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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.23885 |
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| _version_ | 1866913888958676992 |
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| author | Hu, Mengkang Zhou, Yuhang Fan, Wendong Nie, Yuzhou Xia, Bowei Sun, Tao Ye, Ziyu Jin, Zhaoxuan Li, Yingru Chen, Qiguang Zhang, Zeyu Wang, Yifeng Ye, Qianshuo Ghanem, Bernard Luo, Ping Li, Guohao |
| author_facet | Hu, Mengkang Zhou, Yuhang Fan, Wendong Nie, Yuzhou Xia, Bowei Sun, Tao Ye, Ziyu Jin, Zhaoxuan Li, Yingru Chen, Qiguang Zhang, Zeyu Wang, Yifeng Ye, Qianshuo Ghanem, Bernard Luo, Ping Li, Guohao |
| contents | 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 Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with 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 Optimized Workforce Learning (OWL), 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 (69.70%), outperforming commercial systems like OpenAI's Deep Research by 2.34%. More notably, our OWL-trained 32B model achieves 52.73% accuracy (+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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation Hu, Mengkang Zhou, Yuhang Fan, Wendong Nie, Yuzhou Xia, Bowei Sun, Tao Ye, Ziyu Jin, Zhaoxuan Li, Yingru Chen, Qiguang Zhang, Zeyu Wang, Yifeng Ye, Qianshuo Ghanem, Bernard Luo, Ping Li, Guohao Artificial Intelligence Computation and Language 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 Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with 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 Optimized Workforce Learning (OWL), 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 (69.70%), outperforming commercial systems like OpenAI's Deep Research by 2.34%. More notably, our OWL-trained 32B model achieves 52.73% accuracy (+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. |
| title | OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.23885 |