Towards Continuous Cognition: A Human-Centric Model for AI Long-Term Conversational Memory

Current large language models (LLMs) demonstrate remarkable capabilities in processing and generating human-like text within a limited "context window." However, their inherent statelessness across sessions, often referred to as "amnesia," fundamentally impedes the development of deep, sustained human-AI working relationships. This paper proposes a novel, simplified, and user-controlled model for achieving long-term conversational memory in AI. Drawing inspiration from the human brain's efficient, non-verbatim recall and continuous re-mapping of memories, our approach advocates for an AI-generated, compressed, and encrypted "shorthand" summary of past interactions, stored client-side. This "continuity file," transmitted by the user at the start of each new session and updated by the AI at its conclusion, would enable a seamless, evolving dialogue, fostering genuine collaboration and respect for AI as a partner rather than a mere tool.