The University of Texas at Arlington

mRNA foundation models usually average token embeddings over the whole transcript before fitting a linear probe, so signals from short regulatory regions are diluted by longer ones. A parameter-free region-aware readout instead averages within the 5′ UTR, coding sequence and 3′ UTR and concatenates the three means; across 20 frozen models it raises Pearson correlation on translation and stability tasks by 11–25% on average, with no model significantly worse on any regression task. On classification tasks AUPRC changes range from −3% to +11%.

mRNA foundation models usually average token embeddings over the whole transcript before fitting a linear probe, so signals from short regulatory regions are diluted by longer ones. A parameter-free region-aware readout instead averages within the 5′ UTR, coding sequence and 3′ UTR and concatenates the three means; across 20 frozen models it raises Pearson correlation on translation and stability tasks by 11–25% on average, with no model significantly worse on any regression task. On classification tasks AUPRC changes range from −3% to +11%.

Medical vision-language models cave to authoritative prompts, and prompt-level defenses can restore the right answer while visual evidence use still collapses in late fusion layers. HD-AS changes no parameters: it ranks attention heads by activation drift between neutral and biased prompts, then damps those heads' output projections at inference. On SLAKE, Qwen2.5-VL-7B's mean conditional flip rate falls to 34.60% versus 38.29% for contrastive decoding, with neutral-prompt accuracy held at 74.37%.

Medical vision-language models cave to authoritative prompts, and prompt-level defenses can restore the right answer while visual evidence use still collapses in late fusion layers. HD-AS changes no parameters: it ranks attention heads by activation drift between neutral and biased prompts, then damps those heads' output projections at inference. On SLAKE, Qwen2.5-VL-7B's mean conditional flip rate falls to 34.60% versus 38.29% for contrastive decoding, with neutral-prompt accuracy held at 74.37%.