# TextSeal2

LLM watermarks must keep output diversity under a fixed key and must not lower draft acceptance under speculative decoding, and the two have been hard to hold together. TextSeal2 merges two keys' Gaussian values into one virtual key and couples it to fresh noise through a Gaussian copula, so the detector reads full signal on every token; under speculative decoding it decides each round's last token with one more exponential race on the leftovers of the draft races. At equal diversity on three models it detects 8 to 22 percentage points more 100-token answers than TextSeal, and under speculative decoding 18 and 22 points more 200-token answers than TextSeal and textGrain, at unchanged acceptance.

LLM watermarks must keep output diversity under a fixed key and must not lower draft acceptance under speculative decoding, and the two have been hard to hold together. TextSeal2 merges two keys' Gaussian values into one virtual key and couples it to fresh noise through a Gaussian copula, so the detector reads full signal on every token; under speculative decoding it decides each round's last token with one more exponential race on the leftovers of the draft races. At equal diversity on three models it detects 8 to 22 percentage points more 100-token answers than TextSeal, and under speculative decoding 18 and 22 points more 200-token answers than TextSeal and textGrain, at unchanged acceptance.