Large language models have advanced rapidly, from pattern recognition to emerging forms of reasoning, yet they remain confined to linguistic simulation rather than grounded understanding. They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction. This paper proposes a complementary approach in which reasoning is treated as a relational process distributed between human and model rather than an internal capability of either. Building on recent work on "System-2" learning, we relocate reflective reasoning to the interaction layer. Instead of engineering reasoning solely within models, we frame it as a cognitive protocol that can be structured, measured, and governed using existing systems. This perspective emphasizes collaborative intelligence, combining human judgment and contextual understanding with machine speed, memory, and associative capacity. We introduce "The Architect's Pen" as a practical method. Like an architect who thinks through drawing, the human uses the model as an external medium for structured reflection. By embedding phases of articulation, critique, and revision into human-AI interaction, the dialogue itself becomes a reasoning loop: human abstraction -> model articulation -> human reflection. This reframes the question from whether the model can think to whether the human-AI system can reason. The framework enables auditable reasoning traces and supports alignment with emerging governance standards, including the EU AI Act and ISO/IEC 42001. It provides a practical path toward more transparent, controllable, and accountable AI use without requiring new model architectures.

翻译：大型语言模型取得了快速进展，从模式识别到新兴的推理形式，但它们仍然局限于语言模拟，而非有根基的理解。它们能生成看似反思的流畅输出，但缺乏时间连续性、因果反馈以及与现实世界互动的锚定。本文提出一种互补方法，将推理视为分布在人与模型之间的关系过程，而非任何一方的内在能力。基于近期关于“系统-2”学习的研究，我们将反思性推理重新定位至交互层面。我们不单纯在模型内部进行推理工程，而是将其构建为一个可通过现有系统来结构化、度量并治理的认知协议。这一视角强调协作智能，将人类的判断与情境理解同机器的速度、记忆及联想能力相结合。我们引入“建筑师之笔”作为实用方法。如同建筑师通过绘图进行思考，人类将模型用作结构化反思的外部媒介。通过在人类-人工智能交互中嵌入阐述、批判与修订的阶段，对话本身成为一个推理循环：人类抽象 → 模型阐述 → 人类反思。这将问题从“模型能否思考”重新框架为“人-人工智能系统能否推理”。该框架可生成可审计的推理轨迹，并支持与新兴治理标准（包括欧盟人工智能法案和ISO/IEC 42001）的对齐。它为通往更透明、可控且负责任的人工智能应用提供了一条无需新型模型架构的实用路径。