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

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