The Kimi team designed attention residuals where every layer can reach back and look at any previous layer's output, preventing the AI from getting confused or forgetting its earlier thoughts.

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

The Kimi team's recent breakthrough fixes the amnesia problem of current AI models.

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.