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CYGNET is a pre-execution gate system that validates and corrects Cypher queries generated by LLM agents over knowledge graphs, catching structural failures before they hit production databases with near-zero false positives and achieving 81–95% success in repairing broken queries across five language models.
This paper introduces Reflection-Augmented Scaling (RAS), a method that uses execution feedback from failed Cypher queries to iteratively refine query generation via in-context learning, reducing execution error rates by 41-50% across multiple datasets and models.