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cs.LG updates on arXiv.org

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SMT-Based Active Learning of Weighted Automata
Tiago Ferrei · 2026-05-11 · via cs.LG updates on arXiv.org

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Abstract: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. Our extensive experimental evaluation shows that our algorithm is capable of learning numerous minimal WFAs over both finite and infinite semirings, vastly outperforms a naive baseline, and is competitive with a state-of-the-art algorithm while producing significantly smaller automata and requiring less interaction with the teacher.
Comments: Appearing in CAV 2026
Subjects: Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)
Cite as: arXiv:2605.07758 [cs.FL]
  (or arXiv:2605.07758v1 [cs.FL] for this version)
  https://doi.org/10.48550/arXiv.2605.07758

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tiago Ferreira [view email]
[v1] Fri, 8 May 2026 14:01:29 UTC (1,932 KB)