1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CR2026
CachePrune: Privacy-Aware and Fine-Grained KV Cache Sharing for Efficient LLM Inference
Guanlong Wu, Zhaohan li, Yao Zhang +4
Large Language Models (LLMs) rely on Key-Value (KV) caching to accelerate inference, and many serving systems further share the KV cache across users' requests to reduce redundant…
cs.CL2026★ 1 cited
Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource
Houyi Li, Ka Man Lo, Shijie Xuyang +7
Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense…