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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.20668 |
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| _version_ | 1866908607648366592 |
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| author | Bai, Jinbin Lei, Yu Wu, Hecong Zhu, Yuchen Li, Shufan Xin, Yi Li, Xiangtai Tao, Molei Grover, Aditya Yang, Ming-Hsuan |
| author_facet | Bai, Jinbin Lei, Yu Wu, Hecong Zhu, Yuchen Li, Shufan Xin, Yi Li, Xiangtai Tao, Molei Grover, Aditya Yang, Ming-Hsuan |
| contents | This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model". Instead, we follow one clear road: from early masked models that unified representation learning across modalities, to unified architectures that share a single paradigm, then to interactive generative models that close the action-perception loop, and finally to memory-augmented systems that sustain consistent worlds over time. We bypass loosely related branches to focus on the core: the generative heart, the interactive loop, and the memory system. We show that this is the most promising path towards true world models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Masks to Worlds: A Hitchhiker's Guide to World Models Bai, Jinbin Lei, Yu Wu, Hecong Zhu, Yuchen Li, Shufan Xin, Yi Li, Xiangtai Tao, Molei Grover, Aditya Yang, Ming-Hsuan Machine Learning This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model". Instead, we follow one clear road: from early masked models that unified representation learning across modalities, to unified architectures that share a single paradigm, then to interactive generative models that close the action-perception loop, and finally to memory-augmented systems that sustain consistent worlds over time. We bypass loosely related branches to focus on the core: the generative heart, the interactive loop, and the memory system. We show that this is the most promising path towards true world models. |
| title | From Masks to Worlds: A Hitchhiker's Guide to World Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.20668 |