Then bad news: LLMs already use randomness in a fundamental way. Each time they go to generate a token, they first generate a probability distribution of possible tokens. Then they pick one randomly according to this distribution. The technique described can be thought of as making the random number generator pseudo random. The output it generates is one of the possible outputs it would have generated before, just now it's deterministic and will generate the same thing every time.

On the other hand, LLMs are forced into picking some likely-ish word, and then have to build the rest of their response to retcon that choice into making sense.

Even good human writers would probably struggle with this constraint. It would be like someone interrupting your writing to tell you the next word MUST be such-and-such, and then you have to try and make it work as best you can first try, without going back to edit. The result would probably be a little clunky. (Maybe it’s impressive LLMs write as well as they do.)

You're mixing up two claims here, and only one of these is kind of true. Yes LLMs do internally plan ahead in a way that is emergent rather than strictly part of their architecture, so that part of your claim is true. The way you word it by saying they are "coalescing the probabilities of a range of tokens at a time" is poetic sounding jibberish though. What's happening is that the probability of the immediate next token implicitly carries with it the probabilities of the tokens that follow it. It's not a stream of probabilities for a stream of tokens that get computed. It's one distribution for one single token, computed from a hidden state that already encodes where the text is likely headed.

Your claim that if an LLM does happen to pick a token "th" instead of "tw", then the LLM isn't stuck with that decision is entirely false for autoregressive LLMs which is what all of the frontier models are. Whatever an LLM picks as its output token is final, it has no ability to undo that token selection and it must continue on the basis of that choice. It can't go back on that decision and revise the output.

If you're interested in this, Anthropic has a summary of a very technical paper on this topic that mostly deals with this issue with respect to poetry:

https://www.anthropic.com/research/natural-language-autoenco...

Mathematically, a long chain of conditional probabilities is equivalent to a single probability over the whole range. But computationally, for that to work out, the computation for the first probability needs to somehow consider all the downstream probabilities depending on it, which obviously isn't how autoregressive language models work. They can pack in as much downstream computation as their neural architecture allows for, which is quite a lot.

Suppose in some context you have three equally plausible conpletions after "Be": "tween a rock and a hard place", "twixed he stood there" and "lieve he can fly". To model this probability distribution of the whole sentence, the next token "tw" needs to appear at 2/3 probability and "lie" at 1/3. After "tw" would be a 1/2 chance of "ix" and a 1/2 chance of "een"; after "lie" would be a 100% chance of "ve " and in any case the rest of the sentence after that would be 100%.

The model needs to somehow "think ahead" to know those are the possible completions. For example if "lieve he can swim like a dolphin" was another equally plausible completion, that first token would need to be 50/50 instead of 67/33. So the computation of the first token somehow needs to encode the fact that the guy thinks he can fly but not swim, even though it doesn't become relevant in the output until several tokens later.

In practice this probably happens to some degree but definitely doesn't happen perfectly. To perfectly model the first token's probability distribution, it would have to include knowledge of the entire distribution of all possible outputs, which is just not happening. So it approximates. Surprisingly, the approximation is good enough to produce language.

You can see this breaking down in the seahorse emoji incident from last year. When you ask the model if there's a seahorse emoji, it first completes "Yes," as if a few tokens later it's about to produce a seahorse emoji. But when it actually gets to the token that would produce a seahorse emoji, it can't because there isn't one. But it's already outputted "Yes, the seahorse emoji is" and can't just go back and change that to "No, there's no seahorse emoji." Some models would try a few times and then say there isn't one or a system error seems to be making them unable to produce one, other models (including then-current ChatGPT) would loop forever with ensuing hilarity.

Presumably you could use the same reasoning trace, run multiple generations, and get different outputs (if the temperature is >0).

But now I’m interested in playing more with Cowork or Claude Code/Codex for prose writing to see if the set of tools there affects outputs at all. I guess you might need a more custom “writing” harness.

Reasoning tokens with tool calling tell the model to loop on a one phase of a question and call a tool to indications completion when done.

Related, but not the same thing.

Well, I suppose it's nearly the opposite of that experience, upon further review. But for some reason, that's where my head jumped.

You be a human who's brain shifted into LLM mode (chainneling Markov?).

Or perhaps you're an LLM impersonating humanity.

I often wonder how much LLMs are just mirroring our own brain's patterns.

Here’s an example: I had asked Claude for some music recommendations in a certain style. Part of its output was:

—

*Long journey tracks*

Clinic — “The Return of Evil Bill”

Guided by Voices — not really, wrong band

Silver Apples — “Oscillations”. Proto-everything, deeply repetitive, hypnotic.

