Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift
arXiv:2606.25152v2 Announce Type: replace-cross Abstract: Deployed AI text detection approaches are supervised, relying on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable to three types of distribution shifts that occur continually post-deployment, and for which labeled data is often unavailable: adversarial humanization, new LLMs being released, and unidentifiable temporal drift in human writing after widespread LLM usage. Simul…