Zero-shot World Models Are Developmentally Efficient Learners
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.
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Khai Loong Aw
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2026-04-14T08:15:06.935Z
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Khai Loong Aw
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Klemen Kotar
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2026-04-15T12:07:30.873Z
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Klemen Kotar
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Wanhee Lee
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Seungwoo Kim
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Khaled Jedoui
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Rahul Venkatesh
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Lilian Naing Chen
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Michael C. Frank
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Daniel L. K. Yamins
Zero-shot World Models Are Developmentally Efficient Learners
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.
A computational model called Zero-shot Visual World Model demonstrates how children can efficiently learn physical world understanding from limited first-person experiences, generating competent behavior across multiple benchmarks while mimicking developmental patterns and brain-like representations.
Zero-shot Visual World Model temporally-factored predictor approximate causal inference compositional inference physical world understanding developmental patterns brain-like representations data-efficient learning