—

So at some point there, the next token produced was “Guided” or “Guide” or whatever, and then because it can’t go back, it had to correct itself after the fact.

Reasoning/CoT have helped a lot, but I feel like small versions of this still happen all the time.

Human writing is like 90% editing.

But what about the general idea that they can watermark results to tell where they came from. The next step is tracking down which user got a result. I hate both of these things. Must everything we do be tracked? Next altering wikipedia results so they can tell who looked at the page or something?

I'd like "the best answer" from an llm and don't want to be tracked, but this isn't for me, it is for them. I understand llm results are already using a varying statistical input so they aren't always the same. But I really hate watermarking and likely tracking too.

(1) The behavior that is approximately what you describe is not "fundamental" (though it may not be something you can disable on some hosted providers), it is an option that is not fundamental (and with runtimes where you have full control can be either disabled or tuned in a large number of manners), and

(2) The actual behavior that is approximately what you describe already usually involves use of PRNG (with a user or harness supplied seed), not a true RNG; the change to do watermarking isn't going from RNG to PRNG, it involves adding an additional set of constraints on token generation on top of the existing ones, which inherently compromises quality.

Related: if you don't have a limit on sampling (top-K or top-P), eventually you'll hit one of the really unlikely tokens by chance and then the model will switch to Japanese because the most likely completion after a random Japanese character in the middle of an English sentence is more Japanese writing, not a reversal back to English.

I don’t see how this follows? Tokens are chosen randomly. If you choose tokens with a different RNG in the same distribution, you’re still getting equally good or bad tokens.

In fact we know it's not that good because we can often tell Claude's writing apart from human writing.

Could it have been equal or better with slight variations in wording?

The slipper slop argument is too lazy to address directly. Argue A is bad because A, not because A might become B and you’ve got good arguments against B.

I'm skeptical that anybody generating LLM text is really all that concerned about optimal word choice. Or even particularly good prose. But let's pretend that person exists.

If that person tried, say, an open model and that same model with watermarking applied, I'd be eager to hear their thoughts on the prose quality. Especially if they built an experiment harness and rated a few hundred blinded examples and found a measurable difference.

But getting this upset in advance of any demonstrated problem? It really seems to me like the point isn't the point

Cognitive surrender.

How does that follow? AI-generated text is already not a perfect emulation of human writing. There's lots of room to affect it laterally without changing the level of quality.

As I understand it, LLMs with temperature >0 can select from many possible outputs. All they're doing is limiting the possible outputs to ones that contain this pattern. I don't see any reason why the quality of that subset should be lower than average. The very best outputs will likely be eliminated, but so will the very worst.

The "problem" is that seeing the watermark doesn't mean that the person claiming to be the author didn't make extensive changes to the output of the LLM, or that the LLM wasn't simply the final editor of something that the author had put a lot of work into.

> Cognitive surrender.

I don't know what this means. It's just drama. Don't let the LLM write for you and this is not a worry. I'm not worried about the poetry of LLM output being subtly adulterated.

On deeper tech stuff, like this utterly nonsensical misunderstanding of watermarks… yeah, classic case of a guy who is smart, and has lost the ability to realize when they’re not knowledgeable in a domain.

Does the author think he is currently getting T=0 output from Claude? Is he under the impression that T=0 produces the "best" writing?

This entire article just seems so detached from the basics of how LLMs work.

No and no. I am not sure I agree with his point but I know he is not ill-informed on either of these points, because I mentioned them to him a couple of days ago.

Well, akshwally...

> Interoperability. Providers must implement an interoperability solution for watermark detection such as a standardized API access method, a publicly readable signpost mechanism embedded in content, or participation in a consortium detection solution by February 2, 2027

I think that was intended, yes.

It's still possible to use Claude to proofread - highlight grammatical, flow, structure, logic errors and make simple suggestions for you to pick and choose or adapt as you wish. No watermarking will flag your text. No flaw accusations of LLM authorship will haunt you. All will be fine.

But if you want an LLM to rewrite your text, that's (a) not proofreading, and (b) should be flagged as LLM generated ... because it is.

And if you copy-paste the answers from LLM, I think it's only fair the end result gets flagged. You're not writing it yourself.

I wonder if it would even get flagged in that case, because wouldn't the probability distribution of a token when the LLM is suggesting an edit to your writing be different than the distribution of that token once it is in the context of the text it's editing?

They will need to dodge around the EU requirements but it will probably just come down to an alternative method to watermark or a contractual assurance you won't mis-represent the source of the text.

