Reinterpreting RAG Through Topological Closure: External Boundary Fields, Closure Quality, and Structural Diagnosis of AI Alignment (V1.8)

A working paper reinterpreting Retrieval-Augmented Generation (RAG) as an external boundary field system within the CTPT (Cognitive Topological Phase Transition Theory) framework, rather than as knowledge injection. Proposes Closure Quality (CQ) as a core diagnostic metric, analyzes three RAG failure modes (layer mismatch, insufficient coupling, boundary field conflict), introduces the RQ-CQ quadrant classification framework, and derives structural implications of RAG for AI alignment: external constraints can influence closure structure but cannot substitute for internal topological integrity. Released as a proposal-based working paper open to critique and empirical challenge.