3 papers
cs.LG2026
Billion-Scale Graph Foundation Models
Maya Bechler-Speicher, Yoel Gottlieb, Andrey Isakov +5
Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, ext…
cs.CL2026
Improving LLM Final Representations with Inter-Layer Geometry
Tom Ulanovski, Eyal Blyachman, Maya Bechler-Speicher
The standard in LLM-based prediction is to use the final-layer representation as the input to a downstream predictor. However, intermediate layers may encode complementary task-rel…
cs.LG2026
A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
Guy Bar-Shalom, Ami Tavory, Itay Evron +3
Weight-space models learn directly from the parameters of neural networks, enabling tasks such as predicting their accuracy on new datasets. Naive methods -- like applying MLPs to…