Summary
A position paper by systems engineer George Andrikopoulos (arXiv 2608.19125) argues that the loss of expert corrections in LLM sessions is an operations problem rather than a tooling problem. While mechanisms for persisting corrections exist, the governance discipline—versioning with provenance, recurrence monitoring, counter-metrics, and retirement of stale rules—is missing. Drawing on thirty years of systems engineering, the author maps the LLM stack onto familiar machine components: frozen silicon, firmware, loadable modules, persistent configuration, and volatile memory. He identifies where the mapping breaks down: stochastic generation, configurations that bind only probabilistically, and the absence of a general-purpose retirement (verification) stage by default. From these failures he derives a seven-principle operating discipline centered on the error loop. Three case studies from the author's own practice illustrate the mechanisms, including one where a control silently became the exact harm it was designed to prevent. The paper closes with a measurement framework implied by this model and a proposal for laboratory studies to test it.
Paper Overview
Field: Machine Learning
Author: George Andrikopoulos
Published: 2026-08-19
arXiv: 2608.19125
Summary
When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. The author argues this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them—versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules—does not.
Writing as a systems engineer of thirty years, the author maps the LLM stack onto the machines his profession already operates:
From these failures, he derives a seven-principle operating discipline centered on the error loop. Three cases from his own practice illustrate the mechanisms, including one where a control silently became the exact harm it was intended to prevent. The paper concludes with the measurement framework implied by this position and the laboratory research needed to test it.
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Original abstract:
> When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this is an operations problem, not a tooling problem: mechanisms for persisting corrections exist and are shipping, but the discipline for governing them -- versioning with provenance, recurrence monitoring, counter-metrics, retirement of stale rules -- does not.
*Auto-collected on 2026-08-21.*
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