Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark
On the map: LLM safety and security → Watermarking generated content and models
Abstract (arXiv)
With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or timestamps, into the generated text. In this work, we propose a fundamentally new approach for multibit watermarking: introducing binomial encoding to directly encode every bit of the payload at every token position. We complement our approach with a stateful encoder that during generation dynamically redirects encoding pressure toward underencoded bits. Our evaluation against 8 baselines on up to 64-bit payloads shows that our scheme achieves superior message accuracy and robustness, with the gap to baseline methods widening in more relevant settings (i.e., large payloads and low-distortion regimes). At the same time, we challenge prior works' evaluation metrics, highlighting their lack of practical insights, and introduce per-bit confidence scoring as a practically relevant metric for evaluating multibit LLM watermarks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 21 of its 32 references that are in the map. First posted on arXiv 12 May 2026.
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21 of 32 references.
Closest ancestors · 14
Same direction, newest first
- 2026MC2Mark: Distortion-Free Multi-Bit Watermarking for Long Messages15 Feb
- 2026ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport6 Feb
- 2026A Unified Framework for LLM Watermarks6 Feb
- 2025Watermarking Diffusion Language Models29 Sep
- 2025BiMark: Unbiased Multilayer Watermarking for Large Language Models19 Jun
- 2025StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models5 Jun
- 2025Improved Unbiased Watermark for Large Language Models16 Feb
- 2024MarkLLM: An Open-Source Toolkit for LLM Watermarking16 May
- 2024Provably Robust Multi-bit Watermarking for AI-generated Text30 Jan
- 2023A Robust Semantics-based Watermark for Large Language Model against Paraphrasing15 Nov
- 2023Advancing Beyond Identification: Multi-bit Watermark for Large Language Models1 Aug
- 2023Robust Distortion-free Watermarks for Language Models28 Jul
2 more
Other references · 7
- 2021Training Verifiers to Solve Math Word Problemscited by 10,109
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2023Let's Verify Step by Stepcited by 3,789
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2019ELI5: Long Form Question Answeringcited by 575
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Citing papers per year: 2026: 1.
1 paper.
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Close in wording, no citation either way
- 2023Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
- 2026WorldCup Sampling for Multi-bit LLM Watermarking
- 2026XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts
- 2026WeaveMark: Robust and Scalable Multi-bit LLM Watermarking via Coded Payload Spreading
- 2025HeavyWater and SimplexWater: Distortion-Free LLM Watermarks for Low-Entropy Next-Token Predictions
- 2026QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs
- 2026MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking
- 2025Majority Bit-Aware Watermarking For Large Language Models