A Definition and Roadmap for World Models
World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinforcement learning and video generation to embodied robotics and ultimately, physical AI, researchers across AI subfields are building systems that they call "world models", yet there is no consensus on what a world model fundamentally is, what it should predict, or how it should be built. This perspective article provides a scientific definition of world models, discussions of their key technical aspects, and a staged
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorXinyuan Chen →
A Definition and Roadmap for World Models
- Linked via arxiv authorHaoyu Guo →
A Definition and Roadmap for World Models
- Linked via arxiv authorShi Guo →
A Definition and Roadmap for World Models
- Linked via arxiv authorBingqi Jiang →
A Definition and Roadmap for World Models
- Linked via arxiv authorChunhua Shen →
A Definition and Roadmap for World Models
- Linked via arxiv authorXing Shen →
A Definition and Roadmap for World Models
- Linked via arxiv authorTianfan Xue →
A Definition and Roadmap for World Models
- Linked via arxiv authorYufei Xue →
A Definition and Roadmap for World Models
- Linked via arxiv authorMulin Yu →
A Definition and Roadmap for World Models
- Linked via arxiv authorWeinan Zhang →
A Definition and Roadmap for World Models
- Linked via arxiv authorBin Zhao →
A Definition and Roadmap for World Models
- Linked via arxiv authorBowen Zhou →
A Definition and Roadmap for World Models
- Linked via arxiv authorMing Zhou →
A Definition and Roadmap for World Models