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

Researchers present a hybrid neuro-symbolic architecture that combines formal logic with neural semantic analysis to verify LLM outputs in high-stakes domains like healthcare. The system achieves over 83% hallucination detection rates for structured data and 72% for semantic fabrications while reducing report creation time by 30%, demonstrating practical safeguards for deploying LLMs in data-sensitive applications.

The reliability of large language models remains a critical barrier to enterprise and institutional adoption, particularly in sectors where accuracy directly impacts legal liability, financial risk, or patient safety. This research addresses a fundamental tension in LLM deployment: while these models excel at generating contextually coherent text, they remain prone to hallucinations—confident assertions of false information that appear plausible to human readers. Traditional prompt-based verification approaches prove insufficient because they rely on the same underlying model that produced the errors, perpetuating inherent distributional biases.

The proposed neuro-symbolic architecture separates concerns strategically. Formal symbolic methods provide decidable guarantees for structured requirements, operating with mathematical certainty on defined logical constraints. Neural semantic analysis complements this by detecting contextual hallucinations that resist formal specification—subtle inconsistencies or fabricated details that violate real-world domain knowledge. By executing these verification layers in parallel through an actor-based pipeline rather than sequentially through prompts, the system avoids compounding errors.

The validation through HAIMEDA, a medical device damage assessment system, grounds the research in practical reality. Achieving 83% detection rates for structured entity hallucinations and 72% for semantic fabrications represents meaningful progress, though imperfect accuracy warrants ongoing caution. The 30% reduction in report creation time suggests that rigorous verification doesn't necessitate cumbersome overhead—potentially enabling faster deployment without sacrificing reliability.

This work signals growing institutional investment in making LLMs trustworthy for regulated domains. Healthcare, finance, and legal sectors increasingly require not just capable AI systems but verifiable ones. The success of hybrid approaches may drive adoption of similar architectures across other data-sensitive industries, fundamentally shifting expectations around LLM reliability and governance.

• →Neuro-symbolic verification combining formal logic and neural analysis detects over 83% of structured hallucinations and 72% of semantic fabrications in LLM outputs
• →Parallel actor-based pipeline architecture avoids the limitations of prompt-based self-verification by eliminating reliance on the same biased model that generated errors
• →Real-world deployment in medical device assessment reduced report creation time by 30% while improving reliability, demonstrating practical feasibility
• →Hybrid verification enables LLM deployment in high-stakes domains like healthcare and finance where error consequences carry legal and safety implications
• →Formal methods provide decidable guarantees for structured requirements while neural embeddings capture contextual semantic violations that logic alone cannot express