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This paper introduces a computational model that uses probabilistic reasoning over language and code to simulate human inductive learning and active inquiry, outperforming pure LLMs and classic Bayesian models in behavioral studies.
This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model to formalize how design options are valued based on their expected reduction of epistemic uncertainty, and validates it through simulations and human experiments in a graph-shape guessing task.