Optimal Watermark Localization in Mixed-Source Large Language Model Texts
Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mixed-source, with watermark evidence surviving at only a subset of token positions after rewriting, insertion, deletion, or paraphrasing. Although prior work has studied global detection of watermark signals, when such signals can be localized remains unclear. We formulate watermark localization as a token-level multiple-testing problem based on pivotal statistics, with a latent indicator recording whether watermark dependence survives at each position. Under an asymptotic regime indexed by exponents for signal sparsity, next-token concentrati...
| Authors | Jose H. Blanchet, T. Tony Cai, Xiang Li et al. |
|---|---|
| Primary category | stat.ME |
| Published | 14 August 2026 |
| Last updated at source | 15 October 2026 |
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