Study Finds 25-Verifier AI Panel Offers No More Reliability Than a Single Reviewer

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A researcher behind the open-source project IDKMesh tested whether adding more AI verifiers to a review panel increases the reliability of evaluations. In Experiment E017, a 25-verifier panel was built using programmatic test oracles drawn from five distinct input regions, run across 72 candidates with known ground truth. Despite each individual verifier performing meaningfully above chance, error correlation between verifiers from different regions averaged 0.53, meaning they shared more than half their mistakes. The panel's majority-vote error rate of 20.83% was nearly identical to a single verifier's 20.44%, yielding a measured effective panel size of just 1.00. The findings suggest that adding reviewers does not increase independent evidence when those reviewers share correlated errors, challenging a core assumption behind multi-judge AI evaluation systems.

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