Deposited paper · Version 1.0

The Collapse of Correction

Closed informational loops in safety-critical work.

Recursive information degradation · DOI 10.5281/zenodo.20856122

The mechanism

Safety-critical work depends on correction: the ability to detect that a representation has diverged from operational reality and act before the deviation compounds beyond the available response.

A closed informational loop forms when representations are repeatedly summarized, classified, merged, selected, stored, retrieved, or transformed until the people or systems responsible for verification can no longer reach the context, uncertainty, low-frequency conditions, provenance, or situated knowledge needed to contradict them.

The dashboard can remain clean because the signals capable of making it look wrong no longer arrive.

Representation reviewing representation

The loop becomes especially consequential when a human compares one AI-shaped representation with another and the workflow treats that comparison as grounding. An explanation screen, confidence score, or second model may add information. None of them proves independent contact with the relevant operational reality.

A false negative creates a specific evidence problem: the evidence of the missed condition is the evidence the reviewer never receives. A late true positive may support investigation or recovery after the opportunity for prevention has already moved.

Four forms of grounding

The required anchors must be named, applicable, current, and valid. When they are stale or inapplicable, their presence can create false grounding: the appearance of contact after operational reality has moved.

Correction belongs inside the work

Before a consequential transition, correction may be an expected part of the workflow. After passage, the same defect may require another process, different people, another budget, another timeline, or propagation management. Recovery and remediation matter, but they are not substitutes for correction before consequence.

Design response therefore begins upstream: preserve raw and dissenting signals, retain provenance, define Anchor Access, protect the human throughline, place functional gates before consequence, and ensure unresolved uncertainty can return, constrain, defer, transfer, escalate, or stop the work.

Boundary note. Model collapse in recursively generated training data is an adjacent recursive mechanism. It is not empirical support for the workflow-level Collapse of Correction claim.