The attention residuals design allows the AI to stay perfectly focused on the most important details by selectively choosing which layers' information to use.

Residual connections allowed AI models to scale from only a few dozen layers to hundreds or even thousands of layers deep.

Models with attention residuals kept improving with increased depth, demonstrating that depth is an advantage, not a limitation.

Transformers fixed the amnesia issue by introducing an attention mechanism, allowing the model to look back at any previous word directly and selectively get exactly the information it needed.

In models with residual connections, the final result is a massive cumulative pile of data, where the importance of any single layer's contribution shrinks, burying early information.

Applying attention residuals to top AI models with hundreds of billions or even over a trillion parameters runs into physics limitations due to infrastructure.