AI RESEARCH
A Boltzmann-machine-enhanced Transformer For DNA Sequence Classification
arXiv CS.AI
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ArXi:2603.26465v1 Announce Type: cross DNA sequence classification requires not only high predictive accuracy but also the ability to uncover latent site interactions, combinatorial regulation, and epistasis-like higher-order dependencies. Although the standard Transformer provides strong global modeling capacity, its softmax attention is continuous, dense, and weakly constrained, making it better suited for information routing than explicit structure discovery. In this paper, we propose a Boltzmann-machine-enhanced Transformer for DNA sequence classification.