Pragmatic Curiosity: A Hybrid Learning-Optimization Paradigm via Active Inference

• URL: http://arxiv.org/abs/2602.06104v1
• Date: Thu, 05 Feb 2026 18:42:29 GMT
• Title: Pragmatic Curiosity: A Hybrid Learning-Optimization Paradigm via Active Inference
• Authors: Yingke Li, Anjali Parashar, Enlu Zhou, Chuchu Fan,
• Abstract summary: "pragmatic curiosity" is a hybrid learning-optimization paradigm derived from active inference.<n>We demonstrate the practical effectiveness and flexibility of pragmatic curiosity on various real-world hybrid tasks.
• Score: 20.135421015458817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many engineering and scientific workflows depend on expensive black-box evaluations, requiring decision-making that simultaneously improves performance and reduces uncertainty. Bayesian optimization (BO) and Bayesian experimental design (BED) offer powerful yet largely separate treatments of goal-seeking and information-seeking, providing limited guidance for hybrid settings where learning and optimization are intrinsically coupled. We propose "pragmatic curiosity," a hybrid learning-optimization paradigm derived from active inference, in which actions are selected by minimizing the expected free energy--a single objective that couples pragmatic utility with epistemic information gain. We demonstrate the practical effectiveness and flexibility of pragmatic curiosity on various real-world hybrid tasks, including constrained system identification, targeted active search, and composite optimization with unknown preferences. Across these benchmarks, pragmatic curiosity consistently outperforms strong BO-type and BED-type baselines, delivering higher estimation accuracy, better critical-region coverage, and improved final solution quality.

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