How Attention Residuals Rewire Modern LLMs

Attention Residuals replaces the standard fixed residual accumulation with softmax attention over previous layer outputs. This enables each layer to selectively combine earlier representations using learned, input-dependent weights. Attention Residuals replaces standard fixed residual accumulation with depth-wise softmax attention over all preceding layer outputs. This enables each layer to combine earlier representations using learned, input-dependent weights. 00:00 Intro to residual connections 03:27 Intuition behind attention residuals 04:43 Full attention residuals 09:43 Block attention …