The paper for it is open. The technique isn't really hiding information in the text itself, but by forcing some of the rolls to follow a specific pattern. LLMs work by estimating the most likely next token, so there's sometimes a list of possible candidates that would all work in the text (e.g. synonyms). At low "temperature", the output is a bit more deterministic and otherwise it's a weighted dice roll of which token/word to pick. An LLM can loop over existing text and figure out if the output matches something it would do, similar to checking chess moves against the best computed move for detecting cheating. But the LLM purposefully creates a pattern of alternating weighted rolls that are highly unlikely to appear in normal text, and that becomes the watermarking.
The upside is that this has very low false positive detection rate, but the downsides are many. It only works on longer pieces of text. The system is fragile, and small edits (or rewrites by a local model) can fool the detection. Only the owner of the model is able to re-run inference at this level, so data must be sent to them for evaluation. And sometimes the token output is basically 100% deterministic because the input asks for the straight answer to a fact, or to recite a quote verbatim. That leaves no room for watermarking at all, unless the model is able to lie.
The upside is that this has very low false positive detection rate, but the downsides are many. It only works on longer pieces of text. The system is fragile, and small edits (or rewrites by a local model) can fool the detection. Only the owner of the model is able to re-run inference at this level, so data must be sent to them for evaluation. And sometimes the token output is basically 100% deterministic because the input asks for the straight answer to a fact, or to recite a quote verbatim. That leaves no room for watermarking at all, unless the model is able to lie.