\section{Introduction}\label{sec:intro}

\begin{figure}[t]
    \centering
    \includegraphics[width=0.95\linewidth]{images/owl-intro.pdf}
    \caption{Overview of \workforce and \owl.}
    \label{fig:owl-intro}
\end{figure}


% background
Large Language Models (LLMs) have undergone a period of rapid advancement, evolving from simple text predictors into powerful autonomous agents capable of planning, tool use, and multi-step reasoning~\citep{gpt4o,claude37}. 
Recently, multi-agent systems (MAS) have emerged as a promising approach for complex real-world tasks, demonstrating that dividing tasks among specialized agents can enhance performance~\citep{camel,chatdev,ag2}.

% While single LLM-based agents have demonstrated strong performance in controlled sandbox environments, their deployment in complex, real-world scenarios reveals significant limitations. 
% These include constraints on context length, error accumulation over long-horizon tasks, etc. 
% To address these challenges, multi-agent systems (MAS) have emerged as a promising approach, where frameworks like CAMEL~\citep{camel}, MetaGPT~\citep{metagpt}, and ChatDev~\citep{chatdev} have 
% shown that dividing tasks among specialized agents can enhance performance and scalability. 

Although current MAS have yielded impressive results, their designs are typically domain-specific, severely restricting \textbf{cross-domain transferability}. This shortcoming appears in two forms:
\textit{(i)} First, on the \textbf{inference} side, deploying a system in a new domain often entails a full redesign; for instance, MetaGPT \citep{metagpt} depends on Standard Operating Procedures tailored to software engineering, hindering its extension to other fields. 
\textit{(ii)} Second, on the \textbf{training} side, existing works often optimize every agent. MALT~\citep{malt}, for example, follows a fixed generator-verifier-refiner pipeline, necessitating separate training for every component. Consequently, migrating such systems demands retraining the entire agent ensemble, sharply reducing flexibility. 
These drawbacks underscore the need for a generalized, modular multi-agent architecture that can be rapidly adapted to diverse domains with minimal retraining and redesign.

Building upon these observations, firstly, we introduce \workforce, a hierarchical \textbf{multi-agent inference} framework that decouples strategic planning from domain-specific execution. 
As is shown in Figure~\ref{fig:owl-intro}, This modular design comprises three core components:
\textit{(i)} Domain-agnostic \textbf{Planner}: Generates abstract task decompositions based on high-level goals.
\textit{(ii)} \textbf{Coordinator}: Assigns subtasks to appropriate workers.
%  using metadata about their capabilities.
\textit{(ii)} Domain-specific \textbf{Worker Nodes}: A set of specialized agents that perform tool calls to accomplish each subtasks.
The decoupling of these components enables plug-and-play extensibility, allowing \workforce to be seamlessly adapted to new domains by simply replacing or adding worker nodes.
Furthermore, this modular architecture facilitates \owl (\shortowl), a novel \textbf{multi-agent training} paradigm. 
\shortowl focuses on enhancing MAS's cross-domain transferability by training a generalizable domain-agnostic planner. 
Specifically, we employ a two-stage training strategy: Supervised Fine-Tuning (SFT) for planner initialization, followed by Reinforcement Learning~\citep{rafailov2023direct} to further enhance the model's generalization capabilities.

We evaluate our approach on the GAIA benchmark, a rigorous suite for generalist AI assistants that spans diverse domains and demands multimodal reasoning, code execution, and live web search~\citep{gaia}. \workforce attains an accuracy of \textbf{69.70\%}, surpassing strong commercial proprietary baselines like OpenAI's Deep Research~\citep{openai2024deepresearch} (55.15\%). 
To demonstrate the effectiveness of \shortowl, we further post-trained a strategic planner initialized by Qwen2.5-32B-Instruct~\citep{qwen2} on a custom-curated training dataset without using any GAIA data.
After training, the model reaches 52.73\% score (\textbf{+16.37\%}), outperforming models like GPT-4o-mini~\citep{gpt4o} (47.27\%) and Qwen2.5-72B-Instruct (49.09\%). 
These results confirm that our modular training strategy generalizes across domains while requiring minimal retraining.


% Our contribution could be summarized as follows:

\textbf{Our key contributions are fourfold:}

\noindent \textbf{1. A New Flexible and Modular Multi-Agent Architecture.} We propose \workforce that is modular and scalable in both inference and training, enhancing cross-domain transferability.

\noindent \textbf{2. State-of-the-Art Performance.} Our system achieves open-source state-of-the-art performance on the GAIA benchmark, even surpassing proprietary systems like OpenAI's Deep Research.

\noindent \textbf{3. Efficient and Effective Training Paradigm.} \shortowl significantly enhances model capabilities with minimal overhead, enabling Qwen2.5-32B-Instruct to achieve a \textbf{16.37\%} performance gain and reach comparable performance to GPT-4o on challenging tasks.

\noindent \textbf{4. Fully Open-Source.} We release all code, models, and data to support open research.


