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.

翻译：我们提出TextSeal，一种面向大语言模型的先进水印技术。该方法基于Gumbel-max采样，引入双密钥生成机制以恢复输出多样性，结合熵加权评分与多区域定位技术提升检测性能。TextSeal支持推测解码、多词元预测等推理优化，且不引入额外推理开销。该方案在检测强度上严格优于SynthID-text等基线方法，并对稀释攻击具有鲁棒性——即便在人/机混合文档中仍能保持可靠的局部化检测能力。理论上该方案无失真，在推理基准测试中的评估证实其不会损害下游性能；多语言人工评估（6000组A/B对比，5种语言）表明，水印引入的可感知质量差异为零。除溯源检测外，TextSeal还具有“放射性”特性：其水印信号可通过模型蒸馏进行传递，从而实现对未授权使用的检测。