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arXiv · 2609.20001

E-AVI: Evidence-Grounded Multimodal Assessment for Automated Video Interviews

Haoshen Wang·Dongbo Che·Zeyi Xie·Yuanjie Du·Shicheng Hua·Xingyu Wang
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Abstract

Automated video interview assessment integrates verbal content, acoustic delivery, and visual behavior, yet numerical predictions alone provide limited inspectable support. We present E-AVI, an evidence-grounded framework that extracts timestamped multimodal evidence and integrates dimension-conditioned evidence attention with source-level embeddings for scoring. A shared evidence pool further supports natural-language feedback and follow-up question answering. On RecruitView and a private hospitality dataset, E-AVI consistently outperforms fine-tuned multimodal baselines in rank correlation. Ablation, evidence-deletion, bootstrap, human-audit, and QA analyses characterize the predictive contribution, grounding, and practical utility of the evidence pathway. Together, these results demonstrate that our proposed E-AVI framework improves predictive performance while providing inspectable support for assessment, feedback, and interactive analysis.

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Haoshen Wang, Dongbo Che, Zeyi Xie, Yuanjie Du, Shicheng Hua, Xingyu Wang. 2026-09-17. E-AVI: Evidence-Grounded Multimodal Assessment for Automated Video Interviews. https://arxiv.org/abs/2609.20001

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