We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining. Our key observation is that rewrite-based detection implicitly constructs knockoff samples, enabling LLM-generated text detection to be formulated as a multiple hypothesis testing problem with knockoff structure. This perspective separates the design of detection statistics from the control of false discoveries, allowing existing rewrite detectors to inherit finite-sample false discovery rate (FDR) guarantees through a simple calibration procedure. We demonstrate reliable FDR control with meaningful detection power across three detection models, 19 domains, and four LLMs.

翻译：我们提出一种无分布统计框架，可将任意基于重写的检测器转化为具有有限样本FDR保证的检测器，且无需重新训练。关键发现是：基于重写的检测隐式构造了knockoff样本，使得LLM生成文本检测可被建模为具有knockoff结构的多元假设检验问题。该视角将检测统计量设计与错误发现控制相分离，通过简单校准流程使现有重写检测器获得有限样本错误发现率（FDR）保证。我们在三种检测模型、19个领域和四个LLM上验证了可靠的FDR控制与有意义的检测效能。