Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes
As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written content have become essential. Statistical watermarking has emerged as a promising solution, yet most existing methods are typically fixed-horizon procedures, precluding valid early stopping in streaming generation. In this paper, we develop an efficient online watermark detection framework with anytime-valid inference based on Rao-Blackwellized e-processes, enabling recursive token-level evidence updates without storing the full history. In particular, we instantiate the framework for the Gumbel-max watermark and reduce the original token-le...
| Authors | Lu Luo, Dandan Mo, Chengdong Xu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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