The Architecture of Living Memory: A Hybrid AI Model and Federated Learning for Scalable Personality Simulation

This paper introduces a novel architecture for creating scalable, dynamic, and personalized simulations of human personality. Existing approaches to digital legacy are confined to static archives, while the creation of individual large-scale models is computationally and financially prohibitive. We address this challenge through a hybrid "Mind-Soul" architecture, which combines the power of universal foundational AI models (the "Foundational Mind") with lightweight, parameter-efficient adapters (the "Personalized Soul") created using Low-Rank Adaptation (LoRA). Data for personalization is gathered via our novel "Federated Longitudinal Studies" (FLS) methodology, ensuring privacy-by-design through local, on-device data processing. Our architecture presents not only a technically robust solution for modeling unique cognitive and linguistic styles but also a cognitively plausible model of individual learning, laying the groundwork for a new era of digital legacy.