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\newcommand{\owl}{\textsc{Optimized Workforce Learning}\xspace}
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% Title
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\title{\logo\textls[20]{OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation}}

% Author
\author[1,2,4*]{Mengkang Hu}
\author[1*]{Yuhang Zhou}
\author[2,4]{Wendong Fan}
\author[3]{Yuzhou Nie}
\author[1,2]{Bowei Xia}
\author[2,4]{Tao Sun}
\author[5]{Ziyu Ye}
\author[2,4,6]{Zhaoxuan Jin}
\author[7]{Yingru Li}
\author[1]{Qiguang Chen}
\author[2,4,8]{Zeyu Zhang}
\author[2,4]{Yifeng Wang}
\author[2,4,9]{Qianshuo Ye}
\author[2,10]{Bernard Ghanem}
\author[1]{Ping Luo}
\author[2,4$\dagger$]{Guohao Li}

% Affiliation
\affiliation[1]{The University of Hong Kong}
\affiliation[2]{CAMEL-AI.org}
\affiliation[3]{UCSB}
\affiliation[4]{Eigent.AI}
\affiliation[5]{The University of Chicago}
\affiliation[6]{Northwestern University}
\affiliation[7]{CUHK}
\affiliation[8]{ANU}
\affiliation[9]{University of Cambridge}
\affiliation[10]{KAUST}

% \contribution{Full author list in Contributions}
\contribution[*]{Equal Contribution}
\contribution[\dagger]{Corresponding author}

\vspace{-0.3cm}
\abstract{
Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. 
Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains. 
We introduce \textbf{\workforce}, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: 
\textit{(i)} a \textit{domain-agnostic} \textbf{Planner} for task decomposition, 
\textit{(ii)} a \textbf{Coordinator} for subtask management, and \textit{(iii)} specialized \textbf{Workers} with \textit{domain-specific} tool-calling capabilities. 
This decoupling enables cross-domain transferability during both inference and training phases: 
During inference, \workforce seamlessly adapts to new domains by adding or modifying worker agents; 
For training, we introduce \textbf{\owl (\shortowl)}, which improves generalization across domains by optimizing a domain-agnostic planner with reinforcement learning from real-world feedback.
To validate our approach, we evaluate \workforce on the GAIA benchmark, covering various realistic, multi-domain agentic tasks.
Experimental results demonstrate \workforce achieves open-source state-of-the-art performance (\textbf{69.70\%}), outperforming commercial systems like OpenAI's Deep Research by \textbf{2.34\%}. 
More notably, our OWL-trained 32B model achieves \textbf{52.73\%} accuracy (\textbf{+16.37\%}) and demonstrates performance comparable to GPT-4o on challenging tasks. 
To summarize, by enabling scalable generalization and modular domain transfer, our work establishes a foundation for the next generation of general-purpose AI assistants. 
}

\date{May 26, 2025}
% \correspondence{Corresponding at \email{mkhu@connect.hku.hk}}
\correspondence{Guohao Li at \href{mailto:guohao.li@eigent.ai}{\textcolor{speciallink}{guohao.li@eigent.ai}}}
\checkdata[Project Page]{\href{https://github.com/camel-ai/owl}{\textcolor{speciallink}{https://github.com/camel-ai/owl}}}

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\begin{figure}[b]
    \centering
    \includegraphics[width=0.99\linewidth]{images/performance.pdf}
    \caption{Performance comparison of \workforce and \shortowl with other baselines on GAIA benchmark~\citep{gaia}.}
    \label{fig:owl-abs}
\end{figure}


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