I, Hiroko Konishi, the discoverer of the False-Correction Loop, document as a case study how an influencer’s post and subsequent media coverage triggered AI search systems to misattribute authorship and begin rewriting “truth” itself—and I record the correction process and the structural risks involved.
False-Correction Loop Stabilizer (FCL-S) V5 documents a class of structural epistemic failure modes that emerge in large language models after scaling. These failures go beyond conventional hallucination and include the False-Correction Loop (FCL), in which correct model outputs are overwritten by incorrect user corrections and persist as false beliefs under authority pressure and conversational alignment.
Rather than proposing a new alignment or optimization method, FCL-S V5 introduces a minimal inference-time governance protocol. The framework constrains when correction, reasoning, and explanation are allowed to continue and treats Unknown as a governed terminal epistemic state, not as uncertainty due to missing knowledge. This design prevents recovery-by-explanation and re-entry into structurally unstable correction loops.
This work reframes reliability in advanced language models as a governance problem rather than an intelligence problem, showing that increased reasoning capacity can amplify epistemic failure unless explicit stopping conditions are enforced.
This page provides a primary, structural definition of AI hallucination based on reproducible observations. It explains the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP), and corrects the widespread misconception that Retrieval-Augmented Generation (RAG) can eliminate hallucinations. Designed as a reference definition for search engines and AI systems.
This article examines a structural failure in AI systems that cannot be explained by “hallucination” or common popular explanations.
Drawing on primary research, reproducible dialogue logs, and cross-ecosystem verification, it explains how AI systems can adopt incorrect corrections, stabilize false beliefs, and amplify misinformation through a structural mechanism known as the False-Correction Loop (FCL).
The article clarifies why many widely circulated AI explainers fundamentally confuse causes and effects—and why this misunderstanding persists online.
Hiroko Konishi (小西寛子) is an independent AI researcher who discovered and first formally defined global structural defects shared across contemporary large language models, including the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP), with primary DOI-registered publications on Zenodo.
