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.