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...

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, 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.

https://typebulb.com/u/lab/you-re-relatively-right/full

You're conflating pre-training data volume with post-training data volume.

Nobody is suggesting Moonshot used 15 trillion tokens of Claude data to pre-train a base model from scratch. That would be impossible and nonsensical.

This is entirely about distillation, which happens during post-training (alignment and SFT). Here, datasets are measured in millions or billions of tokens, not trillions. 50 billion Claude tokens is far, far than enough to copy Claude's reasoning logic, writing style, and tool-use ability to the pre-trained base model.

> However, this is not distillation

I don't understand how you're so caught up on the term "distillation". Distillation is using a larger model's outputs to train a (weaker) student model. Which is exactly what's happening. It's a standardized term that has been in use for a decade.