定义世界模型并给出分阶段构建路线图
A Definition and Roadmap for World Models
- 提出世界模型的科学定义与核心构成要素
- 梳理从环境建模到物理智能的关键技术路径
- 适合关注具身智能与模型基强化学习的研究者
世界模型——即学习环境结构与动态的内部模拟器——已成为人工智能领域最活跃的讨论话题之一。从基于模型的强化学习、视频生成，到具身机器人乃至物理智能，不同子领域的研究者都在构建被称作‘世界模型’的系统，但对其本质、应预测内容及构建方式尚无共识。本文提出世界模型的科学定义，探讨其关键技术环节，并制定分阶段的发展路线图。
原文摘要 · Abstract (English)
World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinforcement learning and video generation to embodied robotics and ultimately, physical AI, researchers across AI subfields are building systems that they call "world models", yet there is no consensus on what a world model fundamentally is, what it should predict, or how it should be built. This perspective article provides a scientific definition of world models, discussions of their key technical aspects, and a staged roadmap for developing effective world models.
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