Towards Reliable Marking and Verification of AI-Generated Text via Geometry-aware Sentence-level Watermarking

Large generative models raise growing concerns about provenance, misinformation, and impersonation. Digital watermarking offers a principled solution, yet extending it to natural language remains challenging due to text discreteness and sensitivity to semantic perturbations. Existing text watermarking methods either operate at the token level requiring white-box access and remaining fragile to paraphrasing, or at the sentence level, which supports black-box deployment but suffers from low Watermark Success Rate (WSR). We show that low WSR in sentencelevel watermarking primarily stems from low injection success probability caused by a mismatch between posterior embedding distributions and semantic accept regions. Based on this insight, we propose X-Guard, a geometry-aware sentencelevel watermarking framework that improves injection success by systematically optimizing embedding distributions and semantic space partitioning. X-Guard learns a more isotropic embedding space and introduces A 2 PQ, a centroidaligned partitioning scheme that approximately equalizes probability mass across regions. Extensive experiments across multiple models, languages, and attack settings demonstrate that X-Guard consistently improves robustness while preserving text fluency and practical deployability. Code and data are available at https: //github.com/lilice-r/X-Guard.