Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络与信息安全学院）
As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution beyond mere detection. While multi-bit watermarking enables richer provenance encoding, existing approaches typically extend zero-bit watermarking schemes by introducing static logit perturbations and counting-based decoding strategies, which can degrade text quality and compromise decoding robustness as the payload increases. In this paper, we propose WorldCup, a multi-bit watermarking framework for LLMs that models the sampling process as a structured communication channel and embeds message bits through a hierarchical competition mechanism guided by complementary signals. Moreover, WorldCup incorporates entropy-aware modulation to preserve generation quality and enables robust message recovery via confidence-aware decoding that accounts for token-level reliability. Comprehensive experiments demonstrate that WorldCup achieves a strong balance across message capacity, detectability, robustness, text quality, and decoding efficiency, consistently outperforming prior baselines. We believe that this work establishes a scalable and principled foundation for future research on multi-bit watermarking in LLMs.
Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
Department of AI, DGIST, Daegu, Republic of Korea（韩国大邱科学技术院人工智能系）；Department of EECS, DGIST, Daegu, Republic of Korea（韩国大邱科学技术院电子工程与计算机科学系）；ICT convergence, University of Ulsan, Ulsan, Republic of Korea（韩国釜山大学信息通信融合学院）
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Pattern Recognition Center, WeChat AI, Tencent Inc.（腾讯公司微信AI模式识别中心）；DeepSeek AI（深度求索人工智能公司）
TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
Economics and Management School, Wuhan University（武汉大学经济管理学院）；School of Economics and Management, Tsinghua University（清华大学经济管理学院）；School of Business Administration, Southwestern University of Finance and Economics（西南财经大学商学院）
Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems
Beijing University of Posts and Telecommunications（北京邮电大学）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）
