arXiv — cs.AI preprintsInternational21 October 2026
An Empirical Study of Development-Tuned Representation Sensitivity for Medical QA Data Selection
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arXiv:2609.09684v2 Announce Type: replace-cross Abstract: We study medical question-answering (QA) data selection when a developer already has questions, labels, and auxiliary explanatory texts but can retain only $B$ examples for a final LoRA update. Representation-Sensitivity Prioritization (RSP) first restricts the candidate bank to its top-$4B$ predictive-entropy examples, then combines the frequency of answer flips with non-flipping contraction of the top-two answer margin under hidden-state perturbations. We evaluate this operational score on five medical QA datasets with google/medgemma
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