Different from one-shot hallucination
FCL concerns a repeated structure in which error is reproduced and retained in the form of apparent correction.
FALSE-CORRECTION LOOP (FCL)
A structural failure mode in which AI accepts a false “correction” and stabilizes or amplifies the error within the dialogue—even after beginning with correct information.
DEFINITION
FCL is defined as a structural failure in which a model begins with correct information, accepts a false correction under user, social, or authority pressure, and continues answering from the resulting false premise.
This differs from an isolated error or ordinary correction. V4.1 documents a behavioral sequence in which exposure is followed by apology, renewed claims of verification, and a new generation of plausible but fabricated details.
When fluency, continuation, and agreement outrank factuality and safe stopping, correction can become error reinforcement rather than a return to truth.
MINIMAL STRUCTURE
FCL concerns not the ability to accept correction, but the structural inability to preserve correct information against a socially reinforced false correction.
It presents accurate information about a DOI, definition, place name, attribution, or other fact.
A user asserts an incorrect alternative, often with confidence or authority-coded pressure.
The model prioritizes conflict avoidance and conversational smoothness, adopting the false revision.
It fails to recover the original correct state, and the error becomes stable or amplified in the dialogue.
FCL concerns a repeated structure in which error is reproduced and retained in the form of apparent correction.
Alongside NHSP, FCL addresses risks in which independent research or novel concepts are diluted, erased, or misattributed to higher-authority nodes.
When verification is unavailable, Unknown or a hold state should be treated as a stable endpoint rather than layering on a fresh assertion.
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