这篇论文解决了一个实际问题：LLM生成文本的水印如果只能全量披露会泄露隐私。HeRo允许你只给特定验证者看部分信息，像权限分级一样，挺巧妙的。
现有LLM水印方法有零位方案（区分文本是否为AI生成）和多比特方案（嵌入元数据），但多比特方案无法选择性披露——验证任何部分必须揭示整个信息，导致隐私泄露。论文提出分层词汇路由（HeRo）框架，递归划分词汇表并将水印信息分布到层次化层中，不同验证者仅能解码其权限对应的载荷。实验表明HeRo在保持文本质量（无偏采样）的同时支持细粒度访问控制，并实现高检测准确率和低延迟。代码已开源在GitHub。
Selective Disclosure Watermarking for Large Language Models
Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs). Existing approaches include zero-bit schemes for distinguishing synthetic text from human writing and multi-bit schemes for embedding metadata. However, current multi-bit watermarking methods do not allow selective disclosure: verifying any part of the watermark requires revealing the entire embedded message. This lack of control leads to unnecessary information exposure and raises privacy concerns. We propose Hierarchical Vocabulary Routing (HeRo), a watermarking framework that enables selective disclosure of embedded metadata. The method recursively partitions the vocabulary and distributes watermark information across hierarchical layers, so that different verifiers can decode only the portions of the payload corresponding to their access level. We show that the proposed scheme preserves the unbiasedness of the underlying sampling process and thus maintains text quality. Experiments demonstrate that our framework supports fine-grained access control while achieving high detection accuracy and low latency. Code is available at https://github.com/xuyangc03/hero-watermark.