Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes

Abstract: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-level dependence testing problem to a pivot-induced sequential testing problem with an explicit null distribution. Theoretically, we prove anytime-valid Type I error control under arbitrary optional stopping and establish positive asymptotic log-growth under watermarking, implying consistency of the proposed stopping rules. Simulations and experiments on real LLM-generated text demonstrate efficient online detection with rigorous anytime-valid guarantees.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2607.21958 [stat.ML]

(or arXiv:2607.21958v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2607.21958 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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