3 papers
cs.LG2026
Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
Jiajun Zhou, Yadong Li, Xuanze Chen +4
Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static and rigid routing strategies…
cs.CL2025
ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts
Zheyue Tan, Zhiyuan Li, Tao Yuan +13
Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per t…
cs.LG2025
Megrez-Omni Technical Report
Boxun Li, Yadong Li, Zhiyuan Li +12
In this work, we present the Megrez models, comprising a language model (Megrez-3B-Instruct) and a multimodal model (Megrez-3B-Omni). These models are designed to deliver fast infe…