> But it’s just as likely to make an output better. No, for any particular output token the model's true logits are definitionally the 'best' that the model ca

> But it’s just as likely to make an output better. No, for any particular output token the model's true logits are definitionally the 'best' that the model can achieve. This is inherently probabilistic. The model's top-1 guess is not guaranteed to be optimal, but it should be so a proportionate fraction of the time. Same with the top-2, top-3, etc. Watermarking necessarily alters the output distribution away from the model-set distribution, and that alteration is inherently 'worse' in expectation. You can liken this to a weather forecast. If there's a 25% chance of rain, the forecast should say so (or a 'sampled' deterministic forecast should predict rain 25% of the time). If the forecast is 'watermarked' and predicts rain 27% of the time under identical circumstances, it's a worse forecast. That being said, this is a case of hiding a message in a noisy channel. Watermarking only needs to communicate one bit ('yes watermark'), so the effects can be arbitrarily small provided one is willing to tolerate an increase to the text size needed for reliable detection.