3 papers
cs.LG2026
ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration
Juntong Wu, Yifei Liu, Junyi Chen +6
Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-latency MoE serving is increasin…
cs.DC2026
HYPIC: Accelerating Hybrid-Attention LLM Serving with Position-Independent Caching
Yifei Liu, Juntong Wu, Yang Liu +4
In retrieval-augmented generation and agentic LLM serving, prompts are assembled from independent segments into long contexts, making the prefill stage dominate per-request cost. T…
cs.AI2026
BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
Juntong Wu, Jialiang Cheng, Qishen Yin +5
Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K…