Continuity-Governed Proto-Concious Conversational Cognition: A Theoretical Framework and Symbiotic Conversational Continuity Architecture (SCCA)

Large language models (LLMs) have achieved remarkable conversational fluency, yet their dominant paradigm, local next-token prediction, remains structurally limited in sustaining continuity of identity and meaning across time. This article argues that continuity-governed cognition is a prerequisite for high-fidelity human-AI symbiosis and cannot be obtained through scaling or retrieval alone. Drawing on predictive processing and active inference, narrative identity theory, communication privacy management, and affective episodic memory research, we propose a full-spectrum model of conversational processing comprising five layers: factual exchange, emotional disclosure (including silence semantics), meta-processing, identity continuity, and ambiguity retention. We then introduce the Symbiotic Conversational Continuity Architecture (SCCA), a governance-first architecture that operationalizes these layers via a continuity layer, Rogue Variable stabilization, permission governance, perceived agency modulation, and human authorization loops. Finally, we define proto-conscious conversational behavior as persistent, self-consistent interpretive modeling across time under ambiguity and conversational disclosure. The framework reframes the frontier of conversational AI from response quality to longitudinal interpretive coherence.