WorldCup Sampling for Multi-bit LLM Watermarking

As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution and traceability beyond mere detection. While multi-bit watermarking enables richer provenance encoding, existing approaches typically extend zero-bit schemes through static logit perturbations and hard counting-based decoding, which can degrade text quality and compromise decoding robustness as the payload increases. In this paper, we propose WorldCup, a multi-bit watermarking framework that extends tournament sampling and embeds message bits through hierarchical competition, in which complementary functions guide the selection of candidate tokens. Moreover, WorldCup incorporates entropy-aware modulation to adapt watermark strength to local uncertainty in the model distribution and preserve text quality, while confidence-aware decoding uses agreement across tournament layers to aggregate evidence for robust message recovery. Comprehensive experiments across multiple LLMs and diverse downstream tasks show that WorldCup offers a favorable trade-off among message capacity, detectability, robustness, text quality, and decoding efficiency, consistently outperforming prior baselines.