OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
Abstract
OpenWorldLib presents a standardized framework for advanced world models that integrate perception, interaction, and long-term memory capabilities for comprehensive world understanding and prediction.
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
Community
Hello everyone, welcome to follow our work. Given the current diversity of research on world models, we aim to provide a unified definition and calling standard for world models, establishing a clear boundary for this direction. If you are interested, or would like to promote your own work related to world models, please feel free to raise an issue in our code link: https://github.com/OpenDCAI/OpenWorldLib .
One thing to note: because we aim to cover as many methods as possible, the environment is relatively complex. This codebase primarily supports inference for different world model tasks. For training, reward settings, and similar aspects, this project does not currently support them. In our next project, we will focus on training and optimizing the lightest and most effective model for each task.
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