AI RESEARCH
SMT-Based Active Learning of Weighted Automata
arXiv CS.LG
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ArXi:2605.07758v1 Announce Type: cross We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods. Our algorithm is parametric in a given semiring and, if it terminates, guaranteed to produce minimal WFAs. We prove partial correctness and provide a sufficient termination condition, which in particular implies termination for all finite semirings.