DELTAGENOME: SPARSE-EDIT INFERENCE FOR GENOMIC FOUNDATION MODELS

Genomic variant analysis repeatedly evaluates sequences that differ by one or a few nucleotides, yet genomic foundation-model inference usually treats reference and alternate alleles as independent dense inputs. DeltaGenome formulates this workload as sparse-edit inference: given a computed reference, determine which latent states and operations remain reusable after a sparse genomic edit. Across NT-v2, DNABERT-2, Caduceus, and HyenaDNA, single-nucleotide edits induce structured and predictable latent perturbations, but locality alone does not deter- mine computational value. The decisive systems property is reuse-frontier persis- tence: how long an architecture preserves exact reusable dependency structure af- ter the edit. On a frozen 384-variant benchmark, masked reconstruction preserves dense alternate perturbations at controlled selection-policy budgets, yet runtime diverges sharply. DNABERT-2 remains slower than dense inference in the tested executor, whereas HyenaDNA obtains a 1.54x warm-query speedup with a per- sistent full-stack causal reuse frontier and a larger reference cache. DeltaGenome identifies not a universal sparse executor, but a cross-architecture principle for serving genomic foundation models around nearby sequences.