Understanding the Impact of LLM Watermarking on AI Agent Behavior

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• Hacker News
• watermarking is great tho
  by sinan-faizal
• Watermarking sounds like a good idea, but it's not. Token drift from watermarking will degrade the quality of outputs and could allow clever people to circumvent guardrails.

  You should assume all text is AI generated. If you want to "test" someone at school or during an interview, have them write with a pencil and paper.

  by willmadden
• In _1984_ the Big Brother regime has the idea that by controlling language you can influence what is possible to think, and thus becomes a key tool of political repression.

  Political Correctness has a similar idea that by adjusting the terminology we use, we can purge biases and historical implications and speak in a purer way.

  Psychoanalysis has its own idea of repression - where a person struggles to BLOCK our associations between ideas, memories, and words in order to try to stop one thought from being contaminated by another, intolerable thought.

  All of these attempts to control language are fundamentally misguided at best, often have severe unintended consequences, and are genuinely immoral at worse.

  by jamienk
• Am I missing something, or did they actually completely misunderstand how this technology works?
  by Klaus23
• I am unable to understand what happens if the watermarked output goes as input to another agent. Let's say we asked Claude a question and got a watermarked response. If we pick that response and append it to the question we're asking ChatGPT, then will it answer or refuse to do so?

  If that's the case, then it's a brilliant strategy by the labs to cut down cross-AI usage and just stick to one model. But I'm pretty sure this won't be the case.

  by samayashar
• The comments here are terrible. I got a better idea of what’s going on by asking ChatGPT what this paper’s weaknesses are:

  https://chatgpt.com/s/t_6ab7d694885481918083b8cbf0ba9040

  In particular: sometimes they measure “churn”, which doesn’t show whether the results are better or worse on average. They sometimes only test with one random seed. There are multiple-comparison issues. And they’re not testing Anthropic’s algorithm.

  by skybrian
• This article reads like it was written at least partly by AI to me. Specifically it reads like an article written by AI with edits made by a human further prompting the AI.

  > Relevance and irrelevance are excluded because they test whether a call should be made rather than whether the emitted call is correct.

  Relevance and irrelevance are not introduced above this comment. This reads like an LLM-ism (particularly a GPT-ism) editing a document, removing something, and leaving a note about why it was removed, which doesn't really make sense when reading it.

  > Their limited movement under prompt injection should therefore not be interpreted as evidence that watermarking preserves safety behavior more reliably on these models.

  Also a GPT-ism which appears when it draws a counter-conclusion in the text because it feels the need to be honest and a human tells it to remove it because it's not true because of "reason".

  Overall interesting research, however, I think it's great that model output is getting watermarked. I was skeptical of this at first, but Opus 5.5 is so good, it seems like it's a non-issue in practice.

  The reason I think watermarking is great is because it's a really good way of preventing training on it's own output indiscriminately and Ouroboros-ing itself.

  by serbuvlad
• 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.
  by WithinReason