@inproceedings{tian-zhang-2026-pv,
    title = "{PV}-{SQL}: Synergizing Database Probing and Rule-based Verification for Text-to-{SQL} Agents",
    author = "Tian, Yuan  and
      Zhang, Tianyi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1286/",
    pages = "25827--25845",
    ISBN = "979-8-89176-395-1",
    abstract = "Text-to-SQL systems often struggle with deep contextual understanding, particularly for complex queries with subtle requirements.We present **PV-SQL**, an agentic framework that addresses these failures through two complementary components: **P**robe and **V**erify. The *Probe* component iteratively generates probing queries to retrieve concrete records from the database, resolving ambiguities in value formats, column semantics, and inter-table relationships to build richer contextual understanding. The *Verify* component employs a rule-based method to extract verifiable conditions and construct an executable checklist, enabling iterative SQL refinement that effectively reduces missing constraints. Experiments on the BIRD benchmarks show that PV-SQL outperforms the best text-to-SQL baseline by 5{\%} in execution accuracy and 20.8{\%} in valid efficiency score while consuming fewer tokens."
}