.

• A robust world model links a Renderer, Simulator, and Planner through shared records of object identity, geometry, relationships, constraints, runtime state, and version history.
• Current visual and programmatic approaches struggle with slow time-to-first-valid-result and broken global consistency after local edits, often forcing costly full-scene regeneration.
• The proposed closed loop records state–goal–action–new-state–verification trajectories, enabling incremental recomputation, failure diagnosis, rollback, and recursive improvement from user interactions.
• Validation must extend beyond visual quality to geometry, physics, path feasibility, long-horizon state consistency, and successful task completion.