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.

翻译：随着人工智能系统从文本生成转向通过持续交互实现目标，对环境动力学建模的能力成为核心瓶颈。操作物体、导航软件、与他人协作或设计实验的智能体需要预测性环境模型，但"世界模型"一词在不同研究领域中含义各异。本文提出一种"能力层级×规律类型"的分类框架，沿两个维度展开：第一维度定义三个能力层级——L1预测器（学习单步局部转移算子）、L2模拟器（将算子组合为遵循领域规律的多步行动条件化推演）与L3演化器（当预测与新的证据不符时自主修正自身模型）；第二维度识别四种规律控制领域：物理规律、数字规律、社会规律与科学规律。这些领域决定了世界模型必须满足的约束条件及其最可能失效的场景。借助该框架，我们综合分析了400余篇文献，总结了超过100个代表性系统，涵盖基于模型的强化学习、视频生成、网页与图形用户界面智能体、多智能体社会模拟以及人工智能驱动的科学发现。我们针对不同层级-规律组合下的方法、失效模式与评估实践进行剖析，提出以决策为中心的评估原则与可复现的最小化评估工具包，并勾勒出架构指引、开放问题与治理挑战。由此形成的路线图将先前孤立的研究社群联系起来，描绘了一条从被动式下一步预测迈向能够模拟并最终重塑智能体运行环境的世界模型的路径。