Problem
Research questions and friction points this paper is trying to address.
Scientific and Technical Intelligence
technical claim verification
verification gap
methodological validity
emerging technologies
AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models
🤖 AI Summary
This study addresses the challenge of verifying complex technical claims in scientific and technological intelligence analysis by proposing an agent-based framework powered by large language models, which enables fully automated, end-to-end validation without requiring domain experts. The approach parses technical assertions into subject–predicate–object triples to construct a knowledge graph and integrates multi-source cross-validation, external signal corroboration, and conflict-of-interest detection across six progressive levels of scrutiny. Demonstrated in a quantum computing case study, the system effectively uncovers exaggerated statements, inconsistent metrics, cross-source contradictions, and undisclosed commercial interests, producing traceable and reliable technology readiness assessments. This significantly narrows the verification gap between surface-level accuracy and deep methodological validity.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic verification
Large Language Models
Claim decomposition
Knowledge graph reasoning
Cross-source validation
🔎 Similar Papers
No similar papers found.
