# SimpleMark: Fast Multi-Bit Text Watermarking under f -Divergence Constraints

arXiv: https://arxiv.org/abs/2610.05712 · HTML: https://mlsanity.com/paper/2610.05712/

## Key facts

- No citing papers in the map yet (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 42 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 21 of 21)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://mlsanity.com/paper/2311.04378/) (2023)
- [Advancing Beyond Identification: Multi-bit Watermark for Large Language Models](https://mlsanity.com/paper/2308.00221/) (2023)
- [Provably Robust Multi-bit Watermarking for AI-generated Text](https://mlsanity.com/paper/2401.16820/) (2024)
- [Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach](https://mlsanity.com/paper/2410.02890/) (2024)
- [Watermarking Language Models for Many Adaptive Users](https://mlsanity.com/paper/2405.11109/) (2024)
- [StealthInk: A Multi-bit and Stealthy Watermark for Large Language Models](https://mlsanity.com/paper/2506.05502/) (2025)
- [Robust Multi-bit Text Watermark with LLM-based Paraphrasers](https://mlsanity.com/paper/2412.03123/) (2024)
- [MC2Mark: Distortion-Free Multi-Bit Watermarking for Long Messages](https://mlsanity.com/paper/2602.14030/) (2026)
- [MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking](https://mlsanity.com/paper/2601.22246/) (2026)
- [ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport](https://mlsanity.com/paper/2602.07235/) (2026)
- [XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts](https://mlsanity.com/paper/2604.05242/) (2026)
- [QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs](https://mlsanity.com/paper/2604.13786/) (2026)
- [Feedback Coding Enables Inference-Time Covert Agentic Communication](https://mlsanity.com/paper/2609.24994/) (2026)

## Abstract (arXiv)

We introduce a framework for multi-bit text watermarking with security defined directly through f-divergence from the base language model distribution. Unlike prior approaches that focus on average-key distortion-freeness or a particular statistical distance, our formulation supports general f-divergences, including total variation and KL divergence, and enforces the guarantee for each realized key and embedded message. We develop a coding-based watermarking scheme that optimally biases next-token distributions subject to a prescribed divergence budget, and characterize the resulting tradeoff between embedding rate, decoding reliability, and statistical security. Experimentally, we compare our method against prior multi-bit watermarking schemes across modern language models and payload regimes. Our approach achieves substantially lower watermark detectability while maintaining competitive message-recovery performance and generation quality. Our results provide a unified view of secure multi-bit watermarking and recover several commonly used security notions as special cases.