Functional Attention: From Pairwise Affinities to Functional Correspondences

ArXi:2605.31559v1 Announce Type: new Learning mappings between infinite-dimensional function spaces, or operator learning, is essential for many machine learning applications. Although transformer-based operators are popular, they often rely on token-wise attention. These methods treat continuous fields as discrete tokens and usually ignore the global functional structure. We