Large language models (LLMs) are becoming deeply embedded in human communication and decision-making, yet they inherit the ambiguity, bias, and lack of direct access to truth inherent in language itself. While their outputs are fluent, emotionally resonant, and coherent, they are generated through statistical prediction rather than grounded reasoning. This creates the risk of hallucination, responses that sound convincing but lack factual validity. Building on Geoffrey Hinton's observation that AI mirrors human intuition rather than reasoning, this paper argues that LLMs operationalize System 1 cognition at scale: fast, associative, and persuasive, but without reflection or falsification. To address this, we introduce the Rose-Frame, a three-dimensional framework for diagnosing cognitive and epistemic drift in human-AI interaction. The three axes are: (i) Map vs. Territory, which distinguishes representations of reality (epistemology) from reality itself (ontology); (ii) Intuition vs. Reason, drawing on dual-process theory to separate fast, emotional judgments from slow, reflective thinking; and (iii) Conflict vs. Confirmation, which examines whether ideas are critically tested through disagreement or simply reinforced through mutual validation. Each dimension captures a distinct failure mode, and their combination amplifies misalignment. Rose-Frame does not attempt to fix LLMs with more data or rules. Instead, it offers a reflective tool that makes both the model's limitations and the user's assumptions visible, enabling more transparent and critically aware AI deployment. It reframes alignment as cognitive governance: intuition, whether human or artificial, must remain governed by human reason. Only by embedding reflective, falsifiable oversight can we align machine fluency with human understanding.

翻译：大型语言模型正日益深度嵌入人类沟通与决策过程，但其继承了语言本身固有的模糊性、偏见性及对真理缺乏直接访问能力的特性。尽管其输出流畅、富有情感共鸣且逻辑连贯，这些内容是通过统计预测而非基于现实的推理生成的。这导致了幻觉风险——即听起来令人信服但缺乏事实依据的回应。基于Geoffrey Hinton关于人工智能反映人类直觉而非推理的观察，本文认为大型语言模型实现了系统1认知的大规模操作化：快速、联想且具有说服力，但缺乏反思与证伪能力。为此，我们提出玫瑰框架——一个用于诊断人机交互中认知与认识漂移的三维框架。三个维度分别为：（i）地图与疆域，区分现实表征（认识论）与现实本身（本体论）；（ii）直觉与理性，借鉴双过程理论分离快速情绪化判断与缓慢反思性思维；（iii）冲突与确认，考察观点是通过分歧被批判性检验，还是仅通过相互验证被强化。每个维度对应独特的失效模式，其组合效应会放大认知错位。玫瑰框架不试图通过增加数据或规则来修正大型语言模型，而是提供一种反思工具，使模型局限性与用户预设同时显性化，从而实现更透明、更具批判意识的人工智能部署。该框架将对齐问题重构为认知治理：无论人类或人工智能的直觉，都必须受人类理性统辖。唯有嵌入可反思、可证伪的监督机制，我们才能使机器的流畅表达与人类的理解能力真正对齐。