arXiv:2601.22985v2 Announce Type: replace Abstract: We propose dgMARK, a decoding-guided watermarking method for discrete diffusion language models (dLLMs). Unlike autoregressive models, dLLMs can generate tokens in arbitrary order. While an ideal conditional predictor would be invariant to this order, practical dLLMs exhibit strong sensitivity to the unmasking order, creating a new channel for watermarking. dgMARK steers the unmasking order toward positions whose high-reward candidate tokens satisfy a simple parity constraint induced by a binary hash, without explicitly reweighting the model'
The proliferation of advanced language models necessitates robust methods for provenance and authenticity, driving rapid innovation in watermarking techniques amidst concerns about AI-generated content.
Watermarking for diffusion language models addresses a critical gap in content authentication, potentially mitigating disinformation and intellectual property theft as AI capabilities advance.
The development of decoding-guided watermarking introduces a new paradigm for embedding verifiable signals directly into the generation process of discrete diffusion models, previously challenging due to their non-autoregressive nature.
- · Content creators
- · Intellectual property owners
- · AI ethics and safety researchers
- · Platforms combating misinformation
- · Creators of undetectable AI-generated content
- · Malicious actors spreading disinformation
This method enables easier identification of AI-generated text from discrete diffusion models.
Increased trust in digital content provenance could slow the spread of deepfake text and AI-generated disinformation.
Mass adoption of watermarking could lead to regulatory requirements for verifiable AI content, reshaping the digital information ecosystem.
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