A point of confusion for me, however: is every watermark unique? Is every algorithm for watermarking going to vary amongst models and amongst model versions? Will each model publisher keep this watermarking as a trade secret, that they alone can detect? If so, this can't scale! How do you detect "JoeBob 4.3 LLM" output? By querying every single model's watermark-detector? And if they all work by re-running the model and using tokens anew? That is extraordinarily wasteful.

If a watermark is not self-evident, or universally detectable, then it is no good. Take, for example, US currency. The security measures are published and well known. Any count-out room in retail has a big poster indicating how you can detect authentic US bills. Nobody has to accept non-US currency in the US, and so the only authenticity you need to worry about is your US bills alone. LLM watermarking has none of this in common. Currently sounding like a shitshow, if you ask me.

Wouldn’t having that be enough to eventually reverse engineer the key?

Removal may come down to changing every third token to a different one.

Working backwards: if it is possible to confirm 100% confidence that a chunk of text is LLM output, then it is "PD until proven otherwise". How can a human reliably assert human authorship of their source text? When all watermark tests fail? Is that proof of humanity now?

If a human proves human authorship, and LLM watermarking tests positive, then is that going to be considered a "derivative work" or not? What if there is an applicable license for the source work, such as "CC-BY-ND" that prohibits derivative works?

This has not been court-tested, and I expect that it will need testing at that level before we can have any assurances.

As for it being "uncopyrightable" if it were the output of an LLM, I think computer people are making a very aspie interpretation of a single decision. I think it's more that the LLM (and thereby its owners) cannot itself hold a copyright on its output, that output has to be touched by a person before it is copyrightable. A particular view from the top of a mountain can't be copyrighted, for example, but a photograph of that view can be.

I'm not sure it at all precludes a "robosigning"* sort of situation, where machines generate output, hired temps sign and claim that output, and immediately sign it over to the people who hired them (as a work-for-hire.) Copyright is stupid, artificial law, not logical.

-----

* https://www.mortgageauditsonline.com/what-are-robo-signers/

For the watermark to be detectable, the text needs to be like 75% AI generated.

If you have an LLM “touch” one section of the article, it’s not gonna be detectable.

Then why are you using an LLM to write? They're not capable of understanding such nuance. They do pick randomly between two synonymous phrases, they do not use some super smart algorithm to pick the one that sounds the best.

This excuse doesn't hold any water at all - Occam's razor says the author is just super annoyed that his AI writing will be identifiable as AI writing.

I honestly can't stand the way Claude writes. This watermark change just makes it scarier.

So, you don't own the generated text, and can't use it freely then. What if I copy paste a section, or rewrite a section of text to my liking? What if I rewrite some lines of code that contains the mark?

Security theater, and vague enough to be used as a weapon against who the government wishes.

I hope it's left off for non-EU customers.

The "watermark" can be trivially defeated, but may be enough to satisfy the letter of the law, and like many people here, I would argue that if you are letting Claude write for you, you've already accepted getting the literary equivalent of turd soup, so the harm is — or at least could be — fairly minuscule.

[1]: https://digital-strategy.ec.europa.eu/en/policies/code-pract...

(FWIW I have a more favorable view than most people seem to of the EU's efforts to at least try tackle problems like this — but predictably, the bureaucratic "solutions" they come up with don't work, but do make things objectively worse)

Who could have seen this coming???

At the point that you're generating entire volumes of text from Claude you're not really trying to be a sophisticated writer. I don't see how it's going to hurt for it to choose random related words.

The author has expressed a preference. Assume that there is a sequence of tokens, such that it is considered the absolute best by the author. This particular method of watermarking makes it less likely to generate that sequence, by definition.

I feel their argument would have been clearer and stronger if they had spent more time exploring the alternatives, and whether these alternatives would be just as effective. It is trivially easy to remove invisible tokens.

Like it or not, there is a public good to being able to identify AI generated content, and a small degredation in quality is tolerable in my opinion.

I don't think anybody has to worry about this issue though. Manual writing, coding, and proof reading continues to be an option. Where AI output is nothing to be ashamed of, the tools are available. For everyone else, there will be LLM providers that ignore EU law.

You can't assume that because if that was the case he'd already know what sentence to write, because that's what that means.

The notion of a best sentence requires a final cause, an end to write to. By their very nature that's not how LLMs work, so you can't 'degrade' them on that front. They can't lose a property they didn't have.

Try running an llm like qwen 3.8 27B in Q8 locally with an intentionally very low temperature setting, it will write like a caveman crossed with a robot. You may find that an extremely literal output does not look pleasant to read for humans.

