AINeutralarXiv – CS AI · 3h ago6/10
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On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
Researchers introduce the first theoretical framework for analyzing test-time adaptation (TTA) in machine learning, establishing recovery complexity bounds that reveal fundamental limits on how quickly models can adapt to non-stationary data streams without labeled data. The work provides mathematical guarantees for TTA learnability and identifies an intrinsic trade-off between adaptivity and information constraints.