Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large reasoning models have driven rapid progress in text-centric reranking, reasoning-based reranking for video retrieval remains