There is a whole moral judgement to be made here...lets hope Ilya wont get too pissed off if somebody leaks the work of his new initiative...information wants to be free and all that...

Also would love to know if the same Legal team advised on Gemini...

And to make anyone who threatened to expose them “commit suicide”

He's a massive massive thief that people who have stolen far less from a convenience store have gone to prison for. The man is a villain.

DeepSeek research:

- V3 https://arxiv.org/abs/2412.19437

- V2 https://arxiv.org/abs/2405.04434

- R1 https://arxiv.org/abs/2501.12948 (RL applied to ML models was well-known beforehand, but they show it in the open, at scale, on big models)

Then, there's the incentive analysis. If you can see that these models empirically get better with scale, why would you swap the main architecture? Those events will be pretty rare. I'm not saying there's noone cooking a new architecture, just that it is a pretty rare event. And it would have to come from some researchers that would be happy to not publish their findings, which is not really what a sizable portion of elite researchers (obviously not all) are incentivized to do.

Of course, it's a bit of a verbal compression to claim simply 'scaled up'. They are recognisable scaled up transformers, but most new models come with a few tricks, but we're at the point where those usually are not an architectural rewrite and added to solve an explicit problem, like hallucination, not for big new capability gains.

We know for sure the architecture of the open weights models since llama.cpp understands the architecture it needs to build to plug the weights into to run them. It's always possible that the latest closed model is doing something architecturally different than the open weights ones we know about, but judging by how close the large open weight models such as DeepSeek are to SOTA performance, this seems unlikely. When OpenAI first came out with their near-mythical "Strawberry" (aka "o1") thinking model there was all sorts of speculation that they had made some sort of architectural breakthough, but then DeepSeek replicated the capability and published how they did it, proving that it was just better training, not any architectural change.

There have been minor changes to the architecture over the years, but these are basically all efficiency tweaks such as various types of attention (some pioneered in the open by DeepSeek) that better scale to large context lengths, and the confusingly named "mixture of experts" architecture, but what's more notable really is how little the architecture has changed. The capability gains have been coming from better training and better data.

There are thousands of people working in top level labs. Somebody would leak it

No they are clearly not just scaled up versions of gpt 2; there are different LLM architectures like mixture of experts etc that appeared relatively recently. I am not an expert though, far from it.