Hugging Face Trending Papers June 23, 2026

CORE-BREW: LLR-Based Soft Decoding for Robust Multi-Bit LLM Watermarking

Read the original on Hugging Face Trending Papers →

Reliable provenance for LLM outputs requires multi-bit watermarks that remain robust under editing while maintaining strict false-positive control. Existing ECC-based LLM watermarks rely largely on hard-decision decoding, discarding token-level reliability information.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

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 Machine Learning

2d ago

Semantic Differentiation for Tackling Challenges in Watermarking Low-Entropy Constrained Generation Outputs

arXiv:2601. 11629v2 Announce Type: replace-cross Abstract: We demonstrate that while the current approaches for language model watermarking are effective for open-ended generation, they are inadequate at watermarking LM outputs for constrained generation tasks with low-entropy output spaces.

By Nghia T. Le, Alan Ritter, Kartik Goyal

llms nlp

arXiv Machine Learning

Aug 12

Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign

arXiv:2502. 02068v3 Announce Type: replace-cross Abstract: This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development.

By Ruisi Zhang, Neusha Javidnia, Nojan Sheybani, Farinaz Koushanfar

llms

arXiv Machine Learning

Jul 8

Multi-Channel Spread-Spectrum Code Watermarking

arXiv:2607. 06009v1 Announce Type: cross Abstract: Attributing code to the large language model that produced it is essential for provenance, licensing, and misuse accountability, yet no deployed watermark meets this need.

By Soohyeon Choi, Debin Gao, Yue Duan

llms rag

arXiv AI

Jun 17

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.

By Bingxue Zhang, Xiaofeng Xu, Feida Zhu

llms rag fine-tuning efficiency

arXiv Machine Learning

Jul 8

Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking

arXiv:2607. 05694v1 Announce Type: cross Abstract: Logit-based watermarking is a widely used mechanism for identifying LLM generated content, yet its effectiveness is governed by a fundamental trade-off between detectability and semantic distortion.

By Xiaopu Wang, Zelin He, Chengyuan Liu, Runze Li

llms

Related stories

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

Semantic Differentiation for Tackling Challenges in Watermarking Low-Entropy Constrained Generation Outputs

Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign

Multi-Channel Spread-Spectrum Code Watermarking

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

Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking