5 papers · 1 filter
Scaling Embeddings Outperforms Scaling Experts in Language Models
Hong Liu, Jiaqi Zhang, Chao Wang +13
While Mixture-of-Experts (MoE) architectures have become the standard for sparsity scaling in large language models, they increasingly face diminishing returns and system-level bot…
Unveiling Super Experts in Mixture-of-Experts Large Language Models
Zunhai Su, Qingyuan Li, Hao Zhang +6
In this study, we report, for the first time, the discovery and systematic investigation of a distinct subset of experts that play a pivotal role in the MoE LLMs' forward inference…
Efficient Context Scaling with LongCat ZigZag Attention
Chen Zhang, Yang Bai, Jiahuan Li +19
We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited c…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
Yulei Qian, Fengcun Li, Xiangyang Ji +4
The Mixture-of-Experts (MoE) model has emerged as a prominent architecture in the field of Large Language Models (LLMs), providing a better balance between model performance and co…