Abstract
The rapid advancement of large language models (LLMs) has empowered intelligent agents to leverage diverse external tools for solving complex real-world problems. However, as agents increasingly depend on multiple tools, they encounter new challenges: extended contexts from disparate sources and noisy or irrelevant tool outputs can undermine system reliability and accuracy. These challenges underscore the necessity for enhanced stability in agent-based systems. To address this, we introduce dynamic supervision and maneuvering mechanisms, constructing a robust and dynamic Multi-Agent System (MAS) architecture within the AWorld framework. In our approach, the Execution Agent invokes the Guard Agent at critical steps to verify and correct the reasoning process, effectively reducing errors arising from noise and bolstering problem-solving robustness. Extensive experiments on the GAIA test dataset reveal that our dynamic maneuvering mechanism significantly improves both the effectiveness and stability of solutions, outperforming single-agent system (SAS) and standard tool-augmented systems. As a result, our dynamic MAS system achieved first place among open-source projects on the prestigious GAIA leaderboard. These findings highlight the practical value of collaborative agent roles in developing more reliable and trustworthy intelligent systems.
Abstract (translated)
大型语言模型（LLMs）的快速发展使得智能代理能够利用多种外部工具解决复杂的真实世界问题。然而，随着代理越来越多地依赖于多个工具，它们遇到了新的挑战：来自不同来源的扩展上下文和噪声或无关的工具输出会削弱系统的可靠性和准确性。这些挑战凸显了在基于代理的系统中增强稳定性的必要性。 为了解决这些问题，我们引入了动态监督和操控机制，在AWorld框架内构建了一个稳健且灵活的多智能体系统（MAS）架构。在此方法中，执行代理在关键步骤上调用守卫代理来验证和纠正推理过程，从而有效减少因噪声引发的错误，并增强解决问题的鲁棒性。 在GAIA测试数据集上的广泛实验表明，我们的动态操控机制显著提高了解决方案的有效性和稳定性，超越了单个智能体系统（SAS）和标准工具增强系统。因此，我们的动态MAS系统在著名的GAIA排行榜上赢得了开源项目的首位。 这些发现强调了合作型代理角色在开发更加可靠和值得信赖的智能系统的实际价值。
URL
https://arxiv.org/abs/2508.09889
