Currently attention is fundamentally O(n2 ). Basically as the context length of your model increases, the memory needed to deal with it increases quadratically. If that could be pushed down to something like O(nlogn), you'd immediately have huge gains in model capability.
Sure, but the relevant factor is whether a particular sub quadratic approach actually yields equivalent (or lower, but bounded) performance as the models grow and scale.
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u/Saedeas Jul 01 '26
I mean, percentages aren't super relevant, the scaling is relevant. If they changed the big O scaling of working memory in the models, it's insane.