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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.CL2025
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
Yuxuan Zhou, Xien Liu, Chenwei Yan +8
Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored.…
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…