Traditional methods like Nadaraya-Watson kernel regression effectively demonstrate the fundamental principles of attention pooling but also reveal the limitations of hand-crafted attention mechanisms. Manually designing or tuning kernels (such as selecting the optimal width) is restrictive and often sub-optimal for complex tasks. Consequently, modern deep learning adopts a more powerful strategy: automatically learning the attention mechanism by optimizing the representations for both queries and keys through data-driven training.