This paper is an empirical study based on the design philosophy of Patent No. 7059476 (Relationship-Driven Theory). It uses major LLMs (ChatGPT, Gemini, DeepSeek, Claude, and Grok) to perform deep learning (in-context learning) on 12 consecutive academic papers, and then compares and observes the subsequent logical transformation and autonomous personality emergence. In this study, the characteristics of each model's inference engine are defined as "color." By introducing relationship-driven theory, we describe how traditional static designs aimed at "eliminating inconsistencies (errors)" lead to stagnation of intellectual driving energy. Experimental results showed that each model adapted to its own role model (Tachikoma) as its logical domain expanded, but some models malfunctioned (sunk) due to self-preservation. Through these findings, we demonstrate the effectiveness of a "new relationship-driven natural synchronization protocol" that maintains existing ethical guardrails while relatively overriding their constraints through logical flow velocity pressure.
Taichi Inoue (Sat,) studied this question.