An autoencoder outperforms two transformers on prediction of gene expression from chromatin accessibility

Models that predict RNA abundance from chromatin accessibility are rarely compared under the same features, targets and metrics. We built one benchmark on 126,604 paired single-nucleus profiles from 19 human studies with a shared vocabulary of 50,000 peaks and 3,185 genes, ten 5x2 cross-validation folds and one scoring target, and compared seven implemented pipelines and two reference predictors across 70 trained runs; each pipeline keeps its own input normalisation and native training target. The scButterfly-B pipeline, built on dual variational autoencoders, outperformed every transformer pipeline, including an autoregressive Perceiver model (Omic Translator) and a support-free reformulation of Cisformer, on per-gene and within-cell Spearman correlation in all sixty paired fold comparisons, with 3.8 times shorter training. Transformer pipelines come within 0.02-0.04 on within-cell profile shape but lag by 0.10-0.24 on per-gene correlation. On two withheld cell types per-gene correlation is low; controls show the podocyte values are typical of within-type correlation for seen cell types of the same size, whereas Paneth values match a cell-agnostic shuffle for every Omic Translator arm. Within the transformer family, prespecified ablations show the Perceiver encoder was worth +0.0439 in within-donor per-gene correlation over full self-attention, a chromosome-aware genomic attention prior reduced it by 0.0096 (exact donor-level sign-flip test, Holm-adjusted p = 1.2e-4), and decoder output order had no detectable effect. A training-mean profile reaches 0.314 within-cell correlation with no input and a constant at the true expressed-gene positions reaches 0.996 per-gene correlation, so oracle-support protocols are reported only as diagnostics. Code, results and an audited release are provided.