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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint introduces the concept of selective adaptation for vision-language models and proposes a simple heuristic (Cross-Augmentation Similarity) to skip test-time adaptations that are negligible or harmful. The method achieves comparable or improved accuracy while skipping ~85% of adaptations, but lacks peer review, rigorous comparison to alternative selection strategies, and quantified validation metrics.
Preprint. Intervention: Cross-Augmentation Similarity (CAS) heuristic to selectively apply test-time adaptation when predictions across augmented views exhibit low similarity. Compared with: Standard test-time adaptation applied to all test samples.
CAS selectively skips adaptation on ~85% of test samples while preserving or improving overall accuracy Existing test-time adaptation methods exhibit two failure modes: negligible adaptations (no prediction change) and harmful adaptations (correct predictions flipped to incorrect)
Existing test-time adaptation methods exhibit two failure modes: negligible adaptations (no prediction change) and harmful adaptations (correct predictions flipped to incorrect)
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This is an early-stage methodological study proposing a novel problem formulation and baseline approach, without peer review, comparative validation against established methods, or clinical application.
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Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a per-sample analysis of model predictions before and after adaptation, and observe two failure modes in existing TTA methods that echo previous work. Adaptations are frequently negligible, yielding no change in the model's predictions, and more severely, they can be detrimental by flipping previously correct predictions to incorrect ones. This naturally raises a question: Can we identify and skip such negligible or harmful adaptations? In this work, we introduce a new problem of selective adaptation, which aims to determine whether a given test sample should undergo adaptation or be skipped. To this end, we propose Cross-Augmentation Similarity (CAS), a simple baseline that performs adaptation only when predictions across augmented views exhibit low similarity. Notably, CAS not only preserves but in some cases improves overall accuracy, even when skipping nearly 85% of the adaptation process. We hope other researchers will explore this new direction and surpass the performance of our baseline. Our code is available at https://github.com/sirujiang/selective-adaptation.
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