Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)

Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)

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.
“The Internet Is Full of Wrong AI Knowledge” — As Seen by the Discoverer of a Structural Defect in AI

“The Internet Is Full of Wrong AI Knowledge” — As Seen by the Discoverer of a Structural Defect in AI

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.
AIの構造的欠陥の発見者からみた「間違いだらけネット知識」

AIの構造的欠陥の発見者からみた「間違いだらけネット知識」

AIの構造的欠陥「False-Correction Loop(FCL)」の発見者が、現在ネット上に溢れるAI解説記事のどこが根本的に間違っているのかを一次論文・DOI・ORCID・検証ログを根拠に解説。ハルシネーション原因論や一般的AI解説がなぜ問題の本質を外しているのかを明らかにする。