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Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings

Statistics Theory 2026-07-15 v1 Artificial Intelligence Machine Learning

Abstract

Serial verification gates are a core reliability primitive in LLM harnesses: a candidate answer is returned only if kk verifier calls all accept it. Under conditionally independent gates, the recent Odds Law (arXiv:2606.15712) shows that posterior log-odds grow linearly in kk, 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 αG\alpha \sim G (de Finetti), the exact cascade posterior is k=0lnmk\ell_k = \ell_0 - \ln m_k, with mkm_k the kk-th moment of GG. Then: (i) k\ell_k is concave in kk for every non-degenerate GG -- the Odds Law is its tangent at the first gate and an upper bound; (ii) for Beta(a,b)(a,b) latents, failure decays polynomially, 1rkkb1-r_k \asymp k^{-b}, with correlation parameter ρv=1/(a+b+1)\rho_v = 1/(a+b+1); (iii) a blind-spot atom of mass 1π1-\pi at α=1\alpha=1 caps the evidence extractable from any number of gates at ln(1π)-\ln(1-\pi) nats, so reliability saturates below 1; (iv) letting the true-accept rate also vary (βH\beta \sim H) yields a trichotomy -- gates eventually always help, plateau, or actively harm -- decided by the upper-tail exponents of GG and HH, with closed-form crossover kk^\dagger. The mechanism is survivorship: errors surviving gates are the high-α\alpha ones. The theory is measurable: RR repeated verdicts per instance identify the first RR moments of GG, so two verdicts identify ρv\rho_v; 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 k=5k=5 and ~3000x at k=10k=10; the correlated fit at R=8R=8 tracks held-out depths. The practical lever is decorrelation -- changing model family, modality, or evidence source -- not adding gates.

Keywords

Cite

@article{arxiv.2607.13918,
  title  = {Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings},
  author = {Jiangang Han},
  journal= {arXiv preprint arXiv:2607.13918},
  year   = {2026}
}

Comments

14 pages, 2 figures. Code and synthetic-recovery experiments: https://github.com/jianganghan/harness-verifier-cascades