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

Researchers propose a distribution-free statistical framework that enhances rewrite-based LLM detection systems with finite-sample false discovery rate (FDR) guarantees without requiring model retraining. By formulating detection as a knockoff-based multiple hypothesis testing problem, the framework enables existing detectors to inherit statistical guarantees through a simple calibration procedure, validated across multiple detection models, domains, and language models.

This research addresses a critical challenge in AI safety: reliably detecting large language model-generated text while maintaining statistical rigor. The key innovation lies in recognizing that rewrite-based detection methods implicitly construct knockoff samples—synthetic variations of original text—which can be leveraged through established statistical testing frameworks. This perspective fundamentally decouples the engineering of detection statistics from the mathematical control of false positives, a separation that carries significant implications for deployment.

The broader context reflects growing concern about LLM-generated content in academic integrity, content authenticity, and information ecosystems. Existing detection methods often lack formal guarantees about their error rates, making them unreliable for high-stakes applications. This framework bridges that gap by providing finite-sample FDR control, meaning practitioners can know exactly how many false positives to expect under statistical guarantees rather than relying on empirical performance metrics alone.

For developers and organizations deploying content detection systems, this work offers practical value. The framework's model-agnostic approach means institutions can improve existing detection pipelines through calibration rather than complete replacement. The validation across 19 domains and four different LLMs demonstrates generalization capability, suggesting robust performance in diverse real-world scenarios.

The implications extend to trust infrastructure in AI systems. As LLM capabilities improve, detection methods require stronger theoretical foundations. This framework establishes such foundations while remaining computationally practical. Future work likely involves adapting these principles to emerging detection challenges and exploring whether similar statistical frameworks can enhance other AI safety verification methods.

• →The framework converts existing rewrite-based detectors into statistically-guaranteed systems without model retraining through a simple calibration procedure.
• →By formulating detection as a knockoff-based hypothesis testing problem, the approach provides finite-sample FDR control with measurable false positive guarantees.
• →Validation across 19 domains and four LLMs demonstrates the method maintains reliability and detection power across diverse scenarios.
• →The distribution-free approach separates detection statistics design from false discovery control, enabling broader applicability across different detector architectures.
• →The framework provides formal statistical guarantees rather than empirical metrics, critical for high-stakes applications like academic integrity verification.