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

2026-05-26

AI Summary

This study addresses the critical risks posed by large language models (LLMs) in high-stakes, data-sensitive domains—particularly hallucinations, inconsistency, and privacy breaches. To mitigate these issues, the authors propose a hybrid verification architecture that integrates formal symbolic reasoning with neural semantic analysis. The framework employs logical rules to validate input completeness and leverages embedding-based semantic similarity to detect contextual hallucinations in model outputs. Innovatively decoupling input validation from output verification, the approach discards prompt-based self-consistency checks vulnerable to distributional shifts and instead introduces a type-aware neuro-symbolic validation strategy. Furthermore, it implements an Actor-model-based parallel verification pipeline. Evaluated on HAIMEDA, a real-world medical device damage assessment system, the method achieves over 83% detection accuracy for structured entity hallucinations, 72% for semantic fabrications, and reduces report generation time by 30%.

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.

Problem

• hallucinations
• reliability
• privacy vulnerabilities
• data-sensitive domains
• large language models

Innovation

• Neuro-Symbolic Verification
• Hallucination Detection
• Formal Methods
• Semantic Similarity
• Actor-based Pipeline