IntroductionOver the past year, large language models have grown dramatically in size, cost, and deployment scope. Yet, recent evaluations reveal a troubling pattern: increased scale has not reliably produced more stable or trustworthy reasoning. Independent testing and academic analysis have shown that even state-of-the-art models continue to hallucinate, contradict themselves, and fail under adversarial or Read.