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

April 19, 2026 Β· Grace Period Β· πŸ› Findings of ACL 2026

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Authors Yuan Tian, Tianyi Zhang arXiv ID 2604.17653 Category cs.AI: Artificial Intelligence Cross-listed cs.DB Citations 0 Venue Findings of ACL 2026
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.
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