{"type":"ScholarlyArticle","title":"ReSyn: A Generalized Recursive Regular Expression Synthesis Framework","authors":[{"name":"Seongmin Kim","slug":"seongmin-kim","given":"Seongmin","family":"Kim","affiliation":""},{"name":"Hyunjoon Cheon","slug":"hyunjoon-cheon","given":"Hyunjoon","family":"Cheon","affiliation":""},{"name":"Su-Hyeon Kim","slug":"su-hyeon-kim","given":"Su-Hyeon","family":"Kim","affiliation":""},{"name":"Yo-Sub Han","slug":"yo-sub-han","given":"Yo-Sub","family":"Han","affiliation":""},{"name":"Sang-Ki Ko","slug":"sang-ki-ko","given":"Sang-Ki","family":"Ko","affiliation":""}],"abstract":"Existing Programming-By-Example (PBE) systems often rely on simplified benchmarks that fail to capture the high structural complexity of real-world regexes, such as deeper nesting and frequent use of union operations.\r\nTo overcome the resulting performance drop, we propose ReSyn, a synthesizer-agnostic divide-and-conquer framework that decomposes complex synthesis problem into manageable sub-problems.\r\nWe also introduce Set2Regex, a parameter-efficient synthesizer capturing the permutation invariance of examples.\r\nExperimental results demonstrate that ReSyn significantly boosts accuracy across various synthesizers, and its combination with Set2Regex establishes a new state-of-the-art on challenging real-world benchmark.\r\nThe complete source code, datasets, and pre-trained model checkpoints are publicly available at https://github.com/mrseongminkim/ReSyn.","doi":"10.24963/ijcai.2026/435","url":"https://ijcai.org/proceedings/2026/435","pdf":"https://ijcai.org/proceedings/2026/0435.pdf","markdown":"https://ijcai.org/proceedings/2026/435.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/435","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":435,"pages":{"first":3908,"last":3916},"keywords":["Knowledge Representation and Reasoning: Knowledge representation languages","Knowledge Representation and Reasoning: Computational complexity of reasoning","Knowledge Representation and Reasoning: Learning and reasoning","Knowledge Representation and Reasoning: Automated reasoning and theorem proving"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}