280 citations · 293 across the 69 of their papers we have counts for
9 papers · 1 filter
BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry
Zuo Fei, Kezhi Wang, Xiaomin Chen +1
Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language mode…
Secure and Energy-Efficient Wireless Agentic AI Networks
Yuanyan Song, Kezhi Wang, Xinmian Xu
In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI agents to provision quality of service (QoS) for users' re…
Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation
Fahad Anwaar, Adil Mehmood Khan, Muhammad Khalid +2
Large Language Models (LLMs) exhibit potential for explainable recommendation systems but overlook collaborative signals, while prevailing methods treat recommendation and explanat…
ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks
Haoyun Li, Ming Xiao, Kezhi Wang +3
Emerging 6G networks rely on complex cross-layer optimization, yet manually translating high-level intents into mathematical formulations remains a bottleneck. While Large Language…
Agentic AI Empowered Intent-Based Networking for 6G
Genze Jiang, Kezhi Wang, Xiaomin Chen +1
The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executabl…
SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks
Lin Zhu, Kezhi Wang, Luping Xiang +1
Efficient information exchange and reliable contextual reasoning are essential for vehicle-to-everything (V2X) networks. Conventional communication schemes often incur significant…
From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications
Feibo Jiang, Cunhua Pan, Li Dong +3
With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, a…
Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Models
Yizhou Huang, Yihua Cheng, Kezhi Wang
Deep learning architectures with powerful reasoning capabilities have driven significant advancements in autonomous driving technology. Large language models (LLMs) applied in this…
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
Feibo Jiang, Li Dong, Yubo Peng +5
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task…