Which Layer to Read? The Consequences of Layer Choice in DNA Foundation Models

Frozen DNA foundation models are conventionally used through final-layer representations, under the implicit assumption that the final hidden state provides the most informative features. We investigate how this convention affects performance and model comparisons, and how useful layers should be selected. Across ten models from five architectural families and two task suites (280 model–task cells), selecting a layer via held-out validation raises mean test AUROC from $0.726$ to $0.770$ on the primary suite, with similar gains on the external suite. The validation-based layer selection also changes model rankings in both suites. We then examine how to select layers without probing every stored state. Selecting layers directly with representation and transferability metrics yields lower mean test AUROC than full-layer validation. Useful depth varies across models and tasks, yet every model's median validation-selected depth lies in the first half of the stack in both suites. Searching only the first half of stored states nearly matches full-layer validation in mean test AUROC across both suites while requiring approximately half as many layer-specific probe fits. When screening only $10\%$ of stored states, LogME-guided nomination achieves higher mean test AUROC than the evaluated metric-free rules. These findings establish layer choice as a consequential component of frozen model evaluation and support explicit reporting of the extracted state and selection protocol.