标题： 多智能体RAG在现实世界大学招生咨询中的实证研究 显示英文标题

标题： An Empirical Study of Multi-Agent RAG for Real-World University Admissions Counseling

摘要： 本文介绍了MARAUS（多智能体和检索增强的大学入学系统），这是一个在越南高等教育入学咨询中实际部署的对话式AI平台。虽然大型语言模型（LLMs）在自动化咨询服务方面具有潜力，但大多数现有解决方案仍局限于原型或合成基准测试。MARAUS通过将混合检索、多智能体编排和基于LLM的生成结合到一个针对现实世界大学入学的系统中来弥补这一差距。与河内的交通技术大学（UTT）合作，我们进行了两阶段的研究，包括技术开发和实际评估。MARAUS处理了超过6000次实际用户交互，涵盖了六类查询。结果显示，相比仅使用LLM的基线有显著提升：平均准确率为92%，幻觉率从15%降至1.45%，平均响应时间低于4秒。该系统运行成本效益高，使用GPT-4o mini进行两周的部署成本为11.58美元。这项工作为在资源有限的教育环境中部署代理RAG系统提供了可操作的见解。 显示英文摘要

摘要： This paper presents MARAUS (Multi-Agent and Retrieval-Augmented University Admission System), a real-world deployment of a conversational AI platform for higher education admissions counseling in Vietnam. While large language models (LLMs) offer potential for automating advisory tasks, most existing solutions remain limited to prototypes or synthetic benchmarks. MARAUS addresses this gap by combining hybrid retrieval, multi-agent orchestration, and LLM-based generation into a system tailored for real-world university admissions. In collaboration with the University of Transport Technology (UTT) in Hanoi, we conducted a two-phase study involving technical development and real-world evaluation. MARAUS processed over 6,000 actual user interactions, spanning six categories of queries. Results show substantial improvements over LLM-only baselines: on average 92 percent accuracy, hallucination rates reduced from 15 precent to 1.45 percent, and average response times below 4 seconds. The system operated cost-effectively, with a two-week deployment cost of 11.58 USD using GPT-4o mini. This work provides actionable insights for the deployment of agentic RAG systems in low-resource educational settings.