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CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation

Gengxin Sun, Ruihao Yu, Liangyi Yin, Yunqi Yang, Bin Zhang, Zhiwei Xu

Abstract

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.

CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation

Abstract

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.
Paper Structure (26 sections, 6 figures, 4 tables)

This paper contains 26 sections, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multi-Agent Systems
  4. LLM-Driven Chatbots
  5. AI-Assisted Recruitment and Assessment
  6. System Design and Methodology
  7. Multi-Agent Architecture
  8. Coordination and Communication
  9. Specialized Functional Agents
  10. Storage and Knowledge Systems
  11. System Integration and Deployment
  12. Experiments and Result Analysis
  13. Experimental Setup and Baselines
  14. Core Evaluation Metrics
  15. Results and Analysis
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Overview of CoMAI, a collaborative multi-agent interview framework that orchestrates specialized agents through a centralized controller.
  • Figure 2: Process overview of the CoMAI framework. The system retrieves a candidate's resume from the database, which triggers the Question Generation agent to formulate interview questions. Responses are first screened by the Security agent; if approved, they are evaluated by the Scoring agent and archived in the internal memory. Feedback from the Scoring agent informs subsequent question generation. Upon completion of the interview, the Summary agent consolidates all information into a final report, which is stored in the database along with the raw records.
  • Figure 3: CoMAI dynamically asks follow-up questions to probe the interviewee’s reasoning process.
  • Figure 4: Categories of intercepted prompt-word attacks.
  • Figure 5: Comparison of single-agent and multi-agent architectures under adversarial attacks.
  • ...and 1 more figures