{"ID":2852073,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18333","arxiv_id":"2510.18333","title":"Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption","abstract":"Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap stems from misaligned incentives among LLM providers, platforms, and end users, which manifest as three key barriers: competitive risk, detection-tool governance, and attribution issues. We revisit three classes of watermarking through this lens. \\emph{Model watermarking} naturally aligns with LLM provider interests, yet faces new challenges in open-source ecosystems. \\emph{LLM text watermarking} offers modest provider benefit when framed solely as an anti-misuse tool, but can gain traction in narrowly scoped settings such as dataset de-contamination or user-controlled provenance. \\emph{In-context watermarking} (ICW) is tailored for trusted parties, such as conference organizers or educators, who embed hidden watermarking instructions into documents. If a dishonest reviewer or student submits this text to an LLM, the output carries a detectable watermark indicating misuse. This setup aligns incentives: users experience no quality loss, trusted parties gain a detection tool, and LLM providers remain neutral by simply following watermark instructions. We advocate for a broader exploration of incentive-aligned methods, with ICW as an example, in domains where trusted parties need reliable tools to detect misuse. More broadly, we distill design principles for incentive-aligned, domain-specific watermarking and outline future research directions. Our position is that the practical adoption of LLM watermarking requires aligning stakeholder incentives in targeted application domains and fostering active community engagement.","short_abstract":"Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap stems from misaligned incentives among LLM providers, platforms, and end users, which manifest as three key barriers: competitive risk, detection-tool governance, and attribution i...","url_abs":"https://arxiv.org/abs/2510.18333","url_pdf":"https://arxiv.org/pdf/2510.18333v2","authors":"[\"Yepeng Liu\",\"Xuandong Zhao\",\"Dawn Song\",\"Gregory W. Wornell\",\"Yuheng Bu\"]","published":"2025-10-21T06:34:51Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\",\"Generative Adversarial Network\"]","has_code":false}