Transformers, central to advancements in machine learning, leverage the self-attention mechanism for tasks across various domains, including natural language processing and computer vision. However, the underlying dynamics of these models, particularly concerning self-attention networks (SANs), present challenges like rank collapse. A recent study provides a mathematical analysis of this phenomenon…