@inproceedings{du-etal-2025-multi,
title = "Multi-Agent Collaboration via Cross-Team Orchestration",
author = "Du, Zhuoyun and
Qian, Chen and
Liu, Wei and
Xie, Zihao and
Wang, YiFei and
Qiu, Rennai and
Dang, Yufan and
Chen, Weize and
Yang, Cheng and
Tian, Ye and
Xiong, Xuantang and
Han, Lei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.541/",
doi = "10.18653/v1/2025.findings-acl.541",
pages = "10386--10406",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software development, where agents can collaborate in a team like humans, following predefined phases to complete sub-tasks sequentially. However, for an agent team, each phase yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently leading to suboptimal results or extensive trial and error. To address this, we introduce Cross-Team Orchestration (Croto), a scalable multi-team framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. Experiments reveal a notable increase in software quality compared to state-of-the-art baselines. We further tested our framework on story generation tasks, which demonstrated a promising generalization ability of our framework in other domains. The code and data is available at https://github.com/OpenBMB/ChatDev/tree/macnet"
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<abstract>Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software development, where agents can collaborate in a team like humans, following predefined phases to complete sub-tasks sequentially. However, for an agent team, each phase yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently leading to suboptimal results or extensive trial and error. To address this, we introduce Cross-Team Orchestration (Croto), a scalable multi-team framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. Experiments reveal a notable increase in software quality compared to state-of-the-art baselines. We further tested our framework on story generation tasks, which demonstrated a promising generalization ability of our framework in other domains. The code and data is available at https://github.com/OpenBMB/ChatDev/tree/macnet</abstract>
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%0 Conference Proceedings
%T Multi-Agent Collaboration via Cross-Team Orchestration
%A Du, Zhuoyun
%A Qian, Chen
%A Liu, Wei
%A Xie, Zihao
%A Wang, YiFei
%A Qiu, Rennai
%A Dang, Yufan
%A Chen, Weize
%A Yang, Cheng
%A Tian, Ye
%A Xiong, Xuantang
%A Han, Lei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F du-etal-2025-multi
%X Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software development, where agents can collaborate in a team like humans, following predefined phases to complete sub-tasks sequentially. However, for an agent team, each phase yields only one possible outcome. This results in the completion of only one development chain, thereby losing the opportunity to explore multiple potential decision paths within the solution space. Consequently leading to suboptimal results or extensive trial and error. To address this, we introduce Cross-Team Orchestration (Croto), a scalable multi-team framework that enables orchestrated teams to jointly propose various task-oriented solutions and interact with their insights in a self-independence while cross-team collaboration environment for superior solutions generation. Experiments reveal a notable increase in software quality compared to state-of-the-art baselines. We further tested our framework on story generation tasks, which demonstrated a promising generalization ability of our framework in other domains. The code and data is available at https://github.com/OpenBMB/ChatDev/tree/macnet
%R 10.18653/v1/2025.findings-acl.541
%U https://aclanthology.org/2025.findings-acl.541/
%U https://doi.org/10.18653/v1/2025.findings-acl.541
%P 10386-10406
Markdown (Informal)
[Multi-Agent Collaboration via Cross-Team Orchestration](https://aclanthology.org/2025.findings-acl.541/) (Du et al., Findings 2025)
ACL
- Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, YiFei Wang, Rennai Qiu, Yufan Dang, Weize Chen, Cheng Yang, Ye Tian, Xuantang Xiong, and Lei Han. 2025. Multi-Agent Collaboration via Cross-Team Orchestration. In Findings of the Association for Computational Linguistics: ACL 2025, pages 10386–10406, Vienna, Austria. Association for Computational Linguistics.