Abstract
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.
Abstract (translated)
科技与工程情报（S&TI）分析需要验证快速增长的文献中复杂的技术声明，而现有方法无法弥合表面准确性与深层方法学有效性之间的验证差距。我们提出了AutoVerifier——一个基于大语言模型的智能体框架，可在无需领域专家的情况下实现技术声明端到端的自动化验证。AutoVerifier将每项技术断言分解为（主体、谓语、客体）结构的声明三元组，构建支持六层递进式结构化推理的知识图谱：语料库构建与输入、实体与声明提取、文档内验证、跨源验证、外部信号佐证以及最终假设矩阵生成。我们在一个有争议的量子计算声明案例中验证了AutoVerifier：由无量子专业背景的分析师操作，该框架自动识别出目标论文中的过度宣称与指标不一致问题，追溯了跨源矛盾，发现了未披露的商业利益冲突，并生成了最终评估报告。这些结果表明，结构化大语言模型验证能够可靠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、有证据支撑的情报评估。
URL
https://arxiv.org/abs/2604.02617
