A semantic tokenization scheme where token geometry reflects semantic relationships [R]

Explain Like I'm Normal

A new proposal suggests redesigning how models see language by replacing statistical tokenization with a system where token IDs themselves reflect semantic similarity. By organizing the 'alphabet' of a model geometrically, it could potentially learn relationships faster and generalize better from fewer training steps. This moves the logic of understanding from the high-level weights down into the fundamental symbolic architecture.