AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models

arXiv cs.AI / 4/6/2026

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Key Points

AutoVerifier is an LLM-based, agentic framework designed to automate end-to-end verification of complex technical claims in rapidly expanding scientific literature.
It breaks each claim into structured (Subject, Predicate, Object) triples, builds knowledge graphs, and performs verification through six progressively richer layers from corpus ingestion to final hypothesis-matrix generation.
The system targets the gap between surface-level correctness and deeper methodological validity by combining intra-document checks, cross-source consistency checks, and external signal corroboration.
In a demonstration on a contested quantum computing claim, analysts without quantum expertise used AutoVerifier to detect overclaims, metric inconsistencies, cross-source contradictions, and potential undisclosed commercial conflicts of interest.
The authors argue that this structured LLM verification approach can turn raw technical documents into traceable, evidence-backed intelligence assessments for emerging technologies.

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.