LLM Watermarking Using Mixtures and Statistical-to-Computational Gaps

Pedro Abdalla; Roman Vershynin

LLM Watermarking Using Mixtures and Statistical-to-Computational Gaps

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arxiv 2505.01484 v2 pith:3MUBOPQL submitted 2025-05-02 cs.CR cs.LG

Pedro Abdalla , Roman Vershynin This is my paper · ORCID

classification cs.CRcs.LG

keywords watermarkingmodelproposeschemesettingaccessadversaryapproach

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Given a text, can we determine whether it was generated by a large language model (LLM) or by a human? A widely studied approach to this problem is watermarking. We propose an undetectable and elementary watermarking scheme in the closed setting. Also, in the harder open setting, where the adversary has access to most of the model, we propose an unremovable watermarking scheme.

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Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection
stat.AP 2025-07 conditional novelty 6.0 of 10

Standard, hierarchical, and weighted conformal prediction applied to LLM watermark scores can control false-positive rates when detecting guideline-violating AI edits in simulated classroom essays.

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