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.

翻译：在高风险领域部署的大语言模型面临根本性的可靠性挑战：幻觉、不一致性以及隐私漏洞会带来不可接受的风险，其中错误可能引发法律、财务或安全后果。本文提出了一种混合验证架构，将形式化符号方法与神经语义分析相结合，为LLM生成的内容提供互补性保障。该架构采用逻辑推理进行输入验证，利用完备性特性对结构化需求提供可判定的保障。在输出验证方面，基于嵌入的语义相似性可检测形式化方法缺乏表达能力的上下文幻觉。这种分离通过并行的、基于参与者的流水线实现，解决了基于提示的自验证方法的局限性——后者继承了导致幻觉的分布偏差。本文提出的架构和类型感知验证方法通过HAIMEDA系统（基于行动设计研究开发的实际医疗设备损伤评估报告系统）进行验证。评估显示，对结构化实体的幻觉检测率超过83%，对语义捏造的检测率达72%，报告创建时间减少30%，这表明神经符号架构可为数据敏感领域的LLM部署提供原则性安全保障。