3 papers
cs.CL2026
PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition
Ziyan Gan, Fangxin Liu, Chenyang Guan +10
Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remai…
cs.DC2026
HyperOffload: Graph-Driven Hierarchical Memory Management for Large Language Models on SuperNode Architectures
Fangxin Liu, Qinghua Zhang, Hanjing Shen +5
The rapid evolution of Large Language Models (LLMs) towards long-context reasoning and sparse architectures has pushed memory requirements far beyond the capacity of individual dev…
cs.RO2020
Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions
Qi Yan, Li Jiang, Solmaz Kia
This paper studies the measurement scheduling problem for a group of N mobile robots moving on a flat surface that are preforming cooperative localization (CL). We consider a scena…