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[2509.24368] Watermarking Diffusion Language Models
Summary
This article presents a novel watermarking technique specifically designed for diffusion language models (DLMs), addressing challenges in applying existing methods from autoregressive models.
Why It Matters
As diffusion language models gain traction in AI, ensuring the integrity and traceability of generated content becomes crucial. This research provides a reliable watermarking solution, enhancing security and accountability in generative AI applications.
Key Takeaways
- Introduces the first watermark tailored for diffusion language models.
- Addresses challenges in watermarking due to the non-sequential nature of DLMs.
- Demonstrates a >99% true positive rate with minimal impact on output quality.
- Maintains robustness comparable to existing autoregressive model watermarks.
- Enhances the security and traceability of AI-generated content.
Computer Science > Machine Learning arXiv:2509.24368 (cs) [Submitted on 29 Sep 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Watermarking Diffusion Language Models Authors:Thibaud Gloaguen, Robin Staab, Nikola Jovanović, Martin Vechev View a PDF of the paper titled Watermarking Diffusion Language Models, by Thibaud Gloaguen and 3 other authors View PDF HTML (experimental) Abstract:We introduce the first watermark tailored for diffusion language models (DLMs), an emergent LLM paradigm able to generate tokens in arbitrary order, in contrast to standard autoregressive language models (ARLMs) which generate tokens sequentially. While there has been much work in ARLM watermarking, a key challenge when attempting to apply these schemes directly to the DLM setting is that they rely on previously generated tokens, which are not always available with DLM generation. In this work we address this challenge by: (i) applying the watermark in expectation over the context even when some context tokens are yet to be determined, and (ii) promoting tokens which increase the watermark strength when used as context for other tokens. This is accomplished while keeping the watermark detector unchanged. Our experimental evaluation demonstrates that the DLM watermark leads to a >99% true positive rate with minimal quality impact and achieves similar robustness to existing ARLM watermarks, enabling for the first time reliable DLM watermarking. Subjects: Machine Learning (cs.LG); Ar...
