Deep large language models often experience signal degradation as they scale, causing informative features formed in early layers to be lost in later layers due to repeated residual updates. To mitigate this, researchers have introduced mixture-of-depths attention, a novel mechanism enabling each attention head to focus on specific sequence elements. This approach allows for more effective preservation of key features, improving overall model performance. By dynamically adjusting the attention mechanism, models can better retain informative features, even in deeper layers. The introduction of this mechanism addresses a significant challenge in large language model development, where increased depth can lead to diminished feature retention. This matters to practitioners because it enables the creation of more accurate and reliable large language models, capable of capturing complex patterns and relationships in data, so improving model performance has significant implications for natural language processing applications1.