Aligning Genome Language with Structural Diffusion for Nucleic Acid Design

Genome language models and structure-conditioned inverse-folding networks learn complementary views of nucleic acids, linking sequence variation to the three-dimensional folds and molecular environments in which RNA and DNA operate. We introduce **Evo-IF**, which aligns genome-language representations with a compact structural diffusion model for prescribed backbones and nucleic-acid assembly contexts. A 1.18M-parameter context-aware interface enables geometric residue states to retrieve compressed representations from a frozen Evo2 encoder, with staged adaptation across molecular contexts. **Evo-IF** achieves 86.81% native sequence recovery on the Das Benchmark, 18.94 percentage points above NA-MPNN, and 76.96% across 121 DNA-containing assemblies. Capacity-matched and chain-boundary controls separate the effects of genome-language conditioning from those of preprocessing choices. Among the tested benchmark decoding settings, one-pass decoding achieves the highest recovery while reducing measured latency by avoiding costly diffusion refinement. Together, **Evo-IF** extends genome-language representations to macromolecular design under explicit geometric and assembly constraints.