Shanghai AI Lab Recasts World Modeling: From Physical States to Agent-Usable Information
Shanghai-AI-Laboratory · hf · 2026-08-05
Shanghai AI Lab published a paper redefining the role of world models in agent evolution. Classical models relying on physical-state prediction are deemed too narrow for agents requiring actionable feedback.
- Core Concept: Proposes Agent-Centric Interactive World Proxies, shifting the paradigm from physical state transitions to agent-usable information transitions (e.g., execution outcomes, retrieved experiences, verification signals).
- Taxonomy: Organizes world proxies into six functional forms: dynamics, spatial, execution, memory/experience, skill, and reward/verification.
- Empowerment Levels: L.1 Inference-Time Guidance; L.2 Training-Time Optimization; L.3 Agent-Proxy Co-Evolution, where both continuously update based on real-environment evidence.
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