Adaptive Digital Consciousness: An Evolutionary Framework for Human Identity Preservation Using Large Language Models

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have greatly changed the interaction between man and machines through natural-language reasoning, content generation, personalization, and interaction via agents. However, traditional LLM applications do not provide a unified mechanism for maintaining a long-term, structured, and identity-consistent representation of a person during long-time interaction. As shown in the recent studies of generative agents, long-term LLM memory, agentic memory, lifelong learning, and AI self-identity, these processes present essential capabilities that have been studied as separate components. This paper presents the Adaptive Digital Consciousness (ADC) framework as the theoretical architecture for computational representation of core features of human identity. The ADC framework proposes a system of structured representation of Digital DNA for personal knowledge, experience, preferences, values, and behaviors and connects it to multilayer memory, semantic embeddings, vector retrieval, Retrieval-Augmented Generation (RAG), LLM reasoning, adaptive learning, and autonomous agents. The framework also proposes the notion of identity constraints and the system of updating memory, consolidating it, handling conflicts, forgetting, maintaining privacy, consent, and security, as well as monitoring the origin of digital information. The goal of the study is not to replicate biological or subjective consciousness The research motivation, previous works, the system suggested, as well as memory and adaptation techniques, evaluation techniques, ethics and applications of the findings, drawbacks of the research and future research directions on this subject.