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The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring

Abdou-Raouf Atarmla

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Source: arXiv

Published: Sep 19, 2026

arXiv: 2609.23254

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Source abstract

Self-calibrating monitors adapt their threshold online to guarantee a prescribed long-run false-alarm rate under arbitrary drift. We compute the price of that guarantee, stating every law with its exact domain of validity. First, the guarantee is an accounting identity, insensitive to what the monitor is meant to detect. Two evidence identities make the cost exact for the online quantile tracker: a persistent step of height δδ yields excess alarm mass within one alarm of δ/ηδ/η, and exactly δ/ηδ/η pathwise when δδ is a lattice multiple of the gain ηη; a ramp of slope cc yields a stationary excess rate of exactly c/ηc/η, independent of accumulated size, up to a boundary c=η(1α)c=η(1-α) coinciding with the alarm-rate cap. Second, the certificate's own fluctuation obeys an exact law: the windowed alarm rate has standard deviation of order 1/L1/L, not the binomial 1/L1/\sqrt{L}, since the windowed mass telescopes to a difference of a tight internal state; the closed-form constant is validated with no fitted parameter. Detectors calibrated on the binomial scale are miscalibrated by ηφ(q0)L\sqrt{η\varphi(q_0)L}, and correct calibration turns detection windows from quadratic to linear in the inverse fault speed. Third, any monitor required to tolerate a drift class D\mathcal{D} is blind, at any horizon and for any rule, to every fault in DD\mathcal{D}-\mathcal{D}; the proof is a deliberately elementary two-point argument and the contribution is the object it identifies: for speed-bounded classes the blind set is exactly the doubled-speed class, and the tracker absorbs a speed class fixed by its own gain, so that under a certification regime declaring absorbed drift normal, the monitor manufactures D\mathcal{D}. An exact Gaussian projection bound, sharper than Pinsker and never vacuous, quantifies power outside it.

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The Price of Self-Calibration: Exact Evidence Budgets and Manufactured Blind Sets in Adaptive Monitoring — Mathematical Frontier Network