Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems
Haochun Wang, Sendong Zhao, Jingbo Wang, Zewen Qiang, Bing Qin, Ting Liu
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.
BibTeX
@inproceedings{wang-etal-2025-beyond,
title = "Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems",
author = "Wang, Haochun and
Zhao, Sendong and
Wang, Jingbo and
Qiang, Zewen and
Qin, Bing and
Liu, Ting",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1037/",
doi = "10.18653/v1/2025.acl-long.1037",
pages = "21361--21375",
ISBN = "979-8-89176-251-0"
}