Thinking out loud, instead of watermarking you could just look at each tokens conditional probability of being sampled based on the prior tokens; if the probabilities are high in aggregate it is likely to hang come from low temperature GPT. This assumes that transformer models trained by different companies (on presumably overlapping data) will have different enough predictions in long sequences.