世界模型该学什么？答案从物理换成“对你有用”
世界模型一直想当“物理模拟器”：预测下一帧画面、下一个状态。但一个智能体真正需要的反馈，不是“世界会怎样”，而是“我这么做行不行、有没有更好的做法、上次类似情况怎么处理的”。这篇论文把世界模型重新定义成“智能体可用的信息代理”，按反馈类型分成六种：动态、空间、执行、记忆、技能、奖励/验证，并给出三个使用层级——推理时给提示、训练时给奖励或合成数据、以及让代理和模型互相进化。它不是在某个任务上刷分，而是给整个领域画了一张新地图。你明天用不上，但如果你关心 AI 怎么从“会预测”走向“会自己变强”，这是值得盯的方向。
📄 原文摘要(英文)
Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more controllable feedback before committing to real actions. Classical world models instantiate this proxy primarily through future physical-state prediction, a formulation useful yet narrow for agents that require actionable feedback beyond raw state transitions. In this work, we conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile feedback for continually improving agents. To systematically map this design space, we organize world proxies into six functional forms based on their feedback modalities: dynamics, spatial, execution, memory/experience, skill, and reward/verification proxies, which together characterize the primary ways world modeling serves agent improvement. We further analyze how these proxies empower agents across three progressive levels: L.1 Inference-Time Guidance, where proxy outputs enrich in-context information for superior decisions; L.2 Training-Time Optimization, where proxy outputs yield rewards, critiques, or synthetic rollouts for policy learning; and L.3 Agent-Proxy Co-Evolution, where real-environment evidence continuously updates both the proxy and the agent for co-evolution. Ultimately, this work recasts world modeling into an agent-centric paradigm, establishing a roadmap for building world proxies that empower agents to plan better, learn faster, and evolve continually.