Multi-Agent Collaboration via Evolving Orchestration Article Swipe

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Yufan Dang , Qian Chen , X. L. Luo , Jin‐Hu Fan , Zihao Xie , R. S. Shi , Weize Chen , Cheng Yang , Xiaoyin Che , Ye Tian , Xuantang Xiong , Lei Han , Zhiyuan Liu , Maosong Sun ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.19591

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.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.19591
PDF: https://arxiv.org/pdf/2505.19591
OA Status: green
OpenAlex ID: https://openalex.org/W4414586724

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DOI: https://doi.org/10.48550/arxiv.2505.19591

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Title: Multi-Agent Collaboration via Evolving Orchestration

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-26

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Authors: Yufan Dang, Qian Chen, X. L. Luo, Jin‐Hu Fan, Zihao Xie, R. S. Shi, Weize Chen, Cheng Yang, Xiaoyin Che, Ye Tian, Xuantang Xiong, Lei Han, Zhiyuan Liu, Maosong Sun

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Landing page: https://arxiv.org/abs/2505.19591

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PDF URL: https://arxiv.org/pdf/2505.19591

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