SafeQL repairs 87.4% of broken AI queries

KAIST’s SafeQL repaired up to 87.4% of initially erroneous text-to-SQL queries on BIRD, improved execution accuracy by 5.8 percentage points, and cut token use by up to 15.1× versus full-query regeneration, according to KAIST’s research release and the SafeQL paper. It uses PostgreSQL execution feedback to repair only the faulty relation, attribute, function, or value instead of rewriting the whole query. Teams building text-to-SQL agents should test component repair before reaching for larger models, alongside the archive’s analysis of agent-search economics.