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[2511.02083] Watermarking Discrete Diffusion Language Models
Summary
This article presents a novel watermarking technique for discrete diffusion language models (DDLMs), addressing the need for reliable detection of AI-generated content while ensuring minimal distortion and ease of deployment.
Why It Matters
As AI-generated content becomes more prevalent, distinguishing between human and machine-generated text is crucial for authenticity and trust. This research contributes to the field by providing a practical solution for watermarking DDLMs, which are gaining traction due to their efficiency. The findings could have significant implications for content verification in various applications, including media, academia, and online platforms.
Key Takeaways
- Introduces a watermarking method specifically for discrete diffusion language models (DDLMs).
- Employs a distribution-preserving Gumbel-max sampling technique for reliable watermark detection.
- Demonstrates that the watermark is distortion-free with a low false detection probability.
- Offers a straightforward deployment process without the need for extensive hyperparameter tuning.
- Highlights the importance of watermarking in the context of increasing AI-generated content.
Computer Science > Cryptography and Security arXiv:2511.02083 (cs) [Submitted on 3 Nov 2025 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Watermarking Discrete Diffusion Language Models Authors:Avi Bagchi, Akhil Bhimaraju, Moulik Choraria, Daniel Alabi, Lav R. Varshney View a PDF of the paper titled Watermarking Discrete Diffusion Language Models, by Avi Bagchi and 4 other authors View PDF HTML (experimental) Abstract:Watermarking has emerged as a promising technique to track AI-generated content and differentiate it from authentic human creations. While prior work extensively studies watermarking for autoregressive large language models (LLMs) and image diffusion models, it remains comparatively underexplored for discrete diffusion language models (DDLMs), which are becoming popular due to their high inference throughput. In this paper, we introduce one of the first watermarking methods for DDLMs. Our approach applies a distribution-preserving Gumbel-max sampling trick at every diffusion step and seeds the randomness by sequence position to enable reliable detection. We empirically demonstrate reliable detectability on LLaDA, a state-of-the-art DDLM. We also analytically prove that the watermark is distortion-free, with a false detection probability that decays exponentially in the sequence length. A key practical advantage is that our method realizes desired watermarking properties with no expensive hyperparameter tuning, making it straightforward to deploy an...
