AI RESEARCH
On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
arXiv CS.AI
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ArXi:2605.28057v1 Announce Type: cross Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data. Despite its empirical success, the learnability of TTA under non-stationary streams remains unexplored. A key challenge is the lack of a principled theoretical framework that simultaneously aligns with the TTA objective and captures both continuously evolving distribution shifts and intrinsic information constraints.