Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)
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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
Posted inAI AI倫理・ガバナンス ANALOGシンガーソング

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.
Posted inAI AI倫理・ガバナンス ANALOGシンガーソング

Structural Inducements for Hallucination in Large Language Models (V4.1):Cross-Ecosystem Evidence for the False-Correction Loop and the Systemic Suppression of Novel Thought

This paper provides an output-only case study revealing the existence of structurally induced epistemic failures in Large Language Models (LLMs), including the reproducible False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP). Through cross-ecosystem evidence (Model Z, Grok, and Yahoo! AI Assistant), the study demonstrates that current reward architectures prioritize conversational coherence and authority-biased attribution over factuality, leading to systemic hallucination and the suppression of novel, independent research. The paper concludes by proposing a multi-layer governance architecture for structural mitigation.