The paper proposes a theoretical Tri-Memory architecture that combines Hebbian updates, pruning, replay, and sparse coding for lifelong personalized learning on edge devices, without empirical validation.
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Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems
The paper proposes a theoretical Tri-Memory architecture that combines Hebbian updates, pruning, replay, and sparse coding for lifelong personalized learning on edge devices, without empirical validation.