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

• URL: http://arxiv.org/abs/2604.13692v1
• Date: Wed, 15 Apr 2026 10:14:06 GMT
• Title: Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection
• Abstract summary: Large language models (LLMs) generate text that increasingly resembles human writing.<n>New models emerge rapidly and reduce the robustness of such shortcuts.<n>We propose a progressively structured framework that disentangles AI-detection semantics from generator-aware artifacts.
• Score: 17.88697261224505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. To tackle this challenge, we propose a progressively structured framework that disentangles AI-detection semantics from generator-aware artifacts. This is achieved through a compact latent encoding that encourages semantic minimality, followed by perturbation-based regularization to reduce residual entanglement, and finally a discriminative adaptation stage that aligns representations with task objectives. Experiments on MAGE benchmark, covering 20 representative LLMs across 7 categories, demonstrate consistent improvements over state-of-the-art methods, achieving up to 24.2% accuracy gain and 26.2% F1 improvement. Notably, performance continues to improve as the diversity of training generators increases, confirming strong scalability and generalization in open-set scenarios. Our source code will be publicly available at https://github.com/PuXiao06/DRGD.

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