{"ID":2882863,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09889","arxiv_id":"2508.09889","title":"Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld","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, this reliance introduces new challenges, as extended contexts and noisy tool outputs can undermine system reliability. To address this, we propose a dynamic Multi-Agent System (MAS) in our AWorld framework, where an Execution Agent is supervised by a Guard Agent that provides on-demand dynamic maneuvering, verifying and correcting the reasoning process to improve robustness over single-agent systems. To move beyond this generic supervision, we enhance the architecture with a methodology inspired by System Identification from control theory. This method first profiles the Execution Agent offline on a benchmark dataset to create a \"performance fingerprint\" of its unique weaknesses. The Guard Agent then leverages this fingerprint online to deliver profile-aware supervision, making targeted interventions based on known failure patterns rather than merely reacting to immediate logical flaws. Extensive experiments on the GAIA dataset demonstrate that this profile-aware MAS significantly improves both effectiveness and stability, outperforming not only single-agent systems but also its naive counterpart. This superior performance led our system to achieve first place among open-source projects on the prestigious GAIA leaderboard. These findings highlight that building truly trustworthy intelligent systems requires not just collaboration, but a deep, empirically-grounded understanding of each agent's unique capabilities and limitations.","short_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, this reliance introduces new challenges, as extended contexts and noisy tool outputs can undermine system reliability. To address this, we propose a...","url_abs":"https://arxiv.org/abs/2508.09889","url_pdf":"https://arxiv.org/pdf/2508.09889v4","authors":"[\"Zhitian Xie\",\"Qintong Wu\",\"Chengyue Yu\",\"Chenyi Zhuang\",\"Jinjie Gu\"]","published":"2025-08-13T15:46:25Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}