Post #885

GraphML News (Feb 8th) - EEML 2025, Weather models, Science agents Yaay, we got a handful of news this week worth writing about. 🇧🇦 The Eastern European Summer School (EEML) 2025 is coming to Sarajevo July 21-26 (right after ICML) and features a stellar group of speakers, tutorial heads, and organizers. Invited talks include Aaron Courville (Mila, UdeM), Aldan Hung (Isomorphic Labs), Diana Borsa (Google DeepMind), Samy Bengio (Apple) with tutorials led by the Oxford and DeepMind crew - which indicates the presence of graph and geometric learning will be quite strong 📈 There is a plenty of time to apply: the deadline is March 31st, it’s worth attending if you have an opportunity. 🌍 ML-based weather prediction models permeate more into everyday use: first, a few weeks ago Silurian released the Earth API to their Generative Forecasting Transformer (1.5B params) capable of short- and long-range predictions. And DeepMind released the WeatherNext bundle of GraphCast and GenCast (featured many times in media) on Google Cloud. Competition drives the progress (looking at you, DeepSeek-R1, hehe), and weather prediction models are gaining the momentum. 🤖 Andrew White (FutureHouse) published an interesting piece AI for science with reasoning models discussing how frontier models with reasoning and agentic capabilities improve scientific workflows (spoiler: by a good margin). Fast-forward to February 2025, and all major LLM providers offer their Deep Research agents who automatically digest enormous amounts of internet to create reports about your problem: Google offered Gemini Deep Research already in Dec 2024 (powered by Gemini 2.0 Flash Thinking model), OpenAI added Deep Research this week (powered by o3), and HuggingFace is building an open source version of that. One more moat is gone which could be both sad for agentic startups and happy for users who can enjoy the ecosystem they prefer. Weekend reading: On the Emergence of Position Bias in Transformers by Xinyi Wu et al. feat Stefanie Jegelka - a graph-based approach to analyzing positional encodings in Transformers, well in line with Round and Round we Go and other recent works on Transformer PEs Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions? by Xiyuan Wang et al. feat Muhan Zhang - the answer is no, but if you use more expressive GNNs, then maybe. A similar finding is in HOG-Diff: Higher-Order Guided Diffusion for Graph Generation by Yiming Huang and Tolga Birdal. GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation by Linhan Luo et al - an approach based on our ULTRA can be very effective in RAG