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

• URL: http://arxiv.org/abs/2606.00402v1
• Date: Fri, 29 May 2026 22:37:13 GMT
• Title: A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
• Abstract summary: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining.<n>We demonstrate reliable FDR control with meaningful detection power across three detection models, 19 domains, and four LLMs.
• Score: 4.28787537081191
• License: http://creativecommons.org/licenses/by/4.0/
• 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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