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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26942 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | loXAX3RCBzUU |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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