Auditing and calibrating LLM-driven verification gates

Develop auditing protocols to calibrate large-language-model-driven quality gates used in verification and validation, incorporating adversarial probing, measurement of inter-agent disagreement, and evaluation against held-out physical measurements.

Background

The instrumentation pipelines rely on LLM-driven gates for quality control and verification steps. Ensuring these gates are reliable requires independent calibration and robust auditing procedures.

The authors highlight the need for adversarial testing and disagreement-based diagnostics, anchored by comparison to physical measurements, to validate and monitor these gate mechanisms.

References

Nine open questions will determine whether instrumented data matures into a recognised substrate for scientific machine learning. Verification of the verifier. Quality gates are LLM-driven; auditing their calibration requires adversarial probing, inter-agent disagreement, and held-out physical measurements.

— Instrumented data for causal scientific machine learning  (2606.07865 - Wilke, 5 Jun 2026) in Section 7, Methodological questions for the community, Item 3