English

PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents

Artificial Intelligence 2026-04-21 v1 Databases

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: Probe and Verify. 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.

Keywords

Cite

@article{arxiv.2604.17653,
  title  = {PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents},
  author = {Yuan Tian and Tianyi Zhang},
  journal= {arXiv preprint arXiv:2604.17653},
  year   = {2026}
}

Comments

Accepted to Findings of ACL 2026

R2 v1 2026-07-01T12:17:20.173Z