Attention Residuals (AttnRes) serves as a drop-in replacement for standard residual connections in Transformers, allowing each layer to selectively aggregate earlier representations. It includes two variants: Full AttnRes, where each layer attends over all previous outputs, and Block AttnRes, which groups layers into blocks to reduce memory usage from O(Ld) to O(Nd).

A minimal transformer model has been developed to perform 10-digit addition tasks. The model demonstrates the ability to learn and execute arithmetic operations effectively.