2 citations · 2 across the 5 of their papers we have counts for
9 papers
Rethinking Multi-Agent Intelligence Through the Lens of Small-World Networks
Boxuan Wang, Zhuoyun Li, Xiaowei Huang +1
Large language models (LLMs) have enabled multi-agent systems (MAS) in which multiple agents argue, critique, and coordinate to solve complex tasks, making communication topology a…
Distributed Nash Equilibrium Seeking Algorithm in Aggregative Games for Heterogeneous Multi-Robot Systems
Yi Dong, Zhongguo Li, Sarvapali D. Ramchurn +1
This paper develops a distributed Nash Equilibrium seeking algorithm for heterogeneous multi-robot systems. The algorithm utilises distributed optimisation and output control to ac…
Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Jinwei Hu, Yi Dong, Youcheng Sun +1
Autonomous agents in safety-critical applications must continuously adapt to dynamic conditions without compromising performance and reliability. This work introduces TAPA (Trainin…
Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing
Jinwei Hu, Yi Dong, Zhengtao Ding +1
This paper presents a defense framework for enhancing the safety of large language model (LLM) empowered multi-agent systems (MAS) in safety-critical domains such as aerospace. We…
Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems
Jinwei Hu, Zezhi Tang, Xin Jin +3
This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Progn…
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
Shuang Ao, Yi Dong, Jinwei Hu +1
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) enhances adaptability while reducing computational costs. However, fine-tuning can compromise safety alignm…