The paper discusses the EU AI Act’s requirement for generative AI providers to embed detectable watermarks in their outputs, noting that Anthropic’s Claude models and Google’s Gemini use SynthID‑Text by default. It critiques the lack of verifiability of claims about watermark quality, privacy, and robustness, and evaluates the open‑source SynthID‑Text implementation on two open‑weight models, finding minimal impact on prose and modest correctness loss on code. The authors argue that the real governance issue is the inability to verify these assertions and outline necessary steps—such as output release, configuration disclosure, accredited audits, shared evaluation protocols, and interoperable detection—to address the gaps.
By Alexander Nemecek, Vipin Chaudhary, Erman Ayday
arXiv:2610.15725v2 Announce Type: cross
Abstract: Agent skills encode operational procedures and domain expertise as reusable knowledge assets, yet their distribution in plaintext exposes them to una...
By Alan Tang, Lifeng Wang, Gang Wu, Tao Zhang, Yantao Jia, Xiaofeng Zhu
arXiv:2609.37310v1 Announce Type: cross
Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Ye...
By Thibaud Gloaguen, Robin Staab, Martin Vechev
The paper introduces "watermarkability," a metric that quantifies how compatible a language model is with a logit‑based watermark that boosts the probability of a specified token set during decoding. By measuring the expected minimum KL divergence needed to achieve a target probability mass, the authors can assess and compare models and watermark rules. They propose a two‑stage approach—first training a low‑rank adapter conditioned on the target set, then applying a minimal uniform logit bias—to reduce this cost, achieving significant perplexity reductions across multiple watermark schedules on OPT‑125M and Qwen3‑8B.
By Zewei Deng, Mohamed Seif, Liyan Xie
arXiv:2606. 18430v1 Announce Type: new Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited.
By Chih-Duo Hong, Yen-Pang Chen, Fang Yu
MarkTune is a model‑embedded watermarking framework that improves the trade‑off between generation quality and detectability. By regularizing toward a high‑quality reference distribution, it decouples quality preservation from proximity to the base model and uses the GaussMark detection statistic as a reward during on‑policy fine‑tuning. Empirical results show that MarkTune enhances the quality‑detectability frontier, maintains downstream task performance, and remains robust against text‑editing and open‑weight attacks across diverse datasets.
By Yizhou Zhao, Zhiwei Steven Wu, Adam Block
arXiv:2605.23175v2 Announce Type: replace-cross
Abstract: Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collectin...
By Kieu Dang, Phung Lai, NhatHai Phan, Yelong Shen, Ruoming Jin
The paper introduces an adaptive embedding displacement attack (EDA) that exploits rewording, reordering, and resegmentation to remove semantic watermarks from text, achieving a 32.6%–47.9% success rate across four watermarking schemes. To counter this, the authors propose k‑SwordStamp, a semantic watermarking method that uses order‑robust detection over sub‑sentence units, significantly reducing vulnerability to structure‑based edits. Experiments show that EDA remains effective against k‑SwordStamp, but with a lower success rate (10.8%) compared to its performance on other schemes.
By Abdulrahman Diaa, Jonathan Petit, Florian Kerschbaum
arXiv:2607. 13099v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success but raise growing concerns about content provenance and misuse, motivating the need for reliable watermarking techniques.
By Z Sun, Q Jiang, S Sheng, L Xiang
arXiv:2609.38722v1 Announce Type: cross
Abstract: LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during...
By Zewei Deng, Muhammad Siddeek, Liyan Xie, Mohamed Seif, Mengdi Wang, H. Vincent Poor, Andrea Goldsmith
The paper presents a systematic framework for large language model (LLM) watermarking as a provenance tool in big data ecosystems. It categorizes existing watermarking methods along four deployment dimensions—insertion point, verification authority, operational state, and transformation threat model—and aligns them with the big data principles of Volume, Velocity, Variety, Veracity, and Value. The authors introduce a readiness framework that maps four key workloads—online generation, streaming detection, transformation pipelines, and ecosystem governance—to system-level requirements such as throughput, false-positive control, robustness, cross-domain reliability, governance, and downstream utility, while highlighting gaps between benchmark performance and real-world deployment readiness.
By Huy Phan, Kieu Dang, Ojaswi Dulal, Aiham AL Shukairi, Abby Shine, Chase Garner, Phung Lai
arXiv:2608. 12713v1 Announce Type: cross Abstract: Watermarking LLM-generated text is an important task for tracing its provenance.
By Xiaoyan Feng, Yanjun Zhang, He Zhang, Leo Yu Zhang, Shirui Pan