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    <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.</abstract>
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%0 Conference Proceedings
%T PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents
%A Tian, Yuan
%A Zhang, Tianyi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tian-zhang-2026-pv
%X 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.
%R 10.18653/v1/2026.findings-acl.1286
%U https://aclanthology.org/2026.findings-acl.1286/
%U https://doi.org/10.18653/v1/2026.findings-acl.1286
%P 25827-25845

Markdown (Informal)

[PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents](https://aclanthology.org/2026.findings-acl.1286/) (Tian & Zhang, Findings 2026)

• PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents (Tian & Zhang, Findings 2026)

ACL

• Yuan Tian and Tianyi Zhang. 2026. PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25827–25845, San Diego, California, United States. Association for Computational Linguistics.