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

arXiv: https://arxiv.org/abs/2609.20001 · HTML: https://mlsanity.com/paper/2609.20001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2026.

## Abstract (arXiv)

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.