WeaveMark: Robust and Scalable Multi-bit LLM Watermarking via Coded Payload Spreading

Abstract:Multi-bit watermarking for large language models (LLMs) enables content source tracing by embedding user-identifiable messages into generated text. Existing methods face a fundamental trade-off among extraction accuracy, text quality, and payload capacity. We propose WeaveMark, a robust and scalable multi-bit LLM watermarking scheme based on coded payload spreading. WeaveMark shifts this trade-off frontier by improving payload capacity through multi-bit-per-token spreading, improving extraction accuracy through soft-decision error-correcting code, and preserving text quality through unbiased multilayer reweighting. It further introduces dedicated zero-bit layers for reliable watermark presence detection. Experiments show large gains, especially for long messages and edited text. WeaveMark achieves 89.8% match rate for 32-bit messages at 200 tokens, compared with 20.8% for BiMark. Under 10% substitution attacks on 16-bit messages at 200 tokens, it maintains 86.0% versus 30.7%, while preserving text quality. Our code is available at this https URL.

16 pages, 11 figures. Code: this https URL

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2609.02177 [cs.CR]

(or arXiv:2609.02177v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2609.02177 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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