Looking Is Not Depending: Testing Positional Attention with an Addressable k-mer Memory in DNA Models

Pretrained DNA models are widely used to predict regulatory activity, chromatin state, and splicing. As accuracy improves, it remains unclear whether predictions rely on task-relevant biological signals or on incidental correlations in the data. One common form of evidence, attention concentrated at functional elements, shows where the model looks but not what the prediction depends on. Testing this distinction requires connecting attention at annotated positions with interventions on the corresponding internal representations. To enable this, we present LIND, a frozen Mamba backbone with an explicit $k$-mer memory and a local read head. Attention in LIND corresponds to memory sources selected by genomic address, so matched interventions and controls test positional explanations while the input sequence and parameters remain unchanged. To avoid the influence of dataset artifacts, the aforementioned tests were conducted under conditions where LIND achieved relative improvements across 16 Nucleotide Transformer tasks. At splice acceptors, attention is enriched 1.54-fold at the canonical AG, yet removing the underlying sources leaves AUROC essentially unchanged, opposite to the prespecified direction, while full memory removal costs 0.281. Attention looks at the AG in this Mamba case, yet silencing these sources does not reduce AUROC. In this model, looking is not depending. Our code can be found in the Supplementary Materials accompanying this paper.