ByteMark: A Unified Byte-Level Watermark for Detection Across Language Models
cs.CR, cs.LG, stat.ML
Submitted: 2026-10-10
Updated: 2026-10-10
Code: https://github.com/PrithivirajDamodaran/Parrot_Paraphraser
Project page: https://w3techs.com/technologies/overview/character_encoding
License: http://creativecommons.org/licenses/by/4.0/
The gist: As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown.
Abstract
As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown. Existing detectors read the watermark through a specific tokenizer or embedding pipeline, and even if all providers adopted the same watermarking method, differences in these representations would leave their watermarks incompatible, and the text alone does not reveal which representation produced it. We propose ByteMark, a unified watermarking scheme that defines its signal on the UTF-8 bytes of published text. Models with different tokenizers embed this signal under one public specification and key, and a single detector verifies the text from the text and key alone. When the tokenizer or embedding model of a baseline detector is replaced, its detection degrades sharply, whereas ByteMark, which uses neither, retains its detection rate; ByteMark also remains detectable when participating models continue or rewrite the text.
Sources
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- PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints
- SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness
- People cannot distinguish GPT-4 from a human in a Turing test
- Robust Distortion-free Watermarks for Language Models
- AliMark: Enhancing Robustness of Sentence-Level Watermarking Against Text Paraphrasing
- In-Context Watermarks for Large Language Models
- Pointer Sentinel Mixture Models
- Qwen2.5 Technical Report
- Embarrassingly Simple Text Watermarks
- Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings
- Mixture-of-Agents Enhances Large Language Model Capabilities
- Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality
- TinyLlama: An Open-Source Small Language Model
- OPT: Open Pre-trained Transformer Language Models
- Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
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