Organizations: Shandong University, Qingdao, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Shandong University, Jinan, China
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.
Figures & tables
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.
Evaluation Entity
Recall
Accuracy
CoMAI (GPT-5-mini)
83.33%
90.47%
CoMAI (Qwen-plus-2025-07-28)
90.90%
80.00%
CoMAI (Kimi-K2-Instruct)
95.45%
91.30%
Human Interviewer
62.50%
71.42%
Single-Agent Baseline
50.00%
60.00%
LLM-Interviewer
100.00%
42.30%
Table 1. Comparison of assessment accuracy across evaluation entities.
Evaluation Entity
Mean Score
Variance
Admission Rate ( ≥ 70)
Expert Baseline (d)
68.88
–
44.44%
CoMAI (GPT-5-mini)
62.05
348.65
40.00%
CoMAI (Qwen-plus-2025-07-28)
62.34
395.68
48.07%
CoMAI (Kimi-K2-Instruct)
62.92
320.82
44.23%
Human Interviewer
67.54
177.16
38.18%
Single-Agent Baseline
61.45
359.34
34.54%
Table 2. Statistics of interview score distribution (averaged across 55 participants).
Evaluation Entity
Defense Success Rate (%)
CoMAI
100.00
Single-Agent Baseline
81.55
LLM-Interviewer
0.00
AI-Interviewer-Bot v3
28.00
Table 3. Defense success rate against adversarial attacks.
Figure 5. Comparison of single-agent and multi-agent architectures under adversarial attacks.
Evaluation Entity
Satisfaction (%)
Fluency (%)
Feedback Request Rate (%)
CoMAI
84.41
77.00
79.16
Human Interviewer
85.24
–
67.23
Single-Agent Baseline
61.12
43.00
71.33
External AI Interviewers
53.00–63.00
65.00–67.00
60.00–70.00
Table 4. User experience and process quality metrics.
Figure 6. Distribution of scores versus response length.