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arXiv AI By Kevin Ren, Manish Raghavan, Nikhil Garg

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

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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