Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)
A new preprint (arXiv:2605.26942) introduces a neuro-symbolic verification architecture for LLM outputs in data-sensitive domains like healthcare and finance. The system uses formal logical reasoning for input verification, providing decidable guarantees on structured requirements, and embedding-based semantic similarity for output validation to detect contextual hallucinations where formal methods fall short. This separation is implemented in a parallel, actor-based pipeline to avoid the distributional biases of prompt-based self-verification. The approach is validated on HAIMEDA, a real-world medical device damage assessment reporting system developed through Action Design Research. Evaluation shows improved hallucination detection rates, though specific numbers are not provided in the abstract. The work addresses fundamental reliability challenges—hallucinations, inconsistencies, and privacy vulnerabilities—that pose legal, financial, or safety risks in high-stakes deployments.
Enables safer LLM deployment in regulated industries by combining formal guarantees with neural detection.