The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including generative games, autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/World-Model.

翻译：随着GPT-4等多模态大语言模型和Sora等视频生成模型的发展，世界模型的概念因其在追求通用人工智能中的核心地位而受到广泛关注。本文对世界模型的相关文献进行了全面综述。通常，世界模型被视为理解世界当前状态或预测其未来动态的工具。本综述系统性地对世界模型进行了分类，强调其两大主要功能：(1) 构建内部表征以理解世界运行机制；(2) 预测未来状态以模拟并指导决策。首先，我们审视了这两类功能的当前研究进展。随后，我们探讨了世界模型在关键领域的应用，包括生成式游戏、自动驾驶、机器人学和社会模拟，重点关注各领域如何利用这些功能。最后，我们概述了主要挑战，并对未来潜在研究方向提出了见解。代表性论文及其代码仓库已总结于https://github.com/tsinghua-fib-lab/World-Model。