揭秘多智能体协作的四大策略，提升任务准确率与效率
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems
- 分析治理、参与、交互和记忆管理四类协作机制
- 实验证明集中治理+有序交互可提升准确率并降低资源消耗
- 适合研究智能体系统设计与高效协作的开发者
多智能体协作已成为大语言模型驱动应用中解决复杂分布式任务的关键范式。尽管先前研究聚焦于高层架构框架，但影响性能与可扩展性的智能体底层机制仍缺乏深入探索。本研究系统考察了四个协作维度：（1）智能体治理，（2）参与控制，（3）交互动态，（4）对话历史管理。在两种情境依赖场景——分布式证据整合（DEI）与结构化证据合成（SES）下进行严格实验，量化评估这些策略对任务准确率与计算效率的影响。结果表明，集中式治理、导师主导的参与、有序交互模式以及导师定制的上下文摘要，共同优化了决策质量与资源利用之间的权衡，依托提出的令牌-准确率比（TAR）指标。本工作为构建自适应、可扩展的多智能体系统奠定基础，推动研究重心从结构创新转向交互机制设计。
原文摘要 · Abstract (English)
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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