Abstract Generative AI models (LLMs and Diffusion Models) have achieved state-of-the-art results in isolated tasks but suffer from "Contextual Fragmentation" in temporal workflows. This paper introduces the Mnemosyne Protocol, a vector-based orchestration layer designed to maintain semantic and visual consistency across heterogeneous agent systems. Unlike the Model Context Protocol (MCP)1-style context passing (where data is typically transmitted to a model session), Mnemosyne utilizes an "Inverse Context Flow" (ICF) architecture. This approach brings the model's reasoning capabilities to the local data environment, ensuring "Local-First Sovereignty" for intellectual property. Preliminary simulations indicate up to a ~40% reduction in continuity hallucination rates (measured against a baseline of stateless zero-shot prompting on N=100 sequential narrative frames). We propose a mathematical framework for "Contextual Continuity" and demonstrate its application in minimizing production rework while maximizing IP security. Metric Definition: We define "Continuity Hallucination" as the sum of Style Drift (visual inconsistency > delta) and Factual Constraint Violations (e.g., character clothing changes, object permanence errors) per 100 sequential frames. Here, delta denotes a calibrated perceptual-distance threshold (e.g., cosine distance in CLIP embedding space) used to flag style drift relative to a reference style pack.
Mert Kerem Salman (Thu,) studied this question.