Residual Connection

Residual connections, also called skip connections, are direct paths from a layer's input to its output that bypass the intermediate computation, introduced by He et al. in the 2015 ResNet paper and adopted as standard practice in Transformers. Mathematically, the output of each sublayer becomes y = LayerNorm(x + Sublayer(x)) — the addition of the original input is what makes the connection "residual". Residual connections enable training of very deep networks (hundreds of layers) by providing direct gradient paths that prevent vanishing and exploding gradients during backpropagation. Every modern LLM uses residual connections around both the multi-head attention sublayer and the feed-forward network sublayer in every Transformer block, with layer normalization applied either before (pre-norm) or after (post-norm) the addition. The pre-norm formulation has become dominant because it produces more stable training at frontier scale. AI governance teams document the residual structure as part of model architecture lineage, though it is essentially universal across modern Transformer variants.

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