A Second Model Is Not a Second Opinion: Peer Verification, Correlated Failure, and the Independence Variable Nobody Reports

Abstract

When a language model cannot reliably detect its own errors, the standard remedy is to have a second model check the first. That substitution is now the default across agent benchmarks, reward pipelines and safety evaluations, and it rests on an assumption that is rarely stated and that, in the agent-verification papers surveyed here, is never measured: that the checker's errors are independent of the checked system's. This paper separates two things the phrase "peer verification" conflates - adding an evaluator instance, and adding an independent signal - and argues that only the second is what the justification requires. Read through that distinction, a literature that appears to disagree about whether judge panels help turns out to be consistent: aggregation reliably reduces idiosyncratic and adversarial error and reliably fails against common-mode error, and the two camps are measuring different failure distributions. The evidence further indicates that independence is partially recoverable, but along axes the field mostly does not vary - different model family, different evidence channel, non-neural verifier, and, most cheaply and most neglected, a protocol that denies evaluators a shared context before they commit. This paper is the author's analysis and synthesis of the published evidence. It states what the published record establishes, states flatly what it does not, proposes an instrument for the missing quantity, and names five experiments that would settle the open part.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 72 papers by Pranay Mahendrakar, or his ORCID record.