The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 dominant paradigms (training-free and supervised) and show that both are brittle under distribution shift, unseen generators, and simple stylistic perturbations. To address these limitations, we propose a supervised contrastive learning (SCL) framework that learns discriminative style embeddings. Experiments show that while supervised detectors excel in-domain, they degrade sharply out-of-domain, and training-free methods remain highly sensitive to proxy choice. Overall, our results expose fundamental challenges in building domain-agnostic detectors. Our code is available at: https://github.com/HARSHITJAIS14/DetectAI

翻译：随着大型语言模型（LLMs）的快速普及，对可靠AI文本检测的需求日益增长，然而现有检测器在受控基准测试之外常常失效。我们系统评估了两种主流范式（无训练方法与监督学习方法），结果表明二者在分布偏移、未知生成器及简单风格扰动下均表现脆弱。为应对这些局限，我们提出了一个监督对比学习（SCL）框架，用于学习具有判别力的风格嵌入向量。实验表明，监督检测器虽在域内表现优异，但在域外性能急剧下降；而无训练方法对代理选择仍高度敏感。总体而言，我们的研究揭示了构建领域无关检测器面临的根本性挑战。代码已开源：https://github.com/HARSHITJAIS14/DetectAI