high-stakes-domains

This paper presents a neuro-symbolic verification architecture for LLM outputs in high-stakes domains, combining formal symbolic methods with neural semantic analysis. Evaluated on a medical device damage assessment system, it achieves over 83% hallucination detection for structured entities and 30% reduction in report creation time.