标题： Aime：面向完全自主的多智能体框架 显示英文标题

标题： Aime: Towards Fully-Autonomous Multi-Agent Framework

摘要： 多智能体系统（MAS）由大型语言模型（LLMs）驱动，正在成为解决复杂、多方面问题的强大范式。 然而，这些系统的潜力通常受到普遍的计划与执行框架的限制，该框架存在关键缺陷：固定的计划执行、静态的智能体能力以及低效的通信。 这些弱点阻碍了它们在动态环境中的适应性和鲁棒性。 本文介绍了Aime，一种新的多智能体框架，旨在通过动态、反应式的规划和执行克服这些挑战。 Aime用流动且自适应的架构取代了传统的静态工作流。 其核心创新包括：(1) 一个动态规划器，根据实时执行反馈持续优化整体策略；(2) 一个Actor Factory，实现动态Actor实例化，按需组装具有定制工具和知识的专业化智能体；以及 (3) 一个集中式进度管理模块，作为全局状态感知的单一真实来源。 我们在涵盖通用推理（GAIA）、软件工程（SWE-bench Verified）和实时网络导航（WebVoyager）的多样化基准测试中对Aime进行了实证评估。 结果表明，Aime在其各自领域中甚至优于高度专业化的最先进智能体。 其卓越的适应性和任务成功率确立了Aime作为多智能体协作更强大和有效基础的地位。 显示英文摘要

摘要： 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.