A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering

Yi Liu

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arXiv · 2606.00402

A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering

Yi Liu

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Abstract

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.

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Yi Liu. 2026-05-29. A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering. https://arxiv.org/abs/2606.00402

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