Neuro-Symbolic Verification of LLM Outputs: Hybrid Architecture Combines Formal and Neural Checks

arXiv·medium signal

Sigloch and Benzmüller present a hybrid verification architecture for LLM-generated content in high-stakes domains, combining formal symbolic reasoning for input verification with neural semantic analysis for output checking. The system provides complementary guarantees — logical completeness from symbolic methods and contextual understanding from neural methods — targeting hallucinations, inconsistencies, and privacy leaks in domains where errors carry legal or financial consequences.