Aime: Towards Fully-Autonomous Multi-Agent Framework

• URL: http://arxiv.org/abs/2507.11988v2
• Date: Thu, 17 Jul 2025 03:34:27 GMT
• Title: Aime: Towards Fully-Autonomous Multi-Agent Framework
• Authors: 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,
• Abstract summary: Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) are emerging as a powerful paradigm for solving complex, multifaceted problems.<n>The potential of these systems is often constrained by the prevalent plan-and-execute framework, which suffers from critical limitations.<n>This paper introduces Aime, a novel multi-agent framework designed to overcome these challenges through dynamic, reactive planning and execution.
• Score: 13.494469496862534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) are emerging as a powerful paradigm for solving complex, multifaceted problems. However, the potential of these systems is often constrained by the prevalent plan-and-execute framework, which suffers from critical limitations: rigid plan execution, static agent capabilities, and inefficient communication. These weaknesses hinder their adaptability and robustness in dynamic environments. This paper introduces Aime, a novel multi-agent framework designed to overcome these challenges through dynamic, reactive planning and execution. Aime replaces the conventional static workflow with a fluid and adaptive architecture. Its core innovations include: (1) a Dynamic Planner that continuously refines the overall strategy based on real-time execution feedback; (2) an Actor Factory that implements Dynamic Actor instantiation, assembling specialized agents on-demand with tailored tools and knowledge; and (3) a centralized Progress Management Module that serves as a single source of truth for coherent, system-wide state awareness. We empirically evaluated Aime on a diverse suite of benchmarks spanning general reasoning (GAIA), software engineering (SWE-bench Verified), and live web navigation (WebVoyager). The results demonstrate that Aime consistently outperforms even highly specialized state-of-the-art agents in their respective domains. Its superior adaptability and task success rate establish Aime as a more resilient and effective foundation for multi-agent collaboration.

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