Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Authors: name: Meng Chu, name: Xuan Billy Zhang, name: Kevin Qinghong Lin, name: Lingdong Kong, name: Jize Zhang, name: Teng Tu, name: Weijian Ma, name: Ziqi Huang, name: Senqiao Yang, name: Wei Huang, name: Yeying Jin, name: Zhefan Rao, name: Jinhui Ye, name: Xinyu Lin, name: Xichen Zhang, name: Qisheng Hu, name: Shuai Yang, name: Leyang Shen, name: Wei Chow, name: Yifei Dong, name: Fengyi Wu, name: Quanyu Long, name: Bin Xia, name: Shaozuo Yu, name: Mingkang Zhu, name: Wenhu Zhang, name: Jiehui Huang, name: Haokun Gui, name: Haoxuan Che, name: Long Chen, name: Qifeng Chen, name: Wenxuan Zhang, name: Wenya Wang, name: Xiaojuan Qi, name: Yang Deng, name: Yanwei Li, name: Mike Zheng Shou, name: Zhi-Qi Cheng, name: See-Kiong Ng, name: Ziwei Liu, name: Philip Torr, name: Jiaya Jia

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.