(Abstract + short pitch) Generative AI models (LLMs and diffusion/video models) achieve state-of-the-art results in isolated tasks but suffer from Contextual Fragmentation in temporal workflows, leading to continuity drift and expensive human rework. This paper introduces the Mnemosyne Protocol, a vector-based orchestration layer designed to maintain semantic and visual consistency across heterogeneous generative systems while enforcing Local-First Sovereignty for studio IP. Mnemosyne operationalizes continuity as a discrete-time, fail-closed verification gate (a conjunctive product-of-constraints) evaluated across frame sequences, with an explicit rollback + localized re-sampling mechanism before final rendering. Preliminary simulations demonstrate substantial reductions in continuity hallucination rates under defined constraints. What’s new in (v1.6 Benchmarks the paper text is CC BY 4.0.
Mert Kerem Salman (Mon,) studied this question.