As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that manipulate objects, navigate software, coordinate with others, or design experiments require predictive environment models, yet the term world model carries different meanings across research communities. We introduce a "levels x laws" taxonomy organized along two axes. The first defines three capability levels: L1 Predictor, which learns one-step local transition operators; L2 Simulator, which composes them into multi-step, action-conditioned rollouts that respect domain laws; and L3 Evolver, which autonomously revises its own model when predictions fail against new evidence. The second identifies four governing-law regimes: physical, digital, social, and scientific. These regimes determine what constraints a world model must satisfy and where it is most likely to fail. Using this framework, we synthesize over 400 works and summarize more than 100 representative systems spanning model-based reinforcement learning, video generation, web and GUI agents, multi-agent social simulation, and AI-driven scientific discovery. We analyze methods, failure modes, and evaluation practices across level-regime pairs, propose decision-centric evaluation principles and a minimal reproducible evaluation package, and outline architectural guidance, open problems, and governance challenges. The resulting roadmap connects previously isolated communities and charts a path from passive next-step prediction toward world models that can simulate, and ultimately reshape, the environments in which agents operate. Code and resources are available at: https://github.com/matrix-agent/awesome-agentic-world-modeling.

翻译：随着人工智能系统从文本生成转向通过持续交互来实现目标，对环境动态进行建模的能力成为核心瓶颈。能够操控物体、导航软件、与人协作或设计实验的智能体，都需要具备预测性的环境模型；然而，“世界模型”这一术语在不同研究群体中含义各异。我们提出了一种“层级 × 法则”的分类体系，包含两个维度。第一维度定义了三个能力层级：L1 预测器（Predictor），学习单步的局部转移算子；L2 模拟器（Simulator），将多个单步算子组合成遵循领域法则的多步、行动条件下的推演（rollout）；L3 演化器（Evolver），当预测结果与新的证据不符时，能自主修正其自身模型。第二维度则定义了四种支配性法则领域：物理、数字、社会与科学。这些法则领域决定了世界模型必须满足哪些约束，以及其最可能在何处失效。我们利用该框架综合评述了超过400篇文献，并总结了100多个具有代表性的系统，涵盖了基于模型的强化学习、视频生成、网页及图形用户界面（GUI）智能体、多智能体社会模拟，以及人工智能驱动的科学发现。我们分析了不同层级-法则对下的方法、失败模式及评估实践，提出了以决策为中心的评估原则及一个可复现的最小化评估包，并概述了架构设计指南、开放问题及治理挑战。由此形成的路线图将此前相互隔离的研究社群联系起来，并勾画出一条从被动的下一步预测，通往能够模拟、并最终重塑智能体所处环境的世界模型之路。代码与资源可访问：https://github.com/matrix-agent/awesome-agentic-world-modeling。