Transformers Resist Their Own Architecture

According to LessWrong, a new research post argues that transformers work by fighting their own design. Author Zach Baker builds on a mathematical theory that treats a transformer's tokens as particles moving on the surface of a sphere. Left to the architecture alone, those particles are driven to clump into clusters and eventually collapse toward a single point as they pass through the network's layers. The twist: Baker's experiments suggest the weights a model learns during training work to resist that clustering and prevent the collapse. In his framing, the transformer functions precisely by pushing back against what its own attention mechanism would otherwise do. It's the first entry in a planned series comparing the theory against real trained models like GPT-2 and BERT.

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