Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings
Abstract
Serial verification gates are a core reliability primitive in LLM harnesses: a candidate answer is returned only if $k$ verifier calls all accept it. Under conditionally independent gates, the recent Odds Law (arXiv:2606.15712) shows that posterior log-odds grow linearly in $k$, so failure decays exponentially, and states that "a tight theory of partially correlated verifier cascades remains open." This note gives a minimal such theory. Modeling the per-instance false-accept rate on the generato...
Description / Details
Serial verification gates are a core reliability primitive in LLM harnesses: a candidate answer is returned only if verifier calls all accept it. Under conditionally independent gates, the recent Odds Law (arXiv:2606.15712) shows that posterior log-odds grow linearly in , so failure decays exponentially, and states that "a tight theory of partially correlated verifier cascades remains open." This note gives a minimal such theory. Modeling the per-instance false-accept rate on the generator's own errors as a latent variable (de Finetti), the exact cascade posterior is , with the -th moment of . Then: (i) is concave in for every non-degenerate -- the Odds Law is its tangent at the first gate and an upper bound; (ii) for Beta latents, failure decays polynomially, , with correlation parameter ; (iii) a blind-spot atom of mass at caps the evidence extractable from any number of gates at nats, so reliability saturates below 1; (iv) letting the true-accept rate also vary () yields a trichotomy -- gates eventually always help, plateau, or actively harm -- decided by the upper-tail exponents of and , with closed-form crossover . The mechanism is survivorship: errors surviving gates are the high- ones. The theory is measurable: repeated verdicts per instance identify the first moments of , so two verdicts identify ; beta-binomial likelihood and NPMLE recover the reliability curve and the ill-posed ceiling. In synthetic tests, independence-based extrapolation underestimates failure by 20x at and ~3000x at ; the correlated fit at tracks held-out depths. The practical lever is decorrelation -- changing model family, modality, or evidence source -- not adding gates.
Source: arXiv:2607.13918v1 - http://arxiv.org/abs/2607.13918v1 PDF: https://arxiv.org/pdf/2607.13918v1 Original Link: http://arxiv.org/abs/2607.13918v1
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Jul 16, 2026
Artificial Intelligence
AI
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