Variational-Ising-Attention：为科学任务量身定制的注意力机制

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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, extending attention from a ranking over isolated items to a collective state over interacting entities. We instantiate VIA on retrosynthesis reaction center prediction and, as a controlled internal ablation, on protein residue contact prediction, two structured prediction tasks governed by cooperative constraints: cooperative bond-breaking for retrosynthesis and inter-residue interactions for protein contact prediction. Comprehensive experiments across model variants, coupled with mechanistic analyses, demonstrate that VIA substantially outperforms standard softmax attention. More broadly, our findings suggest that for scientific problems, the optimal solution is not general-purpose efficiency, but appropriately tailored attention aligned with intrinsic domain structure. This work provides a theoretically grounded and empirically validated instantiation of this paradigm.

Submission history

From: Rui Wang [view email]
[v1] Sun, 26 Jul 2026 12:44:36 UTC (9,309 KB)
[v2] Mon, 5 Oct 2026 23:24:22 UTC (10,699 KB)