Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems

TL;DR

This paper explores detailed collaboration strategies in multi-agent systems driven by large language models, emphasizing interaction mechanics and their impact on performance and efficiency.

Contribution

It systematically investigates four key collaboration dimensions and introduces the Token-Accuracy Ratio to optimize multi-agent system design.

Findings

Centralized governance improves decision accuracy.

Instructor-led participation enhances task performance.

Ordered interactions and curated context reduce resource use.

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.