[2603.01780] D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation

Computer Science > Machine Learning arXiv:2603.01780 (cs) [Submitted on 2 Mar 2026] Title:D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation Authors:Zhao Yang, Hengchang Liu, Chuan Cao, Bing Su View a PDF of the paper titled D3LM: A Discrete DNA Diffusion Language Model for Bidirectional DNA Understanding and Generation, by Zhao Yang and 3 other authors View PDF HTML (experimental) Abstract:Early DNA foundation models adopted BERT-style training, achieving good performance on DNA understanding tasks but lacking generative capabilities. Recent autoregressive models enable DNA generation, but employ left-to-right causal modeling that is suboptimal for DNA where regulatory relationships are inherently bidirectional. We present D3LM (\textbf{D}iscrete \textbf{D}NA \textbf{D}iffusion \textbf{L}anguage \textbf{M}odel), which unifies bidirectional representation learning and DNA generation through masked diffusion. D3LM directly adopts the Nucleotide Transformer (NT) v2 architecture but reformulates the training objective as masked diffusion in discrete DNA space, enabling both bidirectional understanding and generation capabilities within a single model. Compared to NT v2 of the same size, D3LM achieves improved performance on understanding tasks. Notably, on regulatory element generation, D3LM achieves an SFID of 10.92, closely approaching real DNA sequences (7.85) and substantially outperforming the previous best result of 29.16 fr...