# A Unified Framework for LLM Watermarks

arXiv: https://arxiv.org/abs/2602.06754 · HTML: https://mlsanity.com/paper/2602.06754/

## Key facts

- Cited by 5 papers 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 20 of its 27 references that are in the map.
- First posted on arXiv 6 Feb 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, 20 of 20)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Three Bricks to Consolidate Watermarks for Large Language Models](https://mlsanity.com/paper/2308.00113/) (2023)
- [A Robust Semantics-based Watermark for Large Language Model against Paraphrasing](https://mlsanity.com/paper/2311.08721/) (2023)
- [Jointly Measuring Diversity and Quality in Text Generation Models](https://mlsanity.com/paper/1904.03971/) (2019)
- [A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules](https://mlsanity.com/paper/2404.01245/) (2024)
- [Towards Optimal Statistical Watermarking](https://mlsanity.com/paper/2312.07930/) (2023)
- [Optimizing watermarks for large language models](https://mlsanity.com/paper/2312.17295/) (2023)
- [Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature](https://mlsanity.com/paper/2406.01946/) (2024)
- [Black-Box Detection of Language Model Watermarks](https://mlsanity.com/paper/2405.20777/) (2024)
- [Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach](https://mlsanity.com/paper/2410.02890/) (2024)
- [Improved Unbiased Watermark for Large Language Models](https://mlsanity.com/paper/2502.11268/) (2025)
- [Optimized Couplings for Watermarking Large Language Models](https://mlsanity.com/paper/2505.08878/) (2025)
- [Enhancing LLM Watermark Resilience Against Both Scrubbing and Spoofing Attacks](https://mlsanity.com/paper/2507.06274/) (2025)
- [Analyzing and Evaluating Unbiased Language Model Watermark](https://mlsanity.com/paper/2509.24048/) (2025)
- [SimKey: A Semantically Aware Key Module for Watermarking Language Models](https://mlsanity.com/paper/2510.12828/) (2025)

## Built on by (most cited first, 5 of 5)

- [Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark](https://mlsanity.com/paper/2605.11653/) (2026)
- [Watermark Forensics for Generative Models: An Information-Theoretic Perspective](https://mlsanity.com/paper/2607.13003/) (2026)
- [Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts](https://mlsanity.com/paper/2607.20462/) (2026)
- [WoE Wrote It? Watermarking Mixture-of-Experts LLMs for Black-Box Text Provenance](https://mlsanity.com/paper/2608.29151/) (2026)
- [AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks](https://mlsanity.com/paper/2609.37310/) (2026)

## Abstract (arXiv)

LLM watermarks allow tracing AI-generated texts by inserting a detectable signal into their generated content. Recent works have proposed a wide range of watermarking algorithms, each with distinct designs, usually built using a bottom-up approach. Crucially, there is no general and principled formulation for LLM watermarking. In this work, we show that most existing and widely used watermarking schemes can in fact be derived from a principled constrained optimization problem. Our formulation unifies existing watermarking methods and explicitly reveals the constraints that each method optimizes. In particular, it highlights an understudied quality-diversity-power trade-off. At the same time, our framework also provides a principled approach for designing novel watermarking schemes tailored to specific requirements. For instance, it allows us to directly use perplexity as a proxy for quality, and derive new schemes that are optimal with respect to this constraint. Our experimental evaluation validates our framework: watermarking schemes derived from a given constraint consistently maximize detection power with respect to that constraint.