AutoVerifier: An Agentic Automated Verification Framework Using Large Language Models
Summary: arXiv:2604.02617v1 Announce Type: new
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.
The Framework
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
- Final hypothesis matrix generation
Application in Quantum Computing
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. The system was able to:
- Trace cross-source contradictions
- Uncover undisclosed commercial conflicts of interest
- Produce a final assessment of the claim’s validity
Significance of Findings
These results show that structured LLM verification can reliably evaluate the validity and maturity of emerging technologies. By turning raw technical documents into traceable, evidence-backed intelligence assessments, AutoVerifier represents a significant advancement in the field of automated verification.
Conclusion
In conclusion, AutoVerifier has the potential to revolutionize the way technical claims are verified in scientific literature. By leveraging large language models, it provides a framework that simplifies the verification process, making it accessible to analysts without specialized knowledge. This innovation could enhance the quality and reliability of scientific discourse, paving the way for more robust technological advancements.