The Transformer is currently the reference architecture for serious AI. Not because it is obviously the most brain-like, elegant, or efficient design, but because it has the best scaling story. You add data, parameters, compute, context length, better training recipes, better post-training, and the model gets better in a surprisingly smooth way. That is rare. In deep learning, many ideas are clever. Few are industrial. The Transformer's superpower is attention. Every token can look at every other token and decide what matters. This is an incredibly general operation. It works for language, code, images, audio, video, protein sequences, robotics tokens, and tool traces. The architecture is simple enough to scale, parallel enough to train efficiently, and expressive enough to absorb huge datasets. But it has an obvious tax: attention is expensive. Full self-attention scales badly with sequence length. In autoregressive generation, the model accumulates a key-value cache, which grows...
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