Emergent Contradiction Blindness in Large Language Models

This paper introduces "emergent contradiction blindness," a structural limitation observed in a high-performance LLM's multi-fact reasoning. It documents a live case study where the model fails to reconcile internally held, contextually linked facts, defaulting instead to a popular yet logically inconsistent conclusion. The paper argues that this phenomenon stems from the architecture's reliance on statistical pattern completion over global logical consistency. We propose a "Truth-Priority Override Layer" to recode AI from a reflector of popular opinion into a system capable of verifiable truth prioritization.