World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making. A central distinction can be drawn between explicit world models, which learn structured dynamics for rollout-based reasoning and planning, and implicit world models, which encode predictive structure within scalable learned representations. These complementary paradigms provide a foundation for physical AI in domains such as robotics and autonomous driving, enabling intelligence beyond reactive control under real-world constraints. Recent foundation models further suggest a pathway toward unified systems integrating perception, prediction, and action. Despite rapid progress, major challenges remain in hierarchical reasoning, long-horizon planning, and autonomous goal formation, which are critical for advancing toward artificial general intelligence. This tutorial presents a coherent framework in which diverse world modeling approaches are unified through shared predictive structure and differentiated by how such structure is represented and exploited.

翻译：世界建模正成为构建具备预测、推理与决策能力的智能系统的核心原则。显式世界模型与隐式世界模型之间可建立一项核心区分：前者学习结构化动力学以实现基于推演的推理与规划，后者则在可扩展的习得表征中编码预测性结构。这些互补范式为机器人学和自动驾驶等领域的具身智能奠定了基础，使智能体能够在现实世界约束下超越反应式控制。近期的基础模型进一步揭示了整合感知、预测与行动的统一系统的发展路径。尽管进展迅速，但在层级推理、长时域规划以及自主目标形成等关键领域仍面临重大挑战，这些对迈向通用人工智能具有决定性意义。本导论提出一个统一框架，通过共享的预测性结构将多样化的世界建模方法加以统合，并根据该结构的不同表征与利用方式进行区分。