arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.
By Athanasios Tziouvaras, Carolina Fortuna, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Marko Grobelnik, Bla\v{z} Bertalani\v{c}
arXiv:2606. 27832v1 Announce Type: new Abstract: Statistical adversarial detection (SAD) treats detection as a two-sample test.
By Zhijian Zhou, Xunye Tian, Jiacheng Zhang, Zesheng Ye, Yiyi Guo, Donghao Zhang, Liuhua Peng, Feng Liu
arXiv:2607. 05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of new threats.
By Konstantin Berlin
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet
arXiv:2505. 04608v5 Announce Type: replace-cross Abstract: Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continual, post-deployment monitoring to quickly detect and address any unsafe behavior.
By Drew Prinster, Xing Han, Anqi Liu, Suchi Saria
arXiv:2606. 07631v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task.
By Huy Nghiem, Sy-Tuyen Ho, Sarah Wiegreffe, Hal Daum\'e III