Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection

ArXi:2604.13692v1 Announce Type: new As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become increasingly challenging to capture. Reliance on generator-specific artifacts is inherently unstable, since new models emerge rapidly and reduce the robustness of such shortcuts. This generalizes unseen generators as a central and challenging problem for AI-text detection.