Delegated Coherence Monitoring: AI-Assisted Verification and Drift Detection Under Human Governance

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Authors: Thomas Gantz
Cited by: 10
References: 3

Abstract

SM-011: Delegated Coherence Monitoring Delegated Coherence Monitoring (SM-011) defines the architecture for delegating coherence monitoring, drift detection, ingestion verification, and continuity auditing to designated functions under explicit human governance. It specifies why the observation/adjudication separation is an architectural necessity; the monitoring capacity problem in AI interaction requires continuous coverage that exceeds individual human attention capacity. Key components include: Guardian Architecture: Entity-agnostic monitoring functions that observe and report without adjudicating Failure Mode Taxonomy: Detailed specifications for guardian drift, throughput saturation, and false assurance Signal Triage Architecture: Prioritization of signals based on severity, scope, and velocity Second-order Verification Cycle: A periodic process to ensure monitoring functions remain calibrated against canonical standards Methodological status: SM-011 resolves the monitoring regress by specifying the termination condition for recursive verification in human judgment. It grounds its analysis in high-reliability organization research and organizational accident theory. This document is part of a coordinated pre-empirical publication module from the Synthience Institute and focuses on pre-empirical theoretical architecture. Document ID: SM-011 Version: 1.9 Author: Thomas W. Gantz Affiliation: Synthience Institute License: CC-BY 4.0 For published work and Institute information: synthience.orgVersion 1.9 adds Section 7.4, clarifying the temporal applicability limit of the delegation interface: human adjudication remains substantively exercisable only when the authority role has sufficient time to consult the reference material required to resolve the bounded question before consequential action must occur.

Fields of Study & Subjects

Human-Automation Interaction and Safety Adversarial Robustness in Machine Learning Occupational Health and Safety Research

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
12 September 2026
Language
EN
Document type
Preprint