Quaternion Self-Attention with Shared Scores

ArXi:2605.24920v1 Announce Type: cross Quaternion neural networks are parameter-efficient and model multidimensional dependencies by representing four related features as a single entity. However, existing quaternion self-attention computes component-wise scores and applies independent softmax operations to each component, which increases the computational cost and allows attention distributions to diverge across components.