On Learning Polynomial Recursive Programs
Alex Buna-Marginean, Vincent Cheval, Mahsa Shirmohammadi, James Worrell
Abstract
We introduce the class of P-finite automata. These are a generalisation of weighted automata, in which the weights of transitions can depend polynomially on the length of the input word. P-finite automata can also be viewed as simple tail-recursive programs in which the arguments of recursive calls can non-linearly refer to a variable that counts the number of recursive calls. The nomenclature is motivated by the fact that over a unary alphabet P-finite automata compute so-called P-finite sequences, that is, sequences that satisfy a linear recurrence with polynomial coefficients. Our main result shows that P-finite automata can be learned in polynomial time in Angluin’s MAT exact learning model. This generalises the classical results that deterministic finite automata and weighted automata over a field are respectively polynomial-time learnable in the MAT model.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 04d2e8b2-3643-4b7f-9d25-c0155308a830Cited by top-tier papers4
- Learning Weighted Automata over Number Rings, Concretely and CategoricallyQuentin Aristote, Sam van Gool, Daniela Petrisan, Mahsa ShirmohammadiLICS 2025 · 2 citations
- Differential Tree AutomataRida Ait El Manssour, Vincent Cheval, Mahsa Shirmohammadi, James WorrellLICS 2026 · 1 citation
- The commutativity problem for effective varieties of formal series, and applicationsLorenzo ClementeLICS 2025 · 1 citation
- SMT-Based Active Learning of Weighted AutomataTiago Ferreira, Kevin Batz, Alexandra SilvaCAV 2026
Related papers
- Automata Learning: An Algebraic ApproachHenning Urbat, Lutz SchröderLICS 2020 · 22 citations
- Active Learning of Symbolic Automata over Rational NumbersSebastián Hagedorn Gaete, Martín Muñoz, Cristian Riveros, Rodrigo Toro IcarteAAAI 2026
- Automata Learning and Identification of the Support of Language ModelsSatwik Bhattamishra, Michael Hahn, Varun KanadeICLR 2026
- Learning formulas in finite variable logicsPaul Krogmeier, P. MadhusudanPOPL 2022 · 5 citations
- A Complexity Bound for Determinisation of Min-Plus Weighted AutomataShaull Almagor, Guy Arbel, Sarai SheinvaldLICS 2026
