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.

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.

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

If AI models are built too deeply, the learning signal flowing backwards through the model would vanish before reaching the beginning, a problem called the vanishing gradient problem.