Either they false positve on pretty much everything ever written, or the chances of catching a true positive is so low as to be useless.

Basically Cinavia for text, and that often falls over and is easy to remove even when there is megabytes of data streaming over a long period of time rather than 2 or 3 bits per wall of text, let alone what most people use claude for, when there is a strict dictionary and other tight output constraints.

But it feels to me like you would need a hell of a lot of text to bury even a simple account ID. The nudges they are talking about are of the order of a handful of bits over several hundred words, I think?

I struggle to see how this could possibly be useful unless there's some sort of psy-op going on to trick people into uncritically accepting anything lacking a watermark as not being AI-generated.

Reminds me of printer tracking dots.

https://en.wikipedia.org/wiki/Printer_tracking_dots

LLMs have never been the place I've thought to expect any commitment to the craft of writing, to be fair.

And what I mean by that is that companies that are at the top tend to make anti customer decisions because they have lost the concept that pleasing customers matters as priority one.

Total variation distance has been measured to decrease as you scale a model, and that is the primary mechanism "watermarking" as discussed in the Anthropic announcement relies on. It becomes more difficult to reliably detect text as a fixed sample count without tweaking the distribution further. Either way, it's a minor problem that will be addressed over time, compared to the issue of who can detect this without guessing or developing their own sets: providers not releasing a way to detect any such watermarks without going through them makes this entire approach hostile to the public. The EU regulation on this subject is interesting, although again most certainly not the primary driver for these practices:

"1.1.2: Signatories will ensure that AI-generated or manipulated content is marked with an imperceptible watermark, with the exception of very short text. For free-form text longer than 200 tokens, watermarking still needs to be applied, even though it may have lower reliability compared to that of watermarking very long text"

A proper, effective and useful law would have required providers to regularly release datasets to run your own verification on any text released within a fixed interval of time, presumably once out of rotation. Instead, it only talks about exposing an user interface going through their own services:

"Signatories will ensure access to their detection solution through a user interface appropriate for the audience of end-users that may eventually be exposed to the content generated or manipulated by their AI system. [...] Any restriction to the access will be limited in time until more reliable and robust detection mechanisms have emerged and have been adopted as the state of the art for detection mechanisms for the watermarking of free-form text evolves."

Most interestingly, in line with the EU's mass-surveillance program, an alternative solution to watermarking where it may not be sufficient is also suggested, although only optional for now:

"Where appropriate and taking into account potential trade-offs related to privacy and security, as well as scalability challenges and costs, Signatories may implement as an optional supplementary measure fingerprinting or logging solutions for AI-generated or manipulated content which allow for checking whether content has been generated or manipulated by their AI system. For example, direct logging may be appropriate for text content, whereas fingerprinting approaches may be preferable for audio and visual content."

“Absurdly and insultingly”? Come on…

> One of my fundamental problem with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter.

Doesn’t make sense at all in light of the actual approach, they’re just choosing a different RNG. It’s not like they’re corrupting it by flipping words.

Should add I don’t support the watermarking and requiring it is idiotic.

Perhaps it would be useful to publish examples of samples with/without watermark. I'd suspect that the variability from simply sampling repeated times would dwarf any semantic differences you'd detect with the watermark.

Variable name perversion incoming...

Yeah, John. We're all OK with that.

It already fails. It randomly picks between close candidates. To help fool people into believing in intelligence claim, I guess.

Nanny state nonsense indeed.

As a non American/non European resident all I see from the Europeans are 0 contributions to software progress at any large scale while they surely do a lot of crying and huffing and puffing and demanding. Lots of complaining and rule making but not a lot of creating is a bad look.

FITFY.

I have no sympathy for writers whining about what the AI is doing to 'their' writing. It's only your writing when you write it. There's any easy way to avoid this: don't fucking use it. Use you own brain.

> The difference between watermarked and un-watermarked text will not be distinguishable to readers

https://www.anthropic.com/news/claude-text-watermark

Which is to say, it does not actually meet the EU AI act requirements which require transparency to humans. Not to mention that if the detection requires access to the base models, it makes anthropic the only entity who gets the say on if a piece of text comes out of Claude. Anthropic is both the player and the referee here.

If there is one takeaway you should have from this fiasco it is that you should be wary of using tools that doesn't serve your needs and your needs only.

How would that work? Claude appending " written by AI" to each of its messages? That would both be impractical and useless.

If you get caught uploading watermarked media without the clear label, you're in big trouble, mister.