Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

• URL: http://arxiv.org/abs/2605.26942v2
• Date: Fri, 29 May 2026 12:41:29 GMT
• Title: Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)
• Abstract summary: This paper presents a hybrid verification architecture combining formal symbolic methods with neural semantic analysis.<n>For output validation, embedding-based semantic similarity detects contextual hallucinations.<n> Evaluation shows hallucination detection rates of over 83% for structured entities and 72% for semantic fabrications.
• Score: 0.08594140167290099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce unacceptable risks where errors carry legal, financial, or safety consequences. This paper presents a hybrid verification architecture combining formal symbolic methods with neural semantic analysis to provide complementary guarantees for LLM-generated content. This architecture employs logical reasoning for input verification, leveraging completeness properties to provide decidable guarantees on structured requirements. For output validation, embedding-based semantic similarity detects contextual hallucinations where formal methods lack expressiveness. This separation is realized in a parallel, actor-based pipeline, addressing limitations of prompt-based self-verification approaches, which inherit the distributional biases that produce hallucinations. The proposed architecture and type-aware verification method are validated with HAIMEDA, a real-world medical device damage assessment reporting system developed through Action Design Research. Evaluation shows hallucination detection rates of over 83% for structured entities and 72% for semantic fabrications, with a 30% reduction in report creation time, demonstrating that neuro-symbolic architectures can provide principled safeguards for LLM deployment in data-sensitive domains.

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