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cs.CL2026
Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
Jincheng Xie, Runheng Liu, Heyan Huang +4
Sparse Mixture-of-Experts (MoE) models have become an important approach for scaling Large Language Models (LLMs), but their inference efficiency depends strongly on expert activat…
cs.CL2026
AdaPLD: Adaptive Retrieval and Reuse for Efficient Model-Free Speculative Decoding
Runheng Liu, Jincheng Xie, Wen Hu +2
Speculative decoding accelerates generation by verifying multiple drafted tokens in a single target-model forward pass, reducing sequential decoding iterations. Model-free variants…
cs.CL2026
MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
Xingchen Xiao, Heyan Huang, Runheng Liu +1
Large language models (LLMs) are widely used in retrieval-augmented generation (RAG) to incorporate external knowledge at inference time. However, when retrieved contexts are noisy…