1 citations · 1 across the 5 of their papers we have counts for
5 papers
TokenMem: Faithful Knowledge Injection for Frozen LLMs
Chengzhang Yu, Chenyang Zheng, Zening Lu +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved information contradicts par…
ExplicitLM: Decoupling Knowledge from Parameters via Explicit Memory Banks
Chengzhang Yu, Zening Lu, Chenyang Zheng +3
Large language models suffer from knowledge staleness and lack of interpretability due to implicit knowledge storage across entangled network parameters, preventing targeted update…
From Passive to Proactive: A Hierarchical Multi-Agent Framework for Automated Medical Pre-Consultation
ChengZhang Yu, YingRu He, Hongyan Cheng +5
The post-pandemic surge in healthcare demand, coupled with critical nursing shortages, has placed unprecedented pressure on medical triage systems, necessitating innovative AI-driv…
Collaborative Medical Triage under Uncertainty: A Multi-Agent Dynamic Matching Approach
Hongyan Cheng, Chengzhang Yu, Yanshu Shi +3
The post-pandemic surge in healthcare demand, coupled with critical nursing shortages, has placed unprecedented pressure on medical triage systems, necessitating innovative AI-driv…
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
Chengzhang Yu, Yiming Zhang, Zhixin Liu +3
The automation of scientific research through large language models (LLMs) presents significant opportunities but faces critical challenges in knowledge synthesis and quality assur…