Zero-shot World Models Are Developmentally Efficient Learners
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908957087367168 |
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| author | Aw, Khai Loong Kotar, Klemen Lee, Wanhee Kim, Seungwoo Jedoui, Khaled Venkatesh, Rahul Chen, Lilian Naing Frank, Michael C. Yamins, Daniel L. K. |
| author_facet | Aw, Khai Loong Kotar, Klemen Lee, Wanhee Kim, Seungwoo Jedoui, Khaled Venkatesh, Rahul Chen, Lilian Naing Frank, Michael C. Yamins, Daniel L. K. |
| contents | Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene understanding. Children are both data-efficient and flexible cognitive systems, creating competence despite extremely limited training data, while generalizing to myriad untrained tasks -- a major challenge even for today's best AI systems. Here we introduce a novel computational hypothesis for these abilities, the Zero-shot Visual World Model (ZWM). ZWM is based on three principles: a sparse temporally-factored predictor that decouples appearance from dynamics; zero-shot estimation through approximate causal inference; and composition of inferences to build more complex abilities. We show that ZWM can be learned from the first-person experience of a single child, rapidly generating competence across multiple physical understanding benchmarks. It also broadly recapitulates behavioral signatures of child development and builds brain-like internal representations. Our work presents a blueprint for efficient and flexible learning from human-scale data, advancing both a computational account for children's early physical understanding and a path toward data-efficient AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10333 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Zero-shot World Models Are Developmentally Efficient Learners Aw, Khai Loong Kotar, Klemen Lee, Wanhee Kim, Seungwoo Jedoui, Khaled Venkatesh, Rahul Chen, Lilian Naing Frank, Michael C. Yamins, Daniel L. K. Artificial Intelligence Computer Vision and Pattern Recognition Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene understanding. Children are both data-efficient and flexible cognitive systems, creating competence despite extremely limited training data, while generalizing to myriad untrained tasks -- a major challenge even for today's best AI systems. Here we introduce a novel computational hypothesis for these abilities, the Zero-shot Visual World Model (ZWM). ZWM is based on three principles: a sparse temporally-factored predictor that decouples appearance from dynamics; zero-shot estimation through approximate causal inference; and composition of inferences to build more complex abilities. We show that ZWM can be learned from the first-person experience of a single child, rapidly generating competence across multiple physical understanding benchmarks. It also broadly recapitulates behavioral signatures of child development and builds brain-like internal representations. Our work presents a blueprint for efficient and flexible learning from human-scale data, advancing both a computational account for children's early physical understanding and a path toward data-efficient AI systems. |
| title | Zero-shot World Models Are Developmentally Efficient Learners |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.10333 |