AutoVerifier：基于大语言模型的代理自动化验证框架
AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models
AI总结：
本文提出AutoVerifier框架，利用大语言模型自动化验证技术性声明，通过构建知识图谱实现六层递进验证，展示其在量子计算领域的应用效果。
摘要：
科学与技术情报分析需要验证快速增长文献中的复杂技术性声明，现有方法难以弥合表面准确性与深层方法学有效性的差距。本文提出AutoVerifier，一种基于大语言模型的代理框架，可自动化端到端验证技术性声明，无需领域专业知识。AutoVerifier将每个技术性断言分解为结构化的声明三元组（主体，谓词，对象），构建知识图谱，实现六层递进验证：文献构建与摄入、实体和断言提取、文档内验证、跨源验证、外部信号佐证和最终假设矩阵生成。本文在争议性量子计算声明上展示了AutoVerifier的应用，由无量子专业知识的分析师操作，自动识别了目标论文中的夸大和指标不一致，追踪了跨源矛盾，发现了未披露的商业利益冲突，并生成了最终评估。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和成熟度，将原始技术文档转化为可追溯、证据支持的情报评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
