
dnaHNet: A Scalable and Hierarchical Foundation Model for Genomic Sequence Learning
Standard fixed-vocabulary tokenizers fragment biologically meaningful motifs, while nucleotide-level models preserve biological coherence but incur prohibitive computational costs for long contexts. We introduce dnaHNet, a state-of-the-art tokenizer-free autoregressive model that segments and models genomic sequences end-to-end.








