QKV張量機制與CTPT架構的完整對應分析 (V2.2)

This work presents a correspondence analysis between the QKV tensor mechanisms of Transformer-based large language models and the Cognitive Topological Phase Transition (CTPT) framework. Rather than proposing a new computational model, this study focuses on structural and topological alignment, mapping attention dynamics, spectral properties, and topological invariants to CTPT phases. The objective is to clarify conceptual boundaries and provide a stable analytical bridge between modern LLM architectures and CTPT as a meta-theoretical framework. 本文提出 Transformer 架構中 QKV 張量機制與認知拓撲相變理論(CTPT)之間的對應分析。本文不提出新的計算模型,而是聚焦於結構與拓撲層級的對照,將注意力動態、譜性質與拓撲不變量映射至 CTPT 的相變架構中,旨在釐清概念邊界,並為大型語言模型與 CTPT 元理論之間建立穩定的分析橋接。