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.
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.
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.
That's not true. Watermarks are messing with the next token generation probabilities based on some random seed. The quality is neccesarily lower, the difference is simply too small to notice, typically.
The article has a pretty decent summary of the watermarking algo though. This reads as a pretty dogmatic statement in comparison.
In your analogy: What if seed 42 specifically causes poor quality behaviour (in some contexts specifically). Normally, these quality differences will be washed out because the seed is random, now it is no longer random, so shouldnt we check into specific behaviour under this specific seed?
Model companies are doing this for themselves anyways, it’s so they don’t feed generated content back into the slopper and collapse the model. From that angle it over time contributes to better model quality.
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.
It’s hard to tell because the writing quality is garbage.
Second paragraph:
> Watermarking is designed for provenance, but SynthID-Text changes the process by which the model generates each next token.
This is a stretch. True, but barely. The LLM is making slightly different choices near the end of the token generation process.
> At the model level, this can change safety behavior, including whether the model refuses a harmful request and whether that refusal holds under prompt injection.
Claim support, if it appears, is pages later.
> At the agent level, the same sampled tokens can determine which tool is called and what arguments are passed to it.
?
> Prompt injection connects these two settings because a weakened refusal becomes more consequential when the model can also act through tools.
Wtf. Non-sequitor. Where does this come from?
> Such a watermarking procedure can therefore affect both what the model says and what an agent does.
Duh? In the literal sense of outputting different tokens.
> We call this behavioral effect sampling drift.
I think they should have used an LLM for writing help, or paid more for the one they used.
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.
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.
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.
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.
That's not true. Watermarks are messing with the next token generation probabilities based on some random seed. The quality is neccesarily lower, the difference is simply too small to notice, typically.
The article has a pretty decent summary of the watermarking algo though. This reads as a pretty dogmatic statement in comparison.
In your analogy: What if seed 42 specifically causes poor quality behaviour (in some contexts specifically). Normally, these quality differences will be washed out because the seed is random, now it is no longer random, so shouldnt we check into specific behaviour under this specific seed?
Model companies are doing this for themselves anyways, it’s so they don’t feed generated content back into the slopper and collapse the model. From that angle it over time contributes to better model quality.
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.
Am I missing something, or did they actually completely misunderstand how this technology works?
It’s hard to tell because the writing quality is garbage.
Second paragraph:
> Watermarking is designed for provenance, but SynthID-Text changes the process by which the model generates each next token.
This is a stretch. True, but barely. The LLM is making slightly different choices near the end of the token generation process.
> At the model level, this can change safety behavior, including whether the model refuses a harmful request and whether that refusal holds under prompt injection.
Claim support, if it appears, is pages later.
> At the agent level, the same sampled tokens can determine which tool is called and what arguments are passed to it.
?
> Prompt injection connects these two settings because a weakened refusal becomes more consequential when the model can also act through tools.
Wtf. Non-sequitor. Where does this come from?
> Such a watermarking procedure can therefore affect both what the model says and what an agent does.
Duh? In the literal sense of outputting different tokens.
> We call this behavioral effect sampling drift.
I think they should have used an LLM for writing help, or paid more for the one they used.
More likely you misunderstood it than them.
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.
watermarking is great tho