Papers with detection robustness
QuantileMark: A Message-Symmetric Multi-bit Watermark for LLMs (2026.acl-long)
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| Challenge: | a number of large language models (LLMs) require multi-bit watermarking to ensure provenance. |
| Approach: | They propose a multi-bit watermark that embeds messages within a continuous cumulative probability interval. |
| Outcome: | The proposed watermark breaks message symmetry in low-entropy decoding, showing it can be used for verification and quality verification. |
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)
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| Challenge: | Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods . |
| Approach: | They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low. |
| Outcome: | The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods. |
You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)
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| Challenge: | Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary. |
| Approach: | They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution. |
| Outcome: | The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity. |
A Linguistics-Aware LLM Watermarking via Syntactic Predictability (2026.acl-long)
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| Challenge: | a central challenge remains balancing text quality against detection robustness. |
| Approach: | They propose a framework that aligns watermark strength with linguistic degrees of freedom . they use part-of-speech models to weaken the signal in grammatically constrained contexts . |
| Outcome: | The proposed framework outperforms existing methods in linguistic indeterminacy tests on languages . it weakens the watermark strength in grammatically constrained contexts and strengthens it in contexts with greater linguistic flexibility. |