Dynamic Vector Networks: Self-Organizing Knowledge Structures Beyond Transformers
Dynamic Vector Networks: Self-Organizing Knowledge Structures Beyond Transformers
We introduce Dynamic Vector Networks (DVN), an architecture fundamentally different from Transformers: each node is a rich embedding vector (not a scalar neuron), connections form through Hebbian learning in real-time, and new nodes are spawned dynamically for novel concepts. The architecture combines four properties no existing system offers simultaneously: rich vector nodes, real-time weight evolution, dynamic node creation, and self-organization by semantic similarity. Also available in French: Dynamic Vector Networks : Au-delà des Transformers
beyond transformers, knowledge representation, self-organizing, Hebbian learning, emergent architecture, dynamic vector networks
beyond transformers, knowledge representation, self-organizing, Hebbian learning, emergent architecture, dynamic vector networks
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