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. These results demonstrate that PhoenixNest-Video delivers stable, expert-aligned scoring across candidates, providing a reliable framework for transparent and auditable interview assessment.