mlsanityData to 14 Oct 2026

Verification Pulses and the Cost of Escaping Wrong Consensus

arXiv 2610.00256 ↗First posted 24 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

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 Nlog(x0/b) and a correction of order √ 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 24 Sep 2026.

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References · 6

  1. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  2. 2026Consensus is Not Verification: Why Crowd Wisdom Strategies Fail for LLM Truthfulnesscited by 9
  3. 2026Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agentscited by 6
  4. 2024Steering opinion dynamics through control of social networkscited by 2
  5. 2021Phase transition of the 3-majority opinion dynamics with noisy interactionscited by 1
  6. 2022Optimal control in opinion dynamics models: towards a unified frameworkcited by 1

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