OpenWorldLib：先进世界模型的统一代码库与定义

OpenWorldLib: A Unified Codebase and Definition of Advanced World Models

April 6, 2026

作者: DataFlow Team, Bohan Zeng, Daili Hua, Kaixin Zhu, Yifan Dai, Bozhou Li, Yuran Wang, Chengzhuo Tong, Yifan Yang, Mingkun Chang, Jianbin Zhao, Zhou Liu, Hao Liang, Xiaochen Ma, Ruichuan An, Junbo Niu, Zimo Meng, Tianyi Bai, Meiyi Qiang, Huanyao Zhang, Zhiyou Xiao, Tianyu Guo, Qinhan Yu, Runhao Zhao, Zhengpin Li, Xinyi Huang, Yisheng Pan, Yiwen Tang, Yang Shi, Yue Ding, Xinlong Chen, Hongcheng Gao, Minglei Shi, Jialong Wu, Zekun Wang, Yuanxing Zhang, Xintao Wang, Pengfei Wan, Yiren Song, Mike Zheng Shou, Wentao Zhang

cs.AI

摘要

世界模型作为人工智能领域的重要研究方向备受关注，但目前仍缺乏清晰统一的定义。本文提出OpenWorldLib——一个面向先进世界模型的标准化综合推理框架。基于世界模型的发展脉络，我们给出明确定义：世界模型是以感知为核心、具备交互与长期记忆能力，用于理解和预测复杂世界的模型或框架。我们进一步系统化梳理了世界模型的核心能力体系。基于该定义，OpenWorldLib将不同任务领域的模型整合至统一框架，实现高效复用与协同推理。最后，我们对世界模型研究的未来发展方向提出了进一步思考与分析。代码链接：https://github.com/OpenDCAI/OpenWorldLib

English

World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we introduce OpenWorldLib, a comprehensive and standardized inference framework for Advanced World Models. Drawing on the evolution of world models, we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. We further systematically categorize the essential capabilities of world models. Based on this definition, OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference. Finally, we present additional reflections and analyses on potential future directions for world model research. Code link: https://github.com/OpenDCAI/OpenWorldLib