As large language models (LLMs) become integrated into everyday and high-stakes decision-making, they inherit the ambiguity and biases of human language. While they produce fluent and coherent outputs, they rely on statistical pattern prediction rather than grounded reasoning, creating a risk of outputs that are plausible but incorrect. This paper argues that these failures are not only technical but cognitive. LLMs reproduce associative patterns similar to intuitive human reasoning, amplifying systematic misinterpretations when combined with human users. To analyse this, we introduce the Rose-Frame, a cognitive-epistemological framework for diagnosing breakdowns in human-AI interaction. The framework identifies three recurrent traps: (i) map vs territory, distinguishing representations from reality; (ii) intuition vs reason, separating fast associative judgments from reflective reasoning; and (iii) conflict vs confirmation, examining whether ideas are critically tested or mutually reinforced. These mechanisms can compound into epistemic drift when human and model reasoning interact. We show how these failures emerge in practice and propose human-side interventions, including interpretive cues, reflective prompts, and structured disagreement, to stabilise reasoning. Rather than modifying models, the framework focuses on governing interaction. The central claim is that fluency can create an illusion of understanding. Aligning AI therefore requires not only technical improvements but structures that enable reflective and falsifiable human oversight.

翻译：随着大语言模型（LLMs）融入日常及高风险决策场景，它们继承了人类语言的模糊性与偏见。尽管模型能生成流畅连贯的输出，但其依赖的是统计模式预测而非基于实质的推理，导致可能产生看似合理实则错误的输出。本文论证这些失误不仅是技术性问题，更是认知性问题。LLMs复刻了类似人类直觉性推理的关联模式，在与人类用户交互时会放大系统性误读。为分析该现象，我们引入认知-认识论框架"玫瑰框架"（Rose-Frame），用于诊断人机交互中的认知断裂。该框架识别出三类反复出现的陷阱：（i）地图与疆域：区分表征与现实；（ii）直觉与理性：分离快速关联判断与反思性推理；（iii）冲突与确认：审视观点是经批判性检验还是相互强化。当人类与模型推理交互时，这些机制可能复合为认知漂移。我们展示了这些失误在实践中的显现方式，并提出人类侧干预措施——包括解释性线索、反思性提示及结构化异议——以稳定推理过程。该框架不依赖模型修改，而是聚焦于交互治理。核心主张在于：流畅性可能制造理解幻觉。因此，对齐AI不仅需要技术改进，更需要建立支持人类进行反思性、可证伪监督的治理结构。