Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems

• URL: http://arxiv.org/abs/2505.12467v1
• Date: Sun, 18 May 2025 15:46:14 GMT
• Title: Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems
• Authors: Haochun Wang, Sendong Zhao, Jingbo Wang, Zewen Qiang, Bing Qin, Ting Liu,
• Abstract summary: This study systematically investigates four dimensions of collaboration strategies.<n>We quantify the impact of these strategies on both task accuracy and computational efficiency.<n>This work establishes a foundation for designing adaptive, scalable multi-agent systems.
• Score: 29.924868489451327
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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