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Understanding the Impact of LLM Watermarking on AI Agent Behavior

58 points · 72 comments · nisosguy

  1. WithinReason · · focus · HN ↗
    This is getting tiring. Watermarking has no effect on model output quality when implemented correctly. It's somewhat like swapping a random RNG seed to the seed 42, and detecting what the seed was from a random sequence. The sequence generated from the seed 42 is just as random as any other seed. There couldn't be a quality difference. And yes, the output from an LLM is a conditional random sequence of tokens from a distribution determined by a model.
    1. docjay · · focus · HN ↗
      You’re using the subjective definition of “quality”, as in the shade of blue it chooses for “Build a website”, or the character names for “Tell me a story.” In those cases it’s likely still subjectively “high quality”, depending on who you ask.

      What the article is discussing, and what many people are concerned about, is something that you might be missing in your understanding: they’re not actually random. In fact, they would be entirely useless for real work if every token was randomly selected based on all possible outputs. It’s not, even at temperature 1.0. It’s based on the training corpus and once you have your tool names, syntax, and prompting style aligned with the training data then they become incredibly deterministic in the areas that matter, such as tool calling and parameters. I build my toolset by testing thousands of names, syntax, return format, and other aspects until I find a convention that produces the exact correct call, 100% the exact same every time, regardless of context length. Those decisions are per model and what works with Opus 4.7 won’t necessarily work on 4.8, and neither version will work with a local model or GPT.

      That’s only possible because the massive training corpus is the guiding principle behind the choices. Providing a file reading function called “Read_The_File” will fail, either on the first call or somewhere down the line, because that name is not associated with the concept. Your instructions are trying to override 500 trillion tokens from training and it will cause perplexity to manifest as wrong tool calls, wrong syntax, “oops deleted prod”, “Claude lost the plot again”, “WTF?!”, and probably nearly every frustration you’ve encountered and determined to be “they nerfed Claude” or “it’s a dumbass.”

      For those that are aware of it, that knowledge lets people tweak and tune the prompts/tools accordingly.

      You may not put that effort into your system, perhaps because you’re unaware of it, don’t use it in a way that requires it, or you’ve just taken the failures caused by perplexity as something that’s inherent in the framework, but for people that build precision infrastructure around them it’s potentially devastating news. Watermarking, which is based on whatever tokens, threshold, cutoff, and triggers some guy at a desk decided, will necessarily alter that entire system.

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