上海AI Lab、浙大、NUS团队：世界模型，何去何从？
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TL;DR - A position/survey paper from Shanghai AI Lab, Zhejiang University, and NUS (arXiv:2608.02713) proposes shifting world models from "predicting the world" to an agent-centric "World Proxy" that sits between an agent and the real environment, returning low-cost, controllable feedback for planning, learning, and self-improvement. It reframes the evaluation target from visual realism/state-prediction accuracy to actionable information gain for agents.
- Three requirements for a World Proxy: closed-loop agent-initiated queries/actions/interventions; environment grounding learned from real data, rules, trajectories, or interaction evidence; and optimization for actionable information gain rather than photorealism.
- Three intervention levels: L1 inference-time guidance (memory/skill retrieval, execution simulation, verification injected into context, no parameter changes); L2 training-time optimization (scoring rollouts, failure diagnosis, synthetic trajectories/preference pairs feeding SFT, DPO, PPO, GRPO); L3 agent-proxy co-evolution (real trajectories flow back to update the proxy, which then retrains the agent).
- Six functional forms by modeled information transfer: dynamics (next state/reward), spatial (observations from new viewpoints/poses), execution (code, commands, web clicks, API/tool results including stdout/stderr/tests), memory/experience (retrieved past failures and constraints), skill (reusable tools and action priors), and reward/verification (critique, preferences, safety checks).
- Open problems named: limited fidelity and error accumulation over long trajectories, no reliable signal for when an agent should distrust the proxy and fall back to the real environment, reward hacking when the proxy acts as verifier, and the absence of agent-centric benchmarks that measure whether feedback actually improves the agent.
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上海AI Lab、浙大、NUS团队：世界模型，何去何从？
TL;DR - A position/survey paper from Shanghai AI Lab, Zhejiang University, and NUS (arXiv:2608.02713) proposes shifting world models from "predicting the world" to an agent-centric "World Proxy" that sits between an agent and the real environment, returning low-cost, controllable feedback for planning, learning, and self-improvement. It reframes the evaluation target from visual realism/state-prediction accuracy to actionable information gain for agents.
- Three requirements for a World Proxy: closed-loop agent-initiated queries/actions/interventions; environment grounding learned from real data, rules, trajectories, or interaction evidence; and optimization for actionable information gain rather than photorealism.
- Three intervention levels: L1 inference-time guidance (memory/skill retrieval, execution simulation, verification injected into context, no parameter changes); L2 training-time optimization (scoring rollouts, failure diagnosis, synthetic trajectories/preference pairs feeding SFT, DPO, PPO, GRPO); L3 agent-proxy co-evolution (real trajectories flow back to update the proxy, which then retrains the agent).
- Six functional forms by modeled information transfer: dynamics (next state/reward), spatial (observations from new viewpoints/poses), execution (code, commands, web clicks, API/tool results including stdout/stderr/tests), memory/experience (retrieved past failures and constraints), skill (reusable tools and action priors), and reward/verification (critique, preferences, safety checks).
- Open problems named: limited fidelity and error accumulation over long trajectories, no reliable signal for when an agent should distrust the proxy and fall back to the real environment, reward hacking when the proxy acts as verifier, and the absence of agent-centric benchmarks that measure whether feedback actually improves the agent.