Author

Ke Ren

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#artificial intelligence Jun 2026

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

This work proposes a test-time adaptation (TTA) approach that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time and establishes that TTA is a promising framework for AI text detection in the wild.

Ke Ren, Manish Raghavan, Nikhil Garg · arXiv.org · 1 citation · ⚡1

#artificial intelligence Preprint Sep 2026

Improving OCR Faithfulness via Gated and Attenuated On-Policy Distillation

GAD-RL is introduced, which adaptively regulates teacher supervision during joint post-training according to the student's current task performance and local distributions, and weights forward KL by the student's probability of the teacher's Top-1 token, moderating local auxiliary updates when student support for that...

Bao-De Wang, Zu-Ming Huang, Ke Ren et al. · 0 citations