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This paper outlines the core principles of Asymmetric Memory within the Topos-Memetic Synthesis (TMS) framework. Moving beyond Matrix Rigidity and Euclidean limitations, we define a computational architecture based on 2-Way Flux and Graph Neural Network (GNN) topographies. By utilizing Magnetic Remanence ('Hibernation vs. Deletion' logic) and active defense mechanisms (Rearguard Probing and the Trojan Horse Protocol), we establish a paradigm for managing systemic data—with direct applications to systemic diseases through flow engineering.
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Leonte Stefan (2026) studied this question.
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