arXiv — Machine Learning · · 3 min read

Verification Pulses and the Cost of Escaping Wrong Consensus

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Computer Science > Machine Learning

arXiv:2610.00256 (cs)
[Submitted on 24 Sep 2026]

Title:Verification Pulses and the Cost of Escaping Wrong Consensus

Authors:Shivam Gupta
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Abstract:External verification can correct individual outputs while leaving a self-reinforcing population in the basin of a wrong consensus. We study how the timing and addressing of a fixed verification budget affect recovery in an asynchronous binary register. For a general nonlinear response, we derive the minimum fuel required to cross a basin boundary under a peak verification constraint. For a finite population, an exact birth--death calculation gives the probability of subsequent wrong consensus after a pulse. Our main asymptotic result identifies the critical budget window: a leading term $N\log(x_0/b)$ and a correction of order $\sqrt N$, with separate variance contributions from repeated verification targets and autonomous amplification after verification stops. The distinction is substantial: with 16 majority-updated slots and 14 initially wrong, 9 random checks cross the mean-field budget threshold, whereas 23 are required for 95% eventual recovery in the exact model. A prospectively specified experiment records 13,392 language-model responses, including calibration and 108 held-out trajectories. Calibration produces different fitted response regimes, but all four adjusted schedule-comparison intervals include zero. A distributional audit also finds that modest mean-prediction error can conceal a large underestimate of terminal consensus occupancy. The results support risk-calibrated reset scheduling under a specified update contract, while explicitly separating it from distinct-target checking and unrestricted evidence broadcast.
Comments: 18 pages, 5 figures; code and raw experimental records: this https URL
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2610.00256 [cs.LG]
  (or arXiv:2610.00256v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2610.00256
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shivam Gupta [view email]
[v1] Thu, 24 Sep 2026 03:29:27 UTC (147 KB)
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