2 papers
cs.LG2026
SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
MikoÅaj Zasada, Åukasz Struski, Jacek Tabor +1
Sparse Mixture-of-Experts (MoE) architectures enable scaling LLM parameters under a fixed inference budget by activating only a small subset of experts via top- routing. While t…
cs.GT2026
Learning Real-Life Approval Elections
Piotr Faliszewski, Åukasz Janeczko, Andrzej Kaczmarczyk +3
We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This mo…