arXiv:2605.28890v1 Announce Type: cross Abstract: Large Language Models with Chain-of-Thought reasoning capabilities represent valuable intellectual property, yet existing black-box watermarking methods often trade robustness for reasoning fidelity by perturbing final answers or relying on fragile trigger patterns. We propose BiCoT, a watermarking framework that embeds ownership signals into the internal geometry of reasoning traces by aligning high-saliency structural anchors with a private signature subspace while regularizing ordinary control tokens to preserve semantic capacity. This desig
The increasing commercial value and intellectual property concerns around large language models spurred the development of more robust watermarking techniques for AI outputs.
This development allows for better protection of proprietary AI models, potentially influencing the economic incentives for developing advanced reasoning capabilities.
The ability to embed stealthy ownership signals directly into the internal reasoning processes of LLMs improves IP protection without significantly degrading model performance.
- · AI model developers
- · Companies investing in LLM IP
- · Consulting firms using advanced LLMs
- · Pirates of AI models
- · Competitors reliant on reverse engineering
Increased confidence for AI developers to invest in and deploy proprietary large language models.
Potential for new business models around licensing and auditing of AI reasoning traces.
Impact on open-source AI development as proprietary models become more secure and differentiated.
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Read at arXiv cs.LG