this post was submitted on 09 Jul 2025
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Those models will almost certainly be essentially the same transformer architecture as any of the llms use; simply because they beat most other architectures in almost any field people have tried them. An llm is, after all, just classifier with an unusually large set of classes (all possible tokens) which gets applied repeatedly
A quick search turns up that alpha fold 3, what they are using for this, is a diffusion architecture, not a transformer. It works more the image generators than the GPT text generators. It isn't really the same as "the LLMs".
I skimmed the paper, and it seems pretty cool. I'm not sure I quite follow the "diffusion model-based architecture" it mentioned, but it sounds interesting
Diffusion models iteratively convert noise across a space into forms and that's what they are trained to do. In contrast to, say, a GPT that basically performs a recursive token prediction in sequence. They're just totally different models, both in structure and mode of operation. Diffusion models are actually pretty incredible imo and I think we're just beginning to scratch the surface of their power. A very fundamental part of most modes of cognition is converting the noise of unstructured multimodal signal data into something with form and intention, so being able to do this with a model, even if only in very very narrow domains right now, is a pretty massive leap forward.