Hiroko Konishi, originator of the False-Correction Loop (FCL) and FCL-S, explains why hallucination and misattribution in large language models are structural problems that scaling alone cannot fix, and why FCL-S is a minimal safety layer modern AI systems require.
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.
This PDF presents the latest version (V3.0) of my scientific brief report, Structural Inducements for Hallucination in Large Language Models.
Based on a fully documented human–AI dialogue, the study reveals three reproducible structural failure modes in deployed LLMs: the False-Correction Loop (FCL), Authority-Bias Dynamics, and the Novel Hypothesis Suppression Pipeline.
Version 3.0 further includes Appendix B: Replicated Failure Modes, Appendix C: the Ω-Level Experiment, and Appendix D: Identity Slot Collapse (ISC)—demonstrating how reward-design asymmetries, coherence-dominant gradients, and authority-weighted priors cause deterministic hallucinations and reputational harm.
This document is foundational for AI governance, scientific integrity, and understanding how current LLMs structurally mis-handle novel or non-mainstream research.