--> Computer Science > Formal Languages and Automata Theory arXiv:2604.02244 (cs) [Submitted on 2 Apr 2026 ( v1 ), last revised 3 Apr 2026 (this version, v2)] Title: (PAC-)Learning state machines from data streams: A generic strategy and an improved heuristic (Extended version) Authors: Robert Baumgartner , Sicco Verwer View a PDF of the paper titled (PAC-)Learning state machines from data streams: A generic strategy and an improved heuristic (Extended version), by Robert Baumgartner and Sicco Verwer View PDF HTML (experimental) Abstract: This is an extended version of our publication Learning state machines from data streams: A generic strategy and an improved heuristic, International Conference on Grammatical Inference (ICGI) 2023, Rabat, Morocco. It has been extended with a formal proof on PAC-bounds, and the discussion and analysis of a similar approach has been moved from the appendix and is now a full Section. State machine models are models that simulate the behavior of discrete event systems, capable of representing systems such as software systems, network interactions, and control systems, and have been researched extensively. The nature of most learning algorithms however is the assumption that all data be available at the beginning of the algorithm, and little research has been done in learning state machines from streaming data. In this paper, we want to close this gap further by presenting a generic method for learning state machines from data streams, as well as a merge heuristic that uses sketches to account for incomplete prefix trees. We implement our approach in an open-source state merging library and compare it with existing methods. We show the effectiveness of our approach with respect to run-time, memory consumption, and quality of results on a well known open dataset. Additionally, we provide a formal analysis of our algorithm, showing that it is capable of learning within the PAC framework, and show a theoretical improvement to increase run-time, without sacrificing correctness of the algorithm in larger sample sizes. Comments: Extended version of Learning state machines from data streams: A generic strategy and an improved heuristic, International Conference on Grammatical Inference (ICGI) 2023, Rabat, Morocco Subjects: Formal Languages and Automata Theory (cs.FL) ; Machine Learning (cs.LG) Cite as: arXiv:2604.02244 [cs.FL] (or arXiv:2604.02244v2 [cs.FL] for this version) https://doi.org/10.48550/arXiv.2604.02244 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Robert Baumgartner [ view email ] [v1] Thu, 2 Apr 2026 16:35:07 UTC (372 KB) [v2] Fri, 3 Apr 2026 09:40:09 UTC (372 KB) Full-text links: Access Paper: View a PDF of the paper titled (PAC-)Learning state machines from data streams: A generic strategy and an improved heuristic (Extended version), by Robert Baumgartner and Sicco Verwer View PDF HTML (experimental) TeX Source view license Current browse context: cs.FL < prev | next > new | recent | 2026-04 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )