Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper introduces \textbf{Multi-Agent for Knowledge Augmentation and Reasoning (MAKAR)}, a novel multi-agent system that enhances patient-trial matching by integrating criterion augmentation with structured reasoning. MAKAR consistently improves performance by an average of 7\% across different datasets. Furthermore, it enables privacy-preserving deployment and maintains competitive performance when using smaller open-source models. Overall, MAKAR can contributes to more transparent, accurate, and privacy-conscious AI-driven patient matching.

翻译：由于患者档案和试验标准的复杂性和多样性，有效且高效地匹配临床试验患者是一项重大挑战。本文提出了一种新型多智能体系统——**多智能体知识增强与推理系统（MAKAR）**，该系统通过整合标准增强与结构化推理来提升患者与试验的匹配效果。MAKAR在不同数据集上持续提升性能，平均提高7%。此外，它支持隐私保护部署，并在使用较小的开源模型时保持有竞争力的性能。总体而言，MAKAR有助于实现更透明、更准确且更注重隐私的AI驱动患者匹配。