Flip, Don't Shuffle: Watermarking LLMs at the Speed of Inference
New LLM watermarking method operates at inference speed via stateless Bernoulli trials — relevant as EU AI Act transparency obligations drive demand for output provenance.
Summary written by editorial AI · Source link below
arXiv:2609.03844v1 Announce Type: new Abstract: We introduce Stateless Bernoulli Watermarking (SBW), a new statistical watermark for Large Language Models that determines green list membership through independent per-token Bernoulli trials. Unlike KGW's vocabulary permutation or SynthID's multi-layer tournament, SBW requires only a single comparison per token against a counter-based random number generator, reducing membership complexity to $O(1)$ and enabling single-kernel execution with zero
Editorial Analysis
As the EU AI Act mandates disclosure of AI-generated content, efficient watermarking that preserves output quality becomes a competitive differentiator for LLM providers.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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