CoMAI用多个专业智能体协作评估面试，更安全公平且结果可解释。
CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation
- 四类智能体分工协作，通过状态机协调任务分解。
- 面试评估准确率达90.47%，候选者满意度达84.41%。
- 适合需要降低偏见、保障安全的招聘自动化场景。
确保人工智能驱动的面试评估具备鲁棒性和公平性仍是关键挑战。本文提出CoMAI，一种通用的多智能体面试评估框架，适用于多种评估场景。与基于大语言模型的单体系统不同，CoMAI采用模块化任务分解架构，由中心化的有限状态机协调。系统包含四类专门智能体：题目生成、安全防护、评分与摘要。它们协同工作，提供多层防御以抵御提示注入攻击，支持多维度评估并动态调节难度，实现基于评分量表的结构化打分，减少主观偏差。实验表明，CoMAI在评估中达到90.47%的准确率、83.33%的召回率和84.41%的候选人满意度。结果表明，CoMAI是一种鲁棒、公平且可解释的人工智能面试评估范式。
原文摘要 · Abstract (English)
Ensuring robust and fair interview assessment remains a key challenge in AI-driven evaluation. This paper presents CoMAI, a general-purpose multi-agent interview framework designed for diverse assessment scenarios. In contrast to monolithic single-agent systems based on large language models (LLMs), CoMAI employs a modular task-decomposition architecture coordinated through a centralized finite-state machine. The system comprises four agents specialized in question generation, security, scoring, and summarization. These agents work collaboratively to provide multi-layered security defenses against prompt injection, support multidimensional evaluation with adaptive difficulty adjustment, and enable rubric-based structured scoring that reduces subjective bias. Experimental results demonstrate that CoMAI achieved 90.47% accuracy, 83.33% recall, and 84.41% candidate satisfaction. These results highlight CoMAI as a robust, fair, and interpretable paradigm for AI-driven interview assessment.
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