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

About

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.

Yuan Tian, Tianyi Zhang• 2026

Related benchmarks

TaskDatasetResultRank
Text-to-SQLBIRD (dev)
Execution Accuracy (EA)63.62
387
Text-to-SQLSpider (dev)
EX75.55
147
Text-to-SQLSpider
Exec Acc (All)77.66
139
Text-to-SQLBird
Execution Accuracy (EX)65.12
63
Text-to-SQLBIRD-SQL Mini (dev)
Execution Accuracy (EX)63.8
21
Text-to-SQLBird
Execution Accuracy65.1
20
Text-to-SQLMini (dev)
Execution Accuracy (EX)63.8
9
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