Comment by kamranjon
14 hours ago
It’s so funny to me that Anthropic can make claims like this one with zero evidence provided.
DeepSeek and others like Minimax are publishing deep research on Multi-Head Latent Attention and Mixture of Experts, Multi-Token Prediction, novel Sparse Attention approaches, I mean they trained long context models on a fraction of the resources and gave everyone the recipe.
Chinese labs might not have the funding of labs like Anthropic, but at least they provide the receipts.
There's reproducible evidence of Kimi K3 spontaneously identifying itself as Claude https://x.com/denisewu/status/2077984660211269870
This behavior is exactly what you'd expect from a model distilled from Claude.
Someone even took the time to analyze Kimi's ambiguous identity, in great detail: https://github.com/rgreenblatt/which_claude_is_k3/blob/main/...
And there's an entire Reddit thread discussing this https://www.reddit.com/r/LocalLLaMA/comments/1m2w5ge/did_kim...
That doesn’t prove Anthropic’s specific 3.4m-session allegation, but calling it “zero evidence” is no longer credible.
Kimi K2.5 was worse in a hilarious way, it identified itself as Claude and referenced Anthropic's Constitutional AI as some of its guiding principles https://huggingface.co/moonshotai/Kimi-K2.5/discussions/38
> This behavior is exactly what you'd expect from a model distilled from Claude.
This is not at all what I would expect because it's trivial to change the training data to replace Claude with Kimi. In fact I'd argue it's almost certainly not saying that due to distillation.
I encourage you to review the links before committing to a position. The writeup on K3's anomalous trans-model identity is very comprehensive.
K3 reproduces Claude's internal model identifier when prompted, something which the real Claude models themselves do not emit. This is highly suggestive that K3 was trained on Claude metadata (API logs, tagged synthetic data), rather than Claude's chat outputs.
And it's well documented that Chinese labs are buying large amounts of raw Claude metadata https://www.chinatalk.media/p/how-to-buy-cheap-claude-tokens...
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>This is not at all what I would expect because it's trivial to change the training data to replace Claude with Kimi.
Wait what? The reason you wouldn't expect it is because if it was distilled, it would be easy to get rid of self identification? Is that any less true of a non distilled model? I suppose there's lots of ways to interpret it, but the idea that self-identifying as Claude is affirmative evidence that it's not distilled seems to get the weight of the inference exactly backwards.
By evidence I mean logs, I mean IP addresses, I mean timestamps. They claim millions of requests, let’s see literally any of them?
I don’t consider a tweet by Denise Wu, who works at Anthropic, to be reproducible evidence.
I don’t consider “Caveat: fully AI-generated research.” To be someone taking time to analyze anything in great detail.
Because two AI models produce vaguely similar front-end styles when generating similar prompts I also do not consider to be of much value?
I think this is what I mean when I say the U.S. has its head in the sand. The Chinese labs are releasing ~60 page research reports with citations and analyses and evidence and Anthropic is throwing up defensive blog posts with zilch. I’ve seen more detail in a tech blog from Uber than anything I’ve seen from Anthropic.
You've backtracked significantly here.
"Zero evidence" as you claimed earlier isn't accurate. You've moved the goalposts from "evidence" to "raw internal logs I can independently audit," which is a different and very high standard. Sure Anthropic didn't publish logs, IP addresses, timestamps, or account IDs of the accounts involved. But that's true of any cybersecurity breach/abuse disclosure ever made. Companies are furtive to reveal how they detect fraud, because doing so exposes the signals used to detect bad actors, and makes future abuse easier. Not revealing the "evidence" you're asking for is industry standard practice. You're complaining that Anthropic is following industry standard practice, and conveniently defining the "evidence" you need as something Anthropic is never going to publish.
> I don’t consider a tweet by Denise Wu, who works at Anthropic, to be reproducible evidence.
Is the issue here that she works at Anthropic? Because Denise Wu doesn't work there.
> I don’t consider “Caveat: fully AI-generated research” to be someone taking time to analyze anything in great detail.
The experiments were run by Ryan Greenblatt, who is a real AI safety researcher (at Redwood Research).
The identity experiments and Greenblatt analysis are trivially reproducible. The methodology, code, and metrics are all there in the Github repository. You can ask your preferred AI to independently replicate these results, and it will give you a result within an hour.
You’ve also reduced the evidence to “two models producing vaguely similar front-end styles,” which is not what either analysis shows.
From the analysis, Kimi K3 identifies itself as Claude 15% of the time. How do you explain that? Qwen and GPT identify themselves as Claude 0% of the time.
If a long document is too much analysis for you, someone else made a simple chart which measures the KL divergence between Kimi K3 and other major models. They found K3 is unusually similar to Fable 5 & Opus models. That is, Kimi K3 has an very similar style and phrasing to that of Anthropic models. That behavior is expected from a model distilled from Claude.
https://typebulb.com/u/lab/you-re-relatively-right/full
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