Variational-Ising-Attention (VIA):TailoredAttentionMattersfor Science

תקציר מקורי באנגליתarXiv:2607.23634v1 Announce Type: new Abstract: Attention enables context modeling via query-key scoring with softmax normalization. Driven by industrial long-context demands, mainstream research has converged toward sparsity and efficiency--yet softmax's independence assumption persists. For scientific tasks unburdened by long-token constraints, however, richer structured coupling may often be essential, making tailored attention both viable and more appropriate. To this end, we propose Variational-Ising-Attention (VIA), which augments softmax normalization with an interacting Ising model; attention patterns emerge from learnable pairwise couplings via variational mean-field inference, redefining attention from a ranking over isolated items to a collective state over interacting entities.