Aime: Towards Fully-Autonomous Multi-Agent Framework

In this episode, we discuss Aime: Towards Fully-Autonomous Multi-Agent Framework by Yexuan Shi, Mingyu Wang, Yunxiang Cao, Hongjie Lai, Junjian Lan, Xin Han, Yu Wang, Jie Geng, Zhenan Li, Zihao Xia, Xiang Chen, Chen Li, Jian Xu, Wenbo Duan, Yuanshuo Zhu. The paper presents Aime, a novel multi-agent system framework that improves upon traditional plan-and-execute methods by enabling dynamic, reactive planning and execution. Key innovations include a Dynamic Planner, an Actor Factory for on-demand specialized agent creation, and a centralized Progress Management Module for coherent state tracking. Empirical evaluations show that Aime outperforms specialized state-of-the-art agents across multiple complex tasks, demonstrating greater adaptability and success.