arXiv AI June 17, 2026 By Bingxue Zhang, Xiaofeng Xu, Feida Zhu

LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains

arXiv:2606. 17123v1 Announce Type: cross Abstract: In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 31

OpenStamp: A Watermark for Open-Source Language Models

arXiv:2608.27899v1 Announce Type: cross Abstract: With the growing prevalence of large language model (LLM) generated content, watermarking is considered a promising approach for attributing text to...

By Miroojin Bakshi, Saksham Rastogi, Danish Pruthi

llms fine-tuning

arXiv AI

Jun 11

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

arXiv:2606. 11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures.

By Jian-Ping Mei, Weibin Zhang, Ao Yao, Tiantian Zhu, Jie Xiao

rag fine-tuning

arXiv Computer Vision

3d ago

Persistent Watermarking of Text-to-Image Models

arXiv:2609.39024v1 Announce Type: new Abstract: Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyrigh...

By Dixi Yao, Kaiwen Chen, Tahseen Rabbani, Tian Li

diffusion fine-tuning

arXiv Machine Learning

Jun 18

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models

arXiv:2606. 18430v1 Announce Type: new Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited.

By Chih-Duo Hong, Yen-Pang Chen, Fang Yu

llms rag benchmarks

arXiv AI

Jul 16

WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency

arXiv:2607. 13099v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success but raise growing concerns about content provenance and misuse, motivating the need for reliable watermarking techniques.

By Z Sun, Q Jiang, S Sheng, L Xiang

llms efficiency benchmarks

arXiv Machine Learning

4d ago

AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks

arXiv:2609.37310v1 Announce Type: cross Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Ye...

By Thibaud Gloaguen, Robin Staab, Martin Vechev

llms agents benchmarks

One email a morning, machine-written

Related stories

OpenStamp: A Watermark for Open-Source Language Models

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

Persistent Watermarking of Text-to-Image Models

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models

WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency

AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks