PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints

• URL: http://arxiv.org/abs/2509.21057v1
• Date: Thu, 25 Sep 2025 12:08:31 GMT
• Title: PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints
• Authors: Jiahao Huo, Shuliang Liu, Bin Wang, Junyan Zhang, Yibo Yan, Aiwei Liu, Xuming Hu, Mingxun Zhou,
• Abstract summary: We introduce a new theoretical framework on watermark-leveling (SWM) for large language models (LLMs)<n>We propose PMark, a simple yet powerful SWM method that estimates the median next sentence dynamically through sampling channels.<n> Experimental results show that PMark consistently outperforms existing SWM baselines in both text quality and paraphrasing.
• Score: 49.2373408329323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing attacks by treating the sentence as the fundamental unit. However, existing methods still lack strong theoretical guarantees of robustness, and reject-sampling-based generation often introduces significant distribution distortions compared with unwatermarked outputs. In this work, we introduce a new theoretical framework on SWM through the concept of proxy functions (PFs) $\unicode{x2013}$ functions that map sentences to scalar values. Building on this framework, we propose PMark, a simple yet powerful SWM method that estimates the PF median for the next sentence dynamically through sampling while enforcing multiple PF constraints (which we call channels) to strengthen watermark evidence. Equipped with solid theoretical guarantees, PMark achieves the desired distortion-free property and improves the robustness against paraphrasing-style attacks. We also provide an empirically optimized version that further removes the requirement for dynamical median estimation for better sampling efficiency. Experimental results show that PMark consistently outperforms existing SWM baselines in both text quality and robustness, offering a more effective paradigm for detecting machine-generated text. Our code will be released at [this URL](https://github.com/PMark-repo/PMark).

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