SOURCE-LINKED INTELLIGENCE
Robust Detection of LLM-Generated Text under Contamination
We study the detection of LLM-generated text under editing and contamination. Modeling human and machine text as finite-order Markov processes with Huber contamination, we characterize an exact boundary for reliable detection under our assumptions. Detection is impossible when contamination is sufficiently large relative to clean-source separation. Below this boundary, a collection of clipped likelihood-ratio tests achieves vanishing worst-case errors. This construction motivates clipping as a simple modification of existing statistical detectors. For a broad class of additive scores, we ident
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-24T15:01:38.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.