AI RESEARCH
CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation
arXiv CS.AI
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ArXi:2603.16215v1 Announce Type: cross 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.