Neural Holographic Memory Processing
NHMP (Neural Holographic Memory Processing) is a proposed alternative AI architecture that rethinks how models store and retrieve information. Instead of relying on uniform token-based attention like Transformers, it uses structured signal interaction and orthogonal routing to isolate memory into separate manifolds. This design aims to reduce interference and improve stability, especially when learning structured, correlated knowledge. Retrieval operates through resonance detection rather than probability normalization, drawing from classical signal processing principles. All components are grounded in established mathematics and digital computation, making NHMP a theoretically feasible but empirically testable architectural hypothesis.
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