解析世界模型如何让AI具备预测与决策能力
A Tutorial on World Models and Physical AI
- 区分显式与隐式世界模型，统一预测结构框架
- 支持机器人与自动驾驶的长期规划与推理
- 适合研究物理AI与通用智能的学者参考
世界建模正成为构建具备预测、推理与决策能力智能系统的核心原则。主要分为显式世界模型（学习结构化动态以进行滚动推理与规划）与隐式世界模型（将预测结构编码于可扩展的表示中）。这两种互补范式为机器人学与自动驾驶等领域的物理AI奠定了基础，使智能体能在真实约束下实现超越反应式控制的智能。近期基础模型进一步揭示了感知、预测与行动一体化系统的可能路径。尽管进展迅速，层次化推理、长时程规划与自主目标生成仍是迈向通用人工智能的关键挑战。本教程提出一个统一框架，通过共享的预测结构整合多样世界建模方法，并依据其表示与利用方式加以区分。
原文摘要 · Abstract (English)
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.
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