BabyTheorist: A Benchmark for Learning to Theorize the World from Observation Alone

Authors: Doojin Baek , Junyeob Baek , Mingyu Jo , Hosung Lee , Taegu Kang , Gyubin Lee , Sungjin Ahn

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Abstract: How would we know whether a machine truly understands the world? We argue that one key capability is \textit{observational theory learning}: acquiring reusable theories from pure observations and applying them to new situations. Current visual reasoning benchmarks focus primarily on the latter, presenting each task as a few-shot puzzle grouped by a common rule. We introduce BabyTheorist, a benchmark for controlled training and evaluation of observational theory learning. BabyTheorist generates continual streams of before-after visual observations from hidden compositional programs; during training, learners see only the resulting transitions, with no program labels, task identities, or support sets. To support theory learning and evaluation, BabyTheorist controls the program language, a curriculum over program complexity, recurring frequent subprograms, and a continual train-test protocol with level skipping. Seven representative baselines plateau well below ceiling, with the gap widening as the curriculum advances---evidence that BabyTheorist isolates a capability current program-learning approaches do not yet address.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.08177 [cs.AI]

(or

arXiv:2611.08177v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.08177

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arXiv-issued DOI via DataCite (pending registration)

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From: Junyeob Baek [ view email ]

[v1]

Thu, 5 Nov 2026 15:33:32 UTC (7,293 KB)

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