# CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models

arXiv: https://arxiv.org/abs/2510.02342 · HTML: https://mlsanity.com/paper/2510.02342/

## Key facts

- Cited by 3 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 28 of its 34 references that are in the map.
- First posted on arXiv 27 Sep 2025.

## 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, 28 of 28)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [Who Wrote this Code? Watermarking for Code Generation](https://mlsanity.com/paper/2305.15060/) (2023)
- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [Unbiased Watermark for Large Language Models](https://mlsanity.com/paper/2310.10669/) (2023)
- [REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models](https://mlsanity.com/paper/2310.12362/) (2023)
- [An Entropy-based Text Watermarking Detection Method](https://mlsanity.com/paper/2403.13485/) (2024)
- [Adaptive Text Watermark for Large Language Models](https://mlsanity.com/paper/2401.13927/) (2024)
- [Advancing Beyond Identification: Multi-bit Watermark for Large Language Models](https://mlsanity.com/paper/2308.00221/) (2023)
- [WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models](https://mlsanity.com/paper/2311.07138/) (2023)
- [PostMark: A Robust Blackbox Watermark for Large Language Models](https://mlsanity.com/paper/2406.14517/) (2024)
- [WatME: Towards Lossless Watermarking Through Lexical Redundancy](https://mlsanity.com/paper/2311.09832/) (2023)
- [Towards Optimal Statistical Watermarking](https://mlsanity.com/paper/2312.07930/) (2023)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023)
- [Towards Watermarking of Open-Source LLMs](https://mlsanity.com/paper/2502.10525/) (2025)
- [ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs](https://mlsanity.com/paper/2403.09724/) (2024)
- [Watermarking Generative Tabular Data](https://mlsanity.com/paper/2405.14018/) (2024)
- [Cross-Attention Watermarking of Large Language Models](https://mlsanity.com/paper/2401.06829/) (2024)
- [ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination](https://mlsanity.com/paper/2409.14285/) (2024)
- [Authorship and the Politics and Ethics of LLM Watermarks](https://mlsanity.com/paper/2403.06593/) (2024)

## Built on by (most cited first, 3 of 3)

- [Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models](https://mlsanity.com/paper/2601.05144/) (2026)
- [Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation](https://mlsanity.com/paper/2604.12424/) (2026)
- [WorldMark: A Plug-and-Play World Knowledge Interface for Cross-Host Language Model Watermarking](https://mlsanity.com/paper/2608.06416/) (2026)

## Abstract (arXiv)

Watermarking algorithms for Large Language Models (LLMs) effectively identify machine-generated content by embedding and detecting hidden statistical features in text. However, such embedding leads to a decline in text quality, especially in low-entropy scenarios where performance needs improvement. Existing methods that rely on entropy thresholds often require significant computational resources for tuning and demonstrate poor adaptability to unknown or cross-task generation scenarios. We propose Context-Aware Threshold watermarking (myalgo), a novel framework that dynamically adjusts watermarking intensity based on real-time semantic context. myalgo partitions text generation into semantic states using logits clustering, establishing context-aware entropy thresholds that preserve fidelity in structured content while embedding robust watermarks. Crucially, it requires no pre-defined thresholds or task-specific tuning. Experiments show myalgo improves text quality in cross-tasks without sacrificing detection accuracy.