@inproceedings{wang-etal-2026-hspmark,
title = "{HSPM}ark: Desynchronization-Resilient Multi-Bit {LLM} Watermarking via Semantic Anchoring",
author = "Wang, Jing and
Xiong, Pengyu and
Qian, Kejun and
Yang, Yi and
Fan, Jing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1099/",
pages = "23245--23259",
ISBN = "979-8-89176-462-0",
abstract = "Reliable multi-bit watermarking is essential for tracing the provenance of large language model (LLM) outputs. However, existing high-capacity schemes typically synchronize pseudo-random functions through discrete token $n$-gram matching, which makes them vulnerable to desynchronization under semantics-preserving paraphrasing attacks. To address this issue, we propose HSPMark, a hierarchical semantic projection marking mechanism for context-aware multi-bit watermarking, which shifts watermark synchronization from brittle token histories to continuous semantic anchors. HSPMark extracts stable contextual features using a pre-trained sentence-level encoder and a lightweight Robust Semantic MLP, then projects these features into context-conditioned pseudo-orthogonal bias matrices for multi-bit payload injection. To balance payload capacity and generation quality, HSPMark introduces an entropy-adaptive embedding mechanism that modulates logit bias according to predictive uncertainty. Extensive evaluations demonstrate that HSPMark improves robustness against paraphrase-induced desynchronization and local perturbations while maintaining a competitive trade-off among bit accuracy, payload capacity, and text quality compared with existing high-capacity baselines."
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<abstract>Reliable multi-bit watermarking is essential for tracing the provenance of large language model (LLM) outputs. However, existing high-capacity schemes typically synchronize pseudo-random functions through discrete token n-gram matching, which makes them vulnerable to desynchronization under semantics-preserving paraphrasing attacks. To address this issue, we propose HSPMark, a hierarchical semantic projection marking mechanism for context-aware multi-bit watermarking, which shifts watermark synchronization from brittle token histories to continuous semantic anchors. HSPMark extracts stable contextual features using a pre-trained sentence-level encoder and a lightweight Robust Semantic MLP, then projects these features into context-conditioned pseudo-orthogonal bias matrices for multi-bit payload injection. To balance payload capacity and generation quality, HSPMark introduces an entropy-adaptive embedding mechanism that modulates logit bias according to predictive uncertainty. Extensive evaluations demonstrate that HSPMark improves robustness against paraphrase-induced desynchronization and local perturbations while maintaining a competitive trade-off among bit accuracy, payload capacity, and text quality compared with existing high-capacity baselines.</abstract>
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%0 Conference Proceedings
%T HSPMark: Desynchronization-Resilient Multi-Bit LLM Watermarking via Semantic Anchoring
%A Wang, Jing
%A Xiong, Pengyu
%A Qian, Kejun
%A Yang, Yi
%A Fan, Jing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-hspmark
%X Reliable multi-bit watermarking is essential for tracing the provenance of large language model (LLM) outputs. However, existing high-capacity schemes typically synchronize pseudo-random functions through discrete token n-gram matching, which makes them vulnerable to desynchronization under semantics-preserving paraphrasing attacks. To address this issue, we propose HSPMark, a hierarchical semantic projection marking mechanism for context-aware multi-bit watermarking, which shifts watermark synchronization from brittle token histories to continuous semantic anchors. HSPMark extracts stable contextual features using a pre-trained sentence-level encoder and a lightweight Robust Semantic MLP, then projects these features into context-conditioned pseudo-orthogonal bias matrices for multi-bit payload injection. To balance payload capacity and generation quality, HSPMark introduces an entropy-adaptive embedding mechanism that modulates logit bias according to predictive uncertainty. Extensive evaluations demonstrate that HSPMark improves robustness against paraphrase-induced desynchronization and local perturbations while maintaining a competitive trade-off among bit accuracy, payload capacity, and text quality compared with existing high-capacity baselines.
%U https://aclanthology.org/2026.findings-emnlp.1099/
%P 23245-23259
Markdown (Informal)
[HSPMark: Desynchronization-Resilient Multi-Bit LLM Watermarking via Semantic Anchoring](https://aclanthology.org/2026.findings-emnlp.1099/) (Wang et al., Findings 2026)
ACL