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    <abstract>Multi-agent collaboration has emerged as a pivotal paradigm for addressing complex, distributed tasks in large language model (LLM)-driven applications. While prior research has focused on high-level architectural frameworks, the granular mechanisms governing agents—critical to performance and scalability—remain underexplored. This study systematically investigates four dimensions of collaboration strategies: (1) agent governance, (2) participation control, (3) interaction dynamics, and (4) dialogue history management. Through rigorous experimentation under two context-dependent scenarios—Distributed Evidence Integration (DEI) and Structured Evidence Synthesis (SES)—we quantify the impact of these strategies on both task accuracy and computational efficiency. Our findings reveal that centralized governance, instructor-led participation, ordered interaction patterns, and instructor-curated context summarization collectively optimize the trade-off between decision quality and resource utilization with the support of the proposed Token-Accuracy Ratio (TAR). This work establishes a foundation for designing adaptive, scalable multi-agent systems, shifting the focus from structural novelty to strategic interaction mechanics.</abstract>
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%0 Conference Proceedings
%T Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems
%A Wang, Haochun
%A Zhao, Sendong
%A Wang, Jingbo
%A Qiang, Zewen
%A Qin, Bing
%A Liu, Ting
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-beyond
%X Multi-agent collaboration has emerged as a pivotal paradigm for addressing complex, distributed tasks in large language model (LLM)-driven applications. While prior research has focused on high-level architectural frameworks, the granular mechanisms governing agents—critical to performance and scalability—remain underexplored. This study systematically investigates four dimensions of collaboration strategies: (1) agent governance, (2) participation control, (3) interaction dynamics, and (4) dialogue history management. Through rigorous experimentation under two context-dependent scenarios—Distributed Evidence Integration (DEI) and Structured Evidence Synthesis (SES)—we quantify the impact of these strategies on both task accuracy and computational efficiency. Our findings reveal that centralized governance, instructor-led participation, ordered interaction patterns, and instructor-curated context summarization collectively optimize the trade-off between decision quality and resource utilization with the support of the proposed Token-Accuracy Ratio (TAR). This work establishes a foundation for designing adaptive, scalable multi-agent systems, shifting the focus from structural novelty to strategic interaction mechanics.
%R 10.18653/v1/2025.acl-long.1037
%U https://aclanthology.org/2025.acl-long.1037/
%U https://doi.org/10.18653/v1/2025.acl-long.1037
%P 21361-21375

Markdown (Informal)

[Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems](https://aclanthology.org/2025.acl-long.1037/) (Wang et al., ACL 2025)

• Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (Wang et al., ACL 2025)

ACL

• Haochun Wang, Sendong Zhao, Jingbo Wang, Zewen Qiang, Bing Qin, and Ting Liu. 2025. Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21361–21375, Vienna, Austria. Association for Computational Linguistics.