We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity, along with entropy-weighted scoring and multi-region localization for improved detection. It supports serving optimizations such as speculative decoding and multi-token prediction, and does not add any inference overhead. TextSeal strictly dominates baselines like SynthID-text in detection strength and is robust to dilution, maintaining confident localized detection even in heavily mixed human/AI documents. The scheme is theoretically distortion-free, and evaluation across reasoning benchmarks confirms that it preserves downstream performance; while a multilingual human evaluation (6000 A/B comparisons, 5 languages) shows no perceptible quality difference. Beyond its use for provenance detection, TextSeal is also ``radioactive'': its watermark signal transfers through model distillation, enabling detection of unauthorized use.
@article{arxiv.2605.12456,
title = {TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection},
author = {Tom Sander and Hongyan Chang and Tomáš Souček and Tuan Tran and Valeriu Lacatusu and Sylvestre-Alvise Rebuffi and Alexandre Mourachko and Surya Parimi and Christophe Ropers and Rashel Moritz and Vanessa Stark and Hady Elsahar and Pierre Fernandez},
journal= {arXiv preprint arXiv:2605.12456},
year = {2026}
}