This paper provides a structural explanation of the Chain-of-Thought (CoT) phenomenon observed in large language models (LLMs), where prompting techniques significantly improve AI output quality. Rather than asking whether the AI is "thinking," the core question reframed here is: "Why does the AI respond to the structure provided by the human?" Drawing on the Thought Operating System (思想OS) model, this paper analyzes the structural role of the six elements of Mindflight Cognition and demonstrates how they are externally embedded in CoT prompts. Furthermore, it introduces a quantitative model of structural response, including structural shard density, snap probability, and the SQ-based prompt design score. These allow us to reframe CoT as a case of structural response rather than mere token manipulation. This work thus offers an empirical validation of the Thought Operating System as a structure-capable intelligence, and serves as the first in a trilogy, followed by works on its designability (2025-28) and generative process (2025-29).
HIDEKI (Mon,) studied this question.