Scientific and Technical Intelligence (S&TI) analysis often involves verifying complex technical claims across a rapidly expanding body of literature. Current approaches frequently struggle to bridge the crucial verification gap between surface-level accuracy and deeper methodological validity.
Addressing this challenge, a novel framework named AutoVerifier has been introduced. This LLM-based agentic framework automates the end-to-end verification of technical claims without necessitating domain-specific expertise from its operators.
AutoVerifier's methodology involves decomposing every technical assertion into structured claim triples, formatted as (Subject, Predicate, Object). These triples are then used to construct knowledge graphs, facilitating structured reasoning across six progressively enriching layers:
- Corpus construction and ingestion
- Entity and claim extraction
- Intra-document verification
- Cross-source verification
- External signal corroboration
- Final hypothesis matrix generation
The framework was rigorously demonstrated on a contested quantum computing claim. Analysts, who possessed no prior quantum expertise, successfully utilized AutoVerifier to automatically pinpoint overclaims and metric inconsistencies within the target paper. It also traced cross-source contradictions and uncovered previously undisclosed commercial conflicts of interest, ultimately producing a comprehensive final assessment. These results underscore the capability of structured LLM verification to reliably evaluate the validity and maturity of emerging technologies, effectively transforming raw technical documents into traceable, evidence-backed intelligence assessments.