CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment
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Computer Science > Computation and Language
Title:CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment
Abstract:Language models acting as agents over knowledge graphs generate Cypher queries that fail structurally (crashing at the database) or semantically (executing but returning wrong results). We place a pre-execution gate between query generation and a production Neo4j database. The gate validates structure through a four-backend chain culminating in execution against a mirror graph at 5.6 ms median latency. Structurally broken queries are routed to a corrector that iterates structured error feedback through a language model. On seven CypherBench schemas (2348 questions, ACL 2025) the pipeline maintains generation accuracy on every model tested, confirming it operates as a safe defensive layer. The corrector achieves 81% to 95% success across five models (mean 89%). On a template-generated corpus across nine schemas the gate catches 100% of parse errors, 100% of constraint violations, and 100% of schema-reference errors in path queries with labelled endpoints, at zero false positives across 1135 queries. Property sibling-swaps where the substituted name is valid on the target label score 0%, marking the formal boundary where structural validation ends and semantic validation must begin. A planner-based cost gate flags catastrophic plan structures before execution.
| Subjects: | Computation and Language (cs.CL); Databases (cs.DB) |
| Cite as: | arXiv:2606.04645 [cs.CL] |
| (or arXiv:2606.04645v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04645
arXiv-issued DOI via DataCite (pending registration)
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