# PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment

arXiv: https://arxiv.org/abs/2609.02231 · HTML: https://mlsanity.com/paper/2609.02231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 66 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 30 of 37)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs](https://mlsanity.com/paper/2406.07476/) (2024)
- [VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding](https://mlsanity.com/paper/2501.13106/) (2025)
- [VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models](https://mlsanity.com/paper/2407.11691/) (2024)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2504.06958/) (2025)
- [Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding](https://mlsanity.com/paper/2409.14485/) (2024)
- [SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models](https://mlsanity.com/paper/2407.15841/) (2024)
- [VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning](https://mlsanity.com/paper/2505.12434/) (2025)
- [CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation](https://mlsanity.com/paper/2311.18702/) (2023)
- [Neptune: The Long Orbit to Benchmarking Long Video Understanding](https://mlsanity.com/paper/2412.09582/) (2024)
- [Automated Analysis and Prediction of Job Interview Performance](https://mlsanity.com/paper/1504.03425/) (2015)
- [Multimodal Multi-Agent Empowered Legal Judgment Prediction](https://mlsanity.com/paper/2601.12815/) (2026)
- [Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features](https://mlsanity.com/paper/1610.10048/) (2016)
- [An Open-source Benchmark of Deep Learning Models for Audio-visual Apparent and Self-reported Personality Recognition](https://mlsanity.com/paper/2210.09138/) (2022)
- [Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning](https://mlsanity.com/paper/2506.07227/) (2025)
- [How Order-Sensitive Are LLMs? OrderProbe for Deterministic Structural Reconstruction](https://mlsanity.com/paper/2601.08626/) (2026)
- [Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos](https://mlsanity.com/paper/1802.00745/) (2018)
- [MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning](https://mlsanity.com/paper/2509.21113/) (2025)
- [HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews](https://mlsanity.com/paper/1907.11062/) (2019)
- [SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering](https://mlsanity.com/paper/2606.00593/) (2026)
- [Grading video interviews with fairness considerations](https://mlsanity.com/paper/2007.05461/) (2020)
- [Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset](https://mlsanity.com/paper/2503.12912/) (2025)
- [AI for human assessment: What do professional assessors need?](https://mlsanity.com/paper/2204.08471/) (2022)
- [OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis](https://mlsanity.com/paper/2605.27378/) (2026)
- [To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments](https://mlsanity.com/paper/1911.13248/) (2019)

## Built on by (most cited first, 1 of 1)

- [MetaRubric: Learning to Reward for Rubric-Based Reinforcement Learning](https://mlsanity.com/paper/2610.02824/) (2026)

## Abstract (arXiv)

Interview assessment requires per-criterion judgments grounded in behavioral evidence, yet surging applicant volumes have made human-only evaluation costly and inconsistent, while existing AI approaches yield opaque scores without traceable rationale. We introduce PhoenixNest-Video, an evidence-grounded multimodal agent framework for automated video interview assessment. It builds a semantic video graph as structured working memory, performs rubric-conditioned retrieval with cross-modal verification across visual, audio, and textual streams, and produces per-criterion scores anchored to the candidate's materials. A Scorer trained via Rubrics-based Reinforcement Learning with dual rewards for rubric alignment and score-level differentiation internalizes the discriminative structure of multi-level rubrics. PhoenixNest-Video attains 91.50% grade-level accuracy on VInterview-2025, outperforming substantially larger proprietary models. A compact, rubric-grounded agent therefore scores candidates in closer agreement with an expert panel than direct prompting of much larger models, and exposes the evidence behind each score for human review.