arXiv:2508.03829v3 Announce Type: replace
Abstract: The growing deployment of Large Language Models (LLMs) has raised concerns about their misuse in generating harmful or deceptive content. To addres...
By Jiahao Xu, Rui Hu, Olivera Kotevska, Zikai Zhang
TTMark introduces a pairwise watermarking framework that extends distortion‑free watermarking from single tokens to adjacent token pairs, enlarging the watermarking alphabet from V to V². By watermarking the joint distribution of consecutive tokens, the detector can exploit both token entropy and conditional entropy while maintaining distortion‑freeness. Experiments on multiple language models and datasets show that TTMARK improves detectability, robustness to edits, and localized watermark detection without degrading generation quality.
By Ruibo Chen, Zhengmian Hu, Donghang Lu, Xuehao Cui, Georgios Milis, Yihan Wu, Jian Du, Heng Huang
arXiv:2610.27653v1 Announce Type: cross
Abstract: Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical sig...
By Romina Omidi, Nima Naderloui, Yuan Hong, Binghui Wang
arXiv:2601. 11629v2 Announce Type: replace-cross Abstract: We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate at watermarking LM outputs for constrained generation tasks with low-entropy output spaces.
By Nghia T. Le, Alan Ritter, Kartik Goyal
arXiv:2512. 13325v2 Announce Type: replace-cross Abstract: Securing digital text is becoming increasingly relevant due to the widespread use of large language models.
By Malte Hellmeier
The paper introduces the first watermark designed specifically for diffusion language models (DLMs), which generate tokens in arbitrary order unlike traditional autoregressive models. It overcomes the challenge of missing prior tokens by applying the watermark in expectation over the context and promoting tokens that strengthen the watermark when used as context. Experiments show a >99% true positive rate with minimal quality loss and comparable robustness to existing autoregressive watermarks.
By Thibaud Gloaguen, Robin Staab, Nikola Jovanovi\'c, Martin Vechev