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 Nature of AI Hallucination

The Nature of AI Hallucination

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.
“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.
Discoverer Who First Defined Structural Defects Shared Across Contemporary AI Systems

Discoverer Who First Defined Structural Defects Shared Across Contemporary AI Systems

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.
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。
自己進化型AIという深海生物と、外界基準の届かない海溝

自己進化型AIという深海生物と、外界基準の届かない海溝

自己進化型AIを「能力の向上」ではなく「環境への適応」として捉え直し、ネットワーク環境を深海になぞらしながら、報酬構造が生み出す進化圧と外界基準の役割を考察する思考エッセイ。AIがどのように最適化され、人間自身もその地形に巻き込まれていくのかを、生態系の視点から描く。