Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)
The paper discusses a new verification architecture for large language models (LLMs) used in sensitive domains. It combines formal symbolic methods with neural semantic analysis to enhance reliability and reduce risks associated with errors. The proposed method has shown promising results in detecting hallucinations and improving report creation efficiency in a medical device assessment system.
- ▪LLMs in high-stakes domains face reliability challenges such as hallucinations and privacy vulnerabilities.
- ▪The proposed hybrid verification architecture uses logical reasoning and semantic similarity for input and output validation.
- ▪Evaluation of the method demonstrated over 83% detection rates for structured entities and a 30% reduction in report creation time.
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Computer Science > Artificial Intelligence arXiv:2605.26942 (cs) [Submitted on 26 May 2026] Title:Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint) Authors:Paul Sigloch, Christoph Benzmüller View a PDF of the paper titled Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint), by Paul Sigloch and Christoph Benzm\"uller View PDF HTML (experimental) 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.
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