何往，世界建模？

摘要

持续改进的智能体需要超越静态监督的动态交互反馈，然而直接的真实环境交互代价高昂、速度缓慢、存在安全风险且难以并行化。世界建模提供了一种自然的中间代理，使智能体能够在采取真实行动之前查询成本更低、更可控的反馈。经典世界模型主要通过未来物理状态预测来实例化这种代理，这种形式对于需要原始状态转换之外的可操作反馈的智能体而言虽有用却过于狭窄。在本工作中，我们提出了以智能体为中心的交互式世界代理（Agent-Centric Interactive World Proxies）的概念，将基本范式从物理状态转换转向智能体可用的信息转换，例如执行结果、检索到的经验或技能以及验证信号，从而拓宽世界建模的范围，为持续改进的智能体提供多样化的反馈。为了系统性地描绘这一设计空间，我们根据反馈模态将世界代理组织为六种功能形式：动力学代理、空间代理、执行代理、记忆/经验代理、技能代理以及奖励/验证代理，这些形式共同刻画了世界建模服务于智能体改进的主要方式。我们进一步分析了这些代理如何在三个递进层面赋能智能体：L.1 推理时引导（Inference-Time Guidance），其中代理输出丰富上下文信息以支持更优决策；L.2 训练时优化（Training-Time Optimization），其中代理输出产生奖励、批评或合成轨迹用于策略学习；L.3 智能体-代理协同进化（Agent-Proxy Co-Evolution），其中真实环境证据持续更新代理与智能体，以实现协同进化。最终，本工作将世界建模重塑为以智能体为中心的范式，为构建能够赋能智能体更好地规划、更快学习并持续进化的世界代理确立了路线图。

English

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.