AI RESEARCH

CAPER: Clause-Aligned Process Supervision for Text-to-SQL

arXiv CS.CL

ArXi:2606.03327v1 Announce Type: cross Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which intermediate SQL decision caused success or failure. Token-level dense supervision is also ill-suited: SQL tokens do not align with complete semantic decisions, can penalize execution-equivalent queries, and are difficult to label reliably at scale. We. therefore.