\section{Preliminary}\label{preliminary}

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\textbf{Large Language Model (LLM)-based Agents} are autonomous systems that perceive, reason, and act in various environments~\citep{TianbaoXie2023_OpenAgents}. 
These agents operate in a perception-reasoning-action loop, where they observe their environment, process information through the language model, determine appropriate actions, and execute them to achieve goals.

\textbf{Multi-Agent Systems} extend this paradigm by enabling multiple LLM-based agents to collaborate on complex tasks. Frameworks such as CAMEL~\citep{camel} and MetaGPT~\citep{metagpt} have demonstrated that collaborative approaches can outperform single-agent systems on tasks requiring diverse expertise. 
However, existing multi-agent frameworks are typically constrained by domain-specific designs that limit their broader applicability. This paper aims to develop scalable and generalizable multi-agent frameworks \workforce through strategic planning and task execution decoupling, enabling efficient coordination across diverse domains. (See complete formalization in Appendix~\ref{app:formalization} and more related works in Section~\ref{sec:related_work}.)

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\noindent \textbf{Generalist AI Assistant}  was first introduced by GAIA~\citep{gaia}. 
These systems are designed to handle a diverse range of complicated tasks across multiple domains and modalities.
As the first question answering benchmark evaluating generalist AI assistants, GAIA proposes to enable LLM-based agents to gather information in real-world contexts, testing fundamental capabilities including multi-modal understanding, web browsing, reasoning, and complex problem-solving.
Recently, numerous companies have released generalist AI assistant products (e.g., OpenAI's Deep Research~\citep{openai2024deepresearch}). 
While open-source frameworks have made significant progress (e.g., Huggingface's Open Deep Research~\citep{roucher2025deepresearch}), they still lag behind commercial solutions.
In this paper, we aim to close the gap between open-source and commercial proprietary agent frameworks. The proposed \workforce surpasses OpenAI's Deep Research by 2.34\%, while our training method \shortowl significantly improves Qwen2.5-32B-Instruct's performance by 16.37\%.
