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# ── CITATION.cff ───────────────────────────────────────────────
cff-version: 1.2.0
title: "OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation"
message: "If you use OWL or find it helpful, please cite this paper."
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. We introduce Workforce, a
hierarchical multi-agent framework that decouples strategic planning
from specialized execution via a domain-agnostic Planner, a
Coordinator, and domain-specific Worker agents. Optimized Workforce
Learning (OWL) further improves cross-domain generalization through
reinforcement learning from real-world feedback. Experiments on the
GAIA benchmark show state-of-the-art open-source performance
(69.70 %), surpassing commercial systems like Deep Research and
approaching GPT-4o. By enabling scalable generalization and modular
domain transfer, OWL lays a foundation for the next generation of
general-purpose AI assistants.
version: "v2"
doi: 10.48550/arXiv.2505.23885
url: https://arxiv.org/pdf/2505.23885
date-released: 2025-06-11
authors:
- family-names: Hu
given-names: Mengkang
- family-names: Zhou
given-names: Yuhang
- family-names: Fan
given-names: Wendong
- family-names: Nie
given-names: Yuzhou
- family-names: Xia
given-names: Bowei
- family-names: Sun
given-names: Tao
- family-names: Ye
given-names: Ziyu
- family-names: Jin
given-names: Zhaoxuan
- family-names: Li
given-names: Yingru
- family-names: Chen
given-names: Qiguang
- family-names: Zhang
given-names: Zeyu
- family-names: Wang
given-names: Yifeng
- family-names: Ye
given-names: Qianshuo
- family-names: Ghanem
given-names: Bernard
- family-names: Luo
given-names: Ping
- family-names: Li
given-names: Guohao
# ───────────────────────────────────────────────────────────────