2 citations · 2 across the 5 of their papers we have counts for
6 papers
Reinforced Collaboration in Multi-Agent Flow Networks
Zheng Wang, Yuang Liu, Yangkai Ding
Multi-agent systems provide a powerful way to extend large language models (LLMs) by decomposing a complex task into specialized subtasks handled by different agents. However, thei…
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
TrajSV: A Trajectory-based Model for Sports Video Representations and Applications
Zheng Wang, Shihao Xu, Wei Shi
Sports analytics has received significant attention from both academia and industry in recent years. Despite the growing interest and efforts in this field, several issues remain u…
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning
Zheng Wang, Shu Xian Teo, Jun Jie Chew +1
Recent advancements in large language models (LLMs) have enabled their use as agents for planning complex tasks. Existing methods typically rely on a thought-action-observation (TA…
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
Zheng Wang, Zhongyang Li, Zeren Jiang +2
In the age of mobile internet, user data, often referred to as memories, is continuously generated on personal devices. Effectively managing and utilizing this data to deliver serv…
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions
Zheng Wang, Shu Xian Teo, Jieer Ouyang +2
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organi…