arXiv:2609.37687v1 Announce Type: new
Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively queryin...
By Muhammad Huzaifa, Lea Sch\"onherr, Thorsten Eisenhofer
arXiv:2609.36655v1 Announce Type: new
Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods o...
By Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong
arXiv:2605. 28057v2 Announce Type: replace-cross Abstract: Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data.
By Zhi Zhou, Ming Yang, Shi-Yu Tian, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li
arXiv:2602. 06136v2 Announce Type: replace Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisation on-the-fly with only unlabelled samples.
By Sudarshan Sreeram, Young D. Kwon, Cecilia Mascolo
arXiv:2607. 17531v1 Announce Type: cross Abstract: Test-time collaboration, including self-consistency, best-of-N selection, critic models, and verifier pipelines, is often credited with broadly improving LLM reasoning, yet its gains are uneven and sometimes negative.
By Jie Hu
arXiv:2608.29920v1 Announce Type: cross
Abstract: Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA...
By Chandler Timm C. Doloriel, Yunbei Zhang, Muhammad Salman Siddiqui, Tor Kristian Stevik, Fadi Al Machot, Kristian Hovde Liland, Habib Ullah