Evaluate Before You Commit: Label-Free Candidate-Update Utility for Test-Time Adaptation

Test-time adaptation (TTA) can improve performance under distribution shift, yet deciding where to adapt does not determine whether the resulting parameter update is worth retaining. We formulate candidate-update utility: after a base adaptation operator produces a tentative state, should that realized update enter persistent model state? We introduce a label-free evaluate-before-commit controller that allocates candidate-generation compute using forward-only screening, evaluates each tentative update on independently sampled probe views using held-out predictive consistency and drift from a frozen pretrained reference, and transactionally rolls back low-utility candidates. Across CLIP and DINOv2 under synthetic corruption, natural domain shift, and a cross-hospital stress test, the proposed utility estimator improves harmful-update AUROC from 0.589 to 0.750 and AUPRC from 0.305 to 0.492 relative to predictive entropy. At the principal operating point, Utility-Aware control reduces the harmful committed-update rate (HCUR) from 27.0% to 11.0% while increasing the accuracy gain from +2.20 to +2.40 percentage points, halving candidate-update backward passes, and reducing adaptation-specific wall-clock time by 28±3%. Post-proposal verification also complements strong pre-update selection: adding Utility-Aware verification after Cross-Augmentation Similarity (CAS) reduces HCUR from 15.2% to 8.2%, while the proposed score achieves next-window harmful-update AUROC of 0.738 versus 0.641 for CAS. These results support pre-update selection and post-proposal candidate commitment as distinct and complementary control levels for TTA.