AI RESEARCH
Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference
arXiv CS.LG
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ArXi:2602.06104v2 Announce Type: replace Many engineering and scientific workflows rely on expensive black-box evaluations, requiring sequential decisions that must both improve task performance and reduce uncertainty. Bayesian optimization (BO) and Bayesian experimental design (BED) provide powerful but largely separate treatments of goal-directed optimization and information-seeking experimentation, leaving limited guidance for hybrid settings in which learning and optimization are intrinsically coupled.