2 papers
cs.CV2026
MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
Daniel Perkins, John Squires, Janou Milligan +2
Modern image classification models excel when trained on single task-specific datasets but often struggle to generalize across domains and difficulty levels. We propose ARMDIL, an…
cond-mat.mtrl-sci2026
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8
We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ milli…