But as I explained, the watermark is functionally random PRNG noise overlaid on the token probabilities. It’s not something that can be compensated for because it’s not predictable if you don’t have the seed and PRNG function.
If it's functionally random PRNG, then how does it differ from any other random sampling? If it's biased PRNG, then the LLM can adapt to the bias, and coincidentally might even benefit from this bias.
