GraphML News (September 2025) - Stanford Graph Learning WS, MoML, RF Diffusion 3

While the community is processing NeurIPS rejects due to “limited physical space” and rushing to the ICLR deadline, it’s about time to plan attending some future events!

🌲 Stanford organizes its annual Graph Learning Workshop on Oct 14th. The main topics are Relational Foundation Models (get ready to hear a lot about it, hehe), Agents (Biomni is quite successful), and fast LLM inference. I attended the event last 3 years and it was quite fun.

🧬 About one week later (Oct 22nd) and on the East Coast, MIT organizes Molecular ML (MoML) conference going full Geometric DL mode — expect news about Boltz and new drug discovery methods, most of the big pharma is in the sponsors.

🧬🧬 The Baker Lab released a pre-print of RFDiffusion 3 (the data pipeline of it, AtomWorks, was pre-printed a bit earlier). Compared to AF3, it has much fewer Pairformer layers (only 2 vs 48) without all the triangular attention complexity, and most of the params and compute went into the diffusion module (and good data pipelines, hehe). RFD3 is substantially faster than previous versions on longer residue structures, and much more accurate than RFaa. Code is not yet there.

🎅 FAIR Chemistry opened an Open Molecules 2025 leaderboard and, to our utter amusement, 4-years old GemNet OC tops the benchmark in several tasks. The grand-dad of ML potentials still rocks if you give it better data and more compute. That’s a good lesson on designing models that can stand a test of time and new data.

Finally, for some weekend reading, check Random graphs as perfect expanders on Quanta Magazine. Obtaining good expanders is a non-trivial task (which will very quickly get you into the group theory), but turns out you should never underestimate good ole ER graphs to be sufficiently ok expanders.