AI RESEARCH
RAS: Reflection-Augmented Scaling with In-Context Learning for Executable Cypher Query Generation
arXiv CS.CL
•
ArXi:2605.22937v1 Announce Type: new Inference-time scaling can reduce errors in structured query generation, but methods to allocate the compute for query code generation remains underexplored. We study Text2Cypher, where language models generate Cypher queries that execute against property graph databases. Non-executable queries constitute a distinct syntactic failure separate from semantic inaccuracy: a syntax error triggers a system-generated error message from the database.