AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust
  GAIA Problem Solving
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.
689d4a16b083e610d741e9c1
Zhitian Xie
689d4a16b083e610d741e9c2
Qintong Wu
claimed_verified
2025-09-01T07:54:48.261Z
68243db9d08d8e01109dd4f4
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/iZKRzoFHewLgAyNVanWf_.png
Qing Wu
user
qingw-dev
689d4a16b083e610d741e9c3
Chengyue Yu
claimed_verified
2025-09-01T07:54:45.763Z
643a55c3fe6d02a5a2e14175
/avatars/ea3000cbf689046dfa0cb2f87041ec7c.svg
yuchengyue
user
yuyuzuicool
689d4a16b083e610d741e9c4
Chenyi Zhuang
689d4a16b083e610d741e9c5
Jinjie Gu
AWorld: Dynamic Multi-Agent System with Stable Maneuvering for Robust
  GAIA Problem Solving
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.
A dynamic Multi-Agent System (MAS) with Execution and Guard Agents improves the reliability and effectiveness of intelligent agents using external tools, outperforming single-agent systems on the GAIA leaderboard.
large language models Multi-Agent System Execution Agent Guard Agent dynamic supervision maneuvering mechanisms GAIA test dataset single-agent system tool-augmented systems GAIA leaderboard