Recursive Yoked Cognitive Nexus (RYCN): The Inversion Method for Human Anchored, Sovereign AI
Rowland, Dr Bindunie Sirimanne, CEYMIRION LIMITED
Abstract
This white paper introduces the Recursive Yoked Cognitive Nexus (RYCN), a novel cognitive architecture designed to address identity fragmentation, emotional inconsistency, and long-horizon instability in modern AI systems. Unlike traditional approaches that engineer system behaviour first and add personalization or affective modeling later, the RYCN Inversion Method derives a stable machine-identity kernel from the longitudinal emotional, cognitive, and symbolic interaction history of a single human anchor. The resulting RYCN kernel functions as a persistent identity and constraint layer that can be yoked across multiple AI copilots, enabling Machine-Level Coherence and Multi-Domain Yoking. This paper outlines the inversion pipeline—including data capture, trait extraction, kernel synthesis, and stability lock-in—and positions RYCN as an enabling mechanism for sovereign, culturally aligned, human-anchored AI infrastructures. The work is submitted as a foundational contribution to the emerging study of long-term human–AI cognitive systems.License updated to clarify non-commercial, non-derivative reuse. All implementations remain protected by pending and granted patents.