3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
EmergentBridge: Improving Zero-Shot Cross-Modal Transfer in Unified Multimodal Embedding Models
Jincheng Xie, Xingchen Xiao, Runheng Liu +3
Unified multimodal embedding spaces underpin practical applications such as cross-modal retrieval and zero-shot recognition. In many real deployments, however, supervision is avail…
Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
Runheng Liu, Heyan Huang, Xingchen Xiao +1
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has raised concerns about potential…
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…