Attention-Residuals

Attention Residuals introduces a new technique that replaces standard residual connections in Transformer models with learned attention mechanisms, allowing each layer to selectively combine outputs from previous layers rather than simply adding them together. This approach prevents the dilution problem where deeper layers lose individual contribution and delivers consistent improvements across reasoning tasks, with models matching baseline performance while using 25% less compute.