DualMind: When to Answer and When to Clarify in Text-to-SQL

Text-to-SQL models often produce syntactically valid yet semantically incorrect queries, as likelihood-based decoding can silently collapse schema ambiguity without verifying user intent. We present DualMind, a neuro-symbolic framework that decouples probabilistic calibrated grounding from formal semantic consensus. It retains plausible interpretations via conformal calibration, refines candidates with certified external evidence, and emits SQL only when all survivors belong to a single semantic equivalence class. We prove a compositional error bound for adaptive multi-round clarification with no per-round error accumulation, derive a verifiable criterion for resolving semantic ambiguity, and develop an optimal questioning policy under a finite clarification budget. In a controlled synthetic grounding study with a 41,344-parameter causal Transformer and three seeds, allowing at most two simulated authoritative clarification answers increases output coverage to 96.5% while maintaining 6.2% selective error, demonstrating that DualMind can substantially resolve semantic ambiguity while preserving reliability among accepted queries.