[2604.02617] AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models

Computer Science > Artificial Intelligence arXiv:2604.02617 (cs) [Submitted on 3 Apr 2026] Title:AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models Authors:Yuntao Du, Minh Dinh, Kaiyuan Zhang, Ninghui Li View a PDF of the paper titled AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models, by Yuntao Du and 3 other authors View PDF HTML (experimental) 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...