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A graph-based inference framework is proposed for feedback-driven word deduction in the Jotto problem, generalizing to variable-length words and revealing a logarithmic convergence law validated through statistical tests.
This paper introduces Certification-Driven Reinforcement Learning (CDRL), a framework that leverages symbolic reasoning to generate reusable constraints for improving reinforcement learning in combinatorial search spaces, demonstrated in neutrino flavor model discovery with higher valid model rates and efficiency.