Multi-Agent Collaboration via Evolving Orchestration
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915564808568832 |
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| author | Dang, Yufan Qian, Chen Luo, Xueheng Fan, Jingru Xie, Zihao Shi, Ruijie Chen, Weize Yang, Cheng Che, Xiaoyin Tian, Ye Xiong, Xuantang Han, Lei Liu, Zhiyuan Sun, Maosong |
| author_facet | Dang, Yufan Qian, Chen Luo, Xueheng Fan, Jingru Xie, Zihao Shi, Ruijie Chen, Weize Yang, Cheng Che, Xiaoyin Tian, Ye Xiong, Xuantang Han, Lei Liu, Zhiyuan Sun, Maosong |
| contents | Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving. While recent research explores multi-agent collaboration among LLMs, most approaches rely on static organizational structures that struggle to adapt as task complexity and agent numbers grow, resulting in coordination overhead and inefficiencies. To this end, we propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states. This orchestrator is trained via reinforcement learning to adaptively sequence and prioritize agents, enabling flexible and evolvable collective reasoning. Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs. Analyses further reveal that the key improvements consistently stem from the emergence of more compact, cyclic reasoning structures under the orchestrator's evolution. Our code is available at https://github.com/OpenBMB/ChatDev/tree/puppeteer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Collaboration via Evolving Orchestration Dang, Yufan Qian, Chen Luo, Xueheng Fan, Jingru Xie, Zihao Shi, Ruijie Chen, Weize Yang, Cheng Che, Xiaoyin Tian, Ye Xiong, Xuantang Han, Lei Liu, Zhiyuan Sun, Maosong Computation and Language Artificial Intelligence Multiagent Systems Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving. While recent research explores multi-agent collaboration among LLMs, most approaches rely on static organizational structures that struggle to adapt as task complexity and agent numbers grow, resulting in coordination overhead and inefficiencies. To this end, we propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states. This orchestrator is trained via reinforcement learning to adaptively sequence and prioritize agents, enabling flexible and evolvable collective reasoning. Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs. Analyses further reveal that the key improvements consistently stem from the emergence of more compact, cyclic reasoning structures under the orchestrator's evolution. Our code is available at https://github.com/OpenBMB/ChatDev/tree/puppeteer. |
| title | Multi-Agent Collaboration via Evolving Orchestration |
| topic | Computation and Language Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2505.19591 |