The paper investigates test‑time adaptation for medical image segmentation, showing that a fixed adaptation horizon can harm many individual cases. It introduces prediction fragmentation—a measure of disagreement between the source model and the adapted mask—to predict harmful adaptation without extra labels or backward passes. Using a case‑level router based on this metric, the authors reduce harmful adaptation on cardiac MRI from 58.7% to 20% while maintaining accuracy.
The paper examines safety routers—systems that route user requests to different language models—and finds that their performance degrades significantly when evaluated under distribution shift. In standard benchmarks, routers appear effective because the best single model is chosen from the same evaluation data, but when the data distribution changes, the routing advantage diminishes or disappears. The study quantifies this bias across multiple safety corpora, showing that routers offer little benefit under realistic shift conditions and that recognition‑based defenses can be undermined by attackers who know the model being used.
By Amit Singh Bhatti, Vishal Vaddina
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.
By Yuyang Shen
The paper introduces RouteCert, a method for ensuring risk control in multimodal systems that acquire inputs adaptively. It shows that conditional calibration can remain valid even when the acquisition policy determines the calibration group, and provides two finite‑sample constructions: threshold‑free routing with terminal‑pattern calibration and simultaneous validation of policy‑pattern pairs. Experiments on a clinical ECG task and masked multimodal benchmarks demonstrate that RouteCert achieves low disagreement rates and competitive answered fractions while validating each acquisition stage separately.
By Melika Baghi
arXiv:2610. 12232v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods for time-series forecasting update a deployed model, or a small adapter around it, from incoming ground truth.
By Mohamed Readh Fentazi, Mazene Ameur, Adlen Ksentini
arXiv:2609.39445v1 Announce Type: cross
Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup...
By Hung Phan, Thuy T. Nguyen, Minh Ngoc Dinh, Nhat-Quang Tran