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Text watermarking has no effect on output quality, it just works by changing the explicit source of randomness that is in practice always present in LLM output sampling. See for example https://www.seangoedecke.com/ai-text-watermarking-is-not-a-b....
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> Text watermarking has no effect on output quality

It has an effect, and it's negative. It's hoped that the effect is negligible, and it probably is, but the whole point is that it has an effect.

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Its essentially swapping out the psuedo random number generated with a differently seeded one iirc.

It has an effect on the output, but not the output quality

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It seems fine to me. The model is still solving my problems and writing code that works as well as any other.

Google has been watermarking text with SynthID for a while now and nobody complained about it. Why all the fuss about Claude?

It feels like the real reason behind most complaints is that people want to use AI for writing and not have others find out?

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It reduces diversity, which they don't talk about much. Wrote about it here. https://blog.keyvan.net/p/ai-text-watermarking-and-quality
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It has an effect, 50% of the time it's positive, increasing the quality of the answer, and 50% of the time it's negative.
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These models are already running with a temperature that is >0. This just replaces a random number with a slightly less random number.
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I am pretty sure they did A/B testing to show it didn't. I could gave sworn they even released a quiz were the user has to try and guess which answer is watermarked or not and it was impossible to tell.
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That's not the metric, though.

You either output the best version, or you output something else.

You can't do both.

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That's not the case, because LLMs are non-deterministic.

It only alters outputs when the last layer of the neural network give significant weights to multiple tokens, and it would anyway have picked a random answer.

Instead it picks a non-random one, but non-random in such a way that you can't tell without the private key of the watermarking.

This mostly adds randomness these days for branches in syntax that make no difference, and the model has no reason to believe make a difference. Anything that matters, it is much more confident in the last layer of weights on the token to use.

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I feel as though you are overlooking simple statistics/confidence intervals. It absolutely possible for two different works to be not have a distinguishable difference in quality.
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Agreed, but not if one of them was altered to contain a secret message. That one will have a distinguishable difference in quality. Maybe (almost certainly) negligible, but still there.
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This...isn't true.

Others have already said this, but the watermarking is something like "when the model flips a coin picking between two values, always choose heads". It was already flipping a coin. You're not choosing a less good result, you're just using a deterministic process when it was stochastic before.

This will have some impact on outputs, but unless you have some reason to believe that always picking tails was better than always picking heads (in which case, you should be working at one of these companies in model training!) it won't have any impact on output quality.

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that's fair. but it outputted non-best version in the first place sometimes too. Now it still output non-best version sometimes.
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Why do you claim that?

There is no reason why there has to be a negative effect of text watermarking.

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It literally re-weights the output tokens from what the LLM would otherwise have chosen. It _has_ to. It can't be positive, because then that's not watermarking, it's a better LLM.
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It's a very unintuitive algorithm, and is pretty clever.

I recommend reading up on it: https://www.nature.com/articles/s41586-024-08025-4

But no, it only ever picks tokens that are in the probability distribution of the last layer, and it might have picked anyway.

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Unless you’re at 0 temperature, there is no single token it would have chosen. It’s always picking one of multiple randomly according to a probability distribution.
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Give me an example how would you watermark a single short sentence like "I like turtles"?
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Unless you’re running at temperature 0, there’s not one single token that the model definitely would have chosen each time.
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This is hilarious this keeps being repeated by the true believers ad nauseam.

Also, don't apply EU law to the world. It's a knee jerk reactionary regulation by a bunch of aging ding dongs that can't print their emails.

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You're on Hacker News - I suggest you have technical curiosity and actually understand this very unusual and innovative algorithm, before you claim things about it that aren't true.
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I can't listen to the GoT theme song without hearing "floppy wieners".
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Cut them a break. They are trying to IPO soon.
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when?
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Before OpenAI, before bubble burst, before open-weight Mythos.
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In AI years that's probably next month or two.
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> * Continue tons of hype about how good they are without delivering, going to great lengths to publish how their model "hacked" its way out of a sandbox they misconfigured.

That wasn’t Anthropic. Clearly not a well informed take.

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What do you mean fable is useless?
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(not op) It cannot be used to develop applications. Every application needs to be secure in some way, and any such mention in a review triggers Fable's upsell feature.
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I never actually managed to use fable successfully even once on a pretty standard mvc/microservice app.. It would always find the endpoint permission checks and revert to opus 4.8.

I also had glm 5.3 flash fix an issue that opus 5 could not solve. glm took 4 times as long and a sub-agent tried to cheat (sleep; echo ...), but in the end it actually solved the issue. opus 5 never figured it out.

I think the safeguards might be cooking the anthropic models.

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Agreed. I was trying to get it to review some auth refactoring in my app recently, and it appeared to find some vulnerabilities. as it was aggregating the results it was flagged and restarted the whole process with Opus 4.8 and all of my usage credits were gone.

Anthropic told me to use their `security-review` tool - as this was the exact scenario the tool is for - and it still got flagged.

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Mythos and Fable are the same cost, aren’t they?
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Yes they are.

The OP doesn't appear to know what they are talking about. Fable can absolutely be used to develop applications. It's just that for security stuff I use Opus 5. Which is fine for most use cases.

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Weird. I'm using it to do a bunch of work on something that manages security rules, with a bunch of sample data with spooky scary fixtures all over with "Mimikatz" and "CobaltStrike Beacon" and "Crowdstrike EDR" type stuff everywhere, including work to harden my system, and I've never been downgraded.
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> Nerfed Fable, as many of noted it's useless

I certainly don't take AI advice from HN, but this is amazing.

Useless? Yes, the safeguards are ridiculous and obnoxious, though I can say that 5.1 greatly relaxes them (just doing a hardening of a project parallel with this comment, which 5.0 refused to do...so did Sol and Gemini, fwiw. The Gemini one is a laugh, because 3.1 pretending like it's a dangerous tool is simply ridiculous at this point), however Fable is extraordinarily useful.

It is, far and away, the most powerful programming model, in my experience. Like, crazily so. It absolutely annihilates Opus 4.6, which I mention given the incredibly weird reminiscing people are doing here.

And for that matter it humiliates Opus 5.0 as well. Opus 5 somehow seems like it's neck in neck in the major benchmarks, but there is simply no reality where that is true. Opus stumbles over everything that Fable just blazes through.

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> it humiliates Opus

???

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It is a vastly superior model for complex, real-world coding tasks. I've constantly had Opus 5 hit road blocks where it spins in circles at xhigh, where switching to Fable immediately solves it. I've had Opus create solutions that Fable then points out the gaps and limitations with, and have never seen the opposite happen.

The fantasy that Opus is superior for coding, much less the incredibly weird clutching onto some far obsolete model, is not reality based.

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[flagged]
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That's, uh, a great contribution. Thanks. It's super important that HN learns how this sounds like to you.
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Just doing my part
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While I also agree that Opus 4.6, in some ways, was the last model that truly felt an assistant, all the following ones seem to have inverted the role, even a blind person can see that throwing difficult problems, and complex bugs at this model achieves more than predecessors.

I don't think there's nothing ground breaking, but sure it achieves and finds more, sooner.

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> as many of noted

please rephrase?

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"as many have noted", I suppose.

I'm always baffled at how many people write "of" instead of "have", they don't even sound the same

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The classic one is "should have" or "should've" to "should of" because when spoken, it really does sound similar. I don't know what the fuck people are learning in English classes these days though, or if they even still have them.
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They sound exactly the same to me.

Wiktionary gives <should've> as /ˈʃʊdəv/, unstressed <have> as /(h)əv/ and unstressed <of> as /əv/.

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and yet we still have people saying the rate of change is increasing

my view is we had a leap over the last fe years and it's tapering off.

this is fine, but for the IPOs

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The improvement is compounding just about every way you can look at it. The frontier keeps getting smarter. And at any sub-frontier threshold the cost is dropping dramatically. The amounts of smarts you can fit on hardware is increasing so dramatically that even 6 year old consumer GPUs are increasing in price. The pace of change in LLMs and downstream applications is absolutely ripping compared to 2023 or 2024.
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We had a leap because of the introduction and refinement of agents - the rest has been minor
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I've been using the same agent for 15 months. I think this statement is laughably wrong.
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