2 papers
cs.LG2026
N-vium: Mixture-of-Exits Transformer for Accelerated Exact Generation
Aleksander Lorenc, Frédéric Berdoz, Joël Mathys +1
Improving the inference efficiency of autoregressive transformers typically means reducing FLOPs per token, usually through approximations that degrade model quality. We introduce…
cs.LG2026
From Message-Passing to Linearized Graph Sequence Models
Joël Mathys, Basil Rohner, Saku Peltonen +1
Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of modern deep learning architecture…