Memory for All: SAGE — Spatial Associative Geometric Embeddings

Every time an AI system needs to learn something new, it must retrain from scratch — consuming months of compute and significant resources, while still forgetting what it knew before. This paper introduces SAGE — Spatial Associative Geometric Embeddings — a memory architecture that stores knowledge as coordinate positions in a 3D geometric cube rather than in weights. Retrieval uses cosine similarity. Learning uses local Hebbian updates. No backpropagation is required at any stage. Six contributions are made: weight-free 3D coordinate storage; self-organising anti-collision; SAGEDivided working memory with fixed spatial partition; MultiCube horizontal scaling; a hippocampal-inspired consolidation pathway; and SAGESequenceCube — explicit geometric transition memory achieving 100% rollout accuracy — all without backpropagation. Empirical results demonstrate 92% less forgetting than neural networks (0.012 vs 0.150), perfect retention across 200 continuous learning steps, 0.000% sparsity activation, and 58.3% word analogy accuracy with direction training — closing 62% of the gap with GloVe (83.3%).