Graph Foundation Models

Curved Space, Straighter Retrieval: Why Graph RAG Needs Geometry Retrieval looks simple until the wrong thing keeps showing up. A company builds a graph model over products, papers, suppliers, users, or transactions. The model performs reasonably well inside familiar territory. Then the data shifts. New products appear. A new research domain enters the citation graph. A social platform changes user behavior. The model’s internal knowledge, frozen inside parameters, starts behaving like yesterday’s org chart: technically structured, operationally stale. ...

Benchmarks Are Where Models Stop Being Inspirational Benchmarks are not glamorous. They are where models go after the demo video, after the conference slide, and after the sentence “this generalizes beautifully” has done its little dance in front of investors. Graph learning badly needs that room. For years, graph machine learning has been evaluated on comfortable territory: molecular graphs, citation networks, small academic datasets, and carefully packaged tasks that are useful but narrow. That helped the field grow. It also created a quiet distortion. A model could look impressive while never having to deal with a social network that changes over time, a circuit whose tiny structural error destroys correctness, a SAT instance where solver choice matters, or a weather graph where the planet is inconveniently spherical. ...