A new arXiv preprint presents a method for detecting watermarks in language model output by constructing predictive likelihood ratios. The approach builds on prior work by Li et al. (2025) and averages over uncertainty in the detection test. Watermark detection works by testing whether observed tokens depend on pseudorandom values derived from a secret key.
A new arXiv paper proposes SAC-Copula, a watermarking scheme designed for diffusion language models, which generate text through iterative parallel unmasking rather than left-to-right decoding. The authors argue that existing sampling-based watermarking techniques, which add independent noise at each position, are poorly suited to this parallel process. Their approach instead uses smooth, correlated Gumbel fields intended to preserve output quality while embedding a detectable signal.
Article 50 of the EU AI Act took effect on August 2, 2026, requiring generative AI providers to mark their outputs so they can be detected as machine-generated. A new research paper analyzes the state of AI text watermarking under these rules, coming shortly after Anthropic disclosed that Claude outputs carry watermarks. The authors highlight that current text watermarking techniques still lack reliable verification, creating tension with the new compliance requirements.