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R-CoT: A Reasoning-Layer Watermark via Redundant Chain-of-Thought in Large Language Models

arXiv Security Archived Apr 29, 2026 ✓ Full text saved

arXiv:2604.25247v1 Announce Type: new Abstract: Large language models (LLMs) are widely deployed in multiple scenarios due to reasoning capabilities. In order to prevent the models from being misused, watermarking is generally employed to ensure ownership. However, most existing watermarking methods rely on superficial modifications to the model's output distribution, rendering the watermark vulnerable to perturbation and removal. To overcome this challenge, this paper introduces a reasoning-lay

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    Computer Science > Cryptography and Security [Submitted on 28 Apr 2026] R-CoT: A Reasoning-Layer Watermark via Redundant Chain-of-Thought in Large Language Models Ziming Zhang, Li Li, Guorui Feng, Hanzhou Wu, Xinpeng Zhang Large language models (LLMs) are widely deployed in multiple scenarios due to reasoning capabilities. In order to prevent the models from being misused, watermarking is generally employed to ensure ownership. However, most existing watermarking methods rely on superficial modifications to the model's output distribution, rendering the watermark vulnerable to perturbation and removal. To overcome this challenge, this paper introduces a reasoning-layer framework termed Redundant Chain-of-Thought (R-CoT), which embeds watermarks into the reasoning path. A dual-trajectory optimization mechanism based on GRPO enables the native and the watermark reasoning path to coexist within a shared parameter space, internalizing the watermark as a distinct reasoning policy. Therefore, the watermark is embedded into the model's stable reasoning path, avoiding the watermark failure caused by output-level perturbations. Experimental results show that, compared with existing methods, R-CoT achieves high watermark effectiveness and strong robustness. Under fine-tuning and other post-training operations, the true positive rate (TPR) consistently remains above 95%, exhibiting only marginal degradation. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.25247 [cs.CR]   (or arXiv:2604.25247v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.25247 Focus to learn more Submission history From: Hanzhou Wu [view email] [v1] Tue, 28 Apr 2026 05:52:57 UTC (472 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 29, 2026
    Archived
    Apr 29, 2026
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