The Continuity Layer After Memory: The Mayorga Mnemosyne AI Continuity Framework for AI Systems That Preserve Meaning Over Time

This essay introduces the Mayorga Mnemosyne AI Continuity Framework as a governed continuity architecture for AI-native systems. It argues that memory, retrieval, larger context windows, and AI-maintained knowledge bases are important but insufficient for trustworthy long-term AI systems. The missing layer is governed continuity: the preservation of meaning, evidence, decisions, assumptions, definitions, contradiction handling, canon status, human review, and justified change across time. The essay situates OpenAI Dreaming, Karpathy-style LLM Wikis, agent memory research, wiki-memory compilation, and self-evolving retrieval as signals that the AI industry is moving beyond disposable prompts toward systems that carry context forward. It distinguishes personal memory from structural continuity and frames the Mayorga Mnemosyne AI Continuity Framework as a public doctrine for preserving meaning over time without revealing private implementation details.