FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust test-time adaptation (RTTA) in the adversarial-stream setting, which remains comparatively underexplored relative to standard TTA, and propose SAFER (Stochastic Augmentation Framework for Enhanced Robustness), a training-free reliability-guided augmentation wrapper for RTTA.
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