The Efficient Transformer Design Space: Comparing All Variants and the Three Futures of Attention
A unified view of every efficient attention mechanism — each is a different way of replacing the dense N×N attention matrix (sparsify it, factorize it, approximate the softmax kernel, recurse it, or pool one of its dimensions), every variant computed on a single 8-token example so the differences are visible side by side, ranked on the impossible triangle of training parallelism, low-cost inference, and strong quality, and ending with the three credible futures: hybrid retention, selective state spaces, and latent attention.