构建统一框架，定义高级世界模型的通用标准与能力体系。
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
- 提出世界模型的清晰定义：具感知、交互和长期记忆能力的智能体。
- 建立OpenWorldLib框架，整合多任务模型实现高效协同推理。
- 适合关注通用人工智能与具身智能的研究者参考。
世界模型作为人工智能的重要研究方向备受关注，但缺乏统一的定义。本文提出OpenWorldLib，一个面向高级世界模型的综合性标准化推理框架。基于世界模型的发展脉络，我们明确定义：世界模型是以感知为中心，具备交互与长期记忆能力，用于理解与预测复杂世界的模型或系统。进一步系统化梳理了世界模型的核心能力体系。基于此定义，OpenWorldLib将不同任务的模型整合进统一框架，支持高效复用与协同推理。最后，对世界模型研究的未来方向进行了反思与展望。代码已开源：https://github.com/OpenDCAI/OpenWorldLib。
原文摘要 · Abstract (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
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