13 papers
MUX: Continuous Reasoning via Multiplexed Tokens
Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein +2
Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is computationally bottlenecked: each reasoni…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
Xingyue Huang, Mikhail Galkin, Michael M. Bronstein +1
Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing…
MacroGuide: Topological Guidance for Macrocycle Generation
Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein +3
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. D…
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Jonathan Kouchly, Ben Finkelshtein, Michael Bronstein +1
Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the n…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina PetroviÄ, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…