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
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
OpenWorldLib presents a standardized framework for advanced world models that integrate perception, interaction, and long-term memory capabilities for comprehensive world understanding and prediction.
- Year
- 2026
- Venue
- arXiv 2026
- Stars
- 793
- Authors
- 41
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2604.04707ARXIV-DEFAULT
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Authors
41Yifan YangJunbo NiuWentao ZhangRuichuan AnMike Zheng ShouHao LiangMeiyi QiangZimo MengXiaochen MaBohan ZengZekun WangJialong WuXintao WangPengfei WanYiren SongDaili HuaXinyi HuangXinlong ChenHongcheng GaoYang ShiYue DingBozhou LiYuran WangYifan DaiChengzhuo TongYuanxing ZhangYiwen TangTianyi BaiKaixin ZhuZhou LiuTianyu GuoHuanyao ZhangZhiyou XiaoDataFlow TeamMingkun ChangJianbin ZhaoQinhan YuRunhao ZhaoZhengpin LiYisheng PanMinglei Shi