MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models

• URL: http://arxiv.org/abs/2601.22246v1
• Date: Thu, 29 Jan 2026 19:10:48 GMT
• Title: MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models
• Authors: Ya Jiang, Massieh Kordi Boroujeny, Surender Suresh Kumar, Kai Zeng,
• Abstract summary: We propose MirrorMark, a distortion-free watermark for large language models (LLMs)<n>MirrorMark embeds multi-bit messages without altering the token probability distribution, preserving text quality by design.<n> Experiments show that MirrorMark matches the text quality of non-watermarked generation while achieving substantially stronger detectability.
• Score: 5.735801967350819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution has become increasingly important. Watermarking is a promising approach, but existing methods either provide only binary signals or distort the sampling distribution, degrading text quality; distortion-free approaches, in turn, often suffer from weak detectability or robustness. We propose MirrorMark, a multi-bit and distortion-free watermark for LLMs. By mirroring sampling randomness in a measure-preserving manner, MirrorMark embeds multi-bit messages without altering the token probability distribution, preserving text quality by design. To improve robustness, we introduce a context-based scheduler that balances token assignments across message positions while remaining resilient to insertions and deletions. We further provide a theoretical analysis of the equal error rate to interpret empirical performance. Experiments show that MirrorMark matches the text quality of non-watermarked generation while achieving substantially stronger detectability: with 54 bits embedded in 300 tokens, it improves bit accuracy by 8-12% and correctly identifies up to 11% more watermarked texts at 1% false positive rate.

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