PCS-AC: Compiling Output-Sensitive Actions to Correct Medical Predictions

Correcting a frozen model for medical decision-making requires interventions that fix errors without disrupting predictions that are already correct. A shared representation edit is often insufficient because the same direction may help some inputs but harm others. We introduce Predictable Corrective Subspaces via Action Compilation (), an output-sensitive framework that learns input-dependent corrective interventions in hidden representations. Specifically, a budget-weighted Jacobian identifies an output-sensitive subspace, within which source labels define bounded corrective actions. We compile these actions into an input-dependent low-rank controller via reduced-rank regression and refine it through the frozen backbone to improve predictions while preserving source margins. At inference, the controller uses only the current hidden state, without labels, gradients, or an additional backbone pass. On 2,100 retrospective radiologic questions, reaches 80.95% and 81.62% accuracy with Qwen2.5-VL-7B and InternVL3.5-8B, improving Native by 12.19 and 42.67 percentage points. Under matched source supervision, response subspace, and refinement budget, it outperforms a learned shared direction by 13.31 and 28.45 points. Matched-capacity comparisons further favor output-sensitive construction over random and activation-PCA pipelines. An independently fitted text controller reaches 95.03% on 3,360 controlled synthetic assertions, a 5.98-point improvement over Native. These results show that compiling output-sensitive corrective actions into conditional controllers enables more selective correction of frozen models.