1 citations · 3 across the 3 of their papers we have counts for
4 papers
The Universal Classifier for Graph Learning
Ben Finkelshtein, André Linhares, Petar Veličković +2
While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learning (GML) remains fractured due…
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called cr…
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Florian Grötschla, Luis Müller, Jan Tönshoff +2
Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also…