Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to bridge the verification gap between surface-level accuracy and deeper methodological validity. We present AutoVerifier, an LLM-based agentic framework that automates end-to-end verification of technical claims without requiring domain expertise. AutoVerifier decomposes every technical assertion into structured claim triples of the form (Subject, Predicate, Object), constructing knowledge graphs that enable structured reasoning across six progressively enriching layers: corpus construction and ingestion, entity and claim extraction, intra-document verification, cross-source verification, external signal corroboration, and final hypothesis matrix generation. We demonstrate AutoVerifier on a contested quantum computing claim, where the framework, operated by analysts with no quantum expertise, automatically identified overclaims and metric inconsistencies within the target paper, traced cross-source contradictions, uncovered undisclosed commercial conflicts of interest, and produced a final assessment. These results show that structured LLM verification can reliably evaluate the validity and maturity of emerging technologies, turning raw technical documents into traceable, evidence-backed intelligence assessments.

翻译：科学技术情报（S&TI）分析要求对快速增长的文献中复杂的技术主张进行验证，而现有方法无法弥合表层准确性验证与深层方法论有效性验证之间的鸿沟。我们提出AutoVerifier——一种基于大语言模型的智能体框架，无需领域专业知识即可自动完成技术主张的端到端验证。AutoVerifier将每项技术断言分解为结构化的三元组主张（主体，谓词，客体），构建知识图谱以实现六个逐步深化的层次的结构化推理：语料库构建与导入、实体与主张抽取、文档内验证、跨源验证、外部信号佐证及最终假设矩阵生成。我们针对一个有争议的量子计算主张展示AutoVerifier：由无量子专业知识的分析师操作的该框架，自动识别了目标论文中的过度声明和指标不一致性，追溯了跨源矛盾，揭示了未披露的商业利益冲突，并生成了最终评估。这些结果表明，结构化的大语言模型验证能够可靠地评估新兴技术的有效性与成熟度，将原始技术文档转化为可追溯、有证据支持的情报评估。