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Introduces ZendoWorld, a controlled interactive environment for evaluating AI agents on active visual concept induction, where agents must perceive scenes, infer hidden logical rules, and design informative experiments. Experiments with various agent classes reveal that high prediction accuracy does not guarantee rule recovery, and VLM-based agents struggle with informative experimentation, highlighting gaps compared to human inductive reasoning.