arXiv AI By Siyu Zhou, Jialin Ouyang, Fan Xia, Adarsh Subbaswamy, Drew Prinster, Suchi Saria, Augusto Garcia-Agundez, Sarah Pollet, Jinoos Yazdany, Patrick Vossler, Jean Feng

Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
4d ago

Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift

The paper introduces a test‑time adaptation (TTA) method for AI text detection that addresses continual distribution shifts such as adversarial humanization, new large language models, and temporal drift in human writing. By exploiting inference‑time homogeneity among unlabeled samples, the TTA approach achieves robust performance, outperforming state‑of‑the‑art supervised detectors—e.g., improving detection of adversarial AI‑generated text from 27.0% to 93.3%. The study demonstrates that TTA can maintain high detection accuracy in real‑world, evolving deployment scenarios.

By Kevin Ren, Manish Raghavan, Nikhil Garg
arXiv AI
Sep 11

SCCM : Stream Cruise Control Method for Automated Drift Detection and Adaptation

The paper introduces the Stream Cruise Control Method (SCCM), a framework for detecting and adapting to concept drift in online regression. SCCM performs early-response drift detection, quantifies drift magnitude, applies KPI-window-based thresholding to reduce false alarms, dynamically tunes hyperparameters, and recalibrates models, all within an in-memory design for real-time operation. Evaluations on synthetic and real-world datasets demonstrate that SCCM improves predictive performance compared to eight baseline detector–adaptation methods.

By Mohammad Abu-Shaira, Weishi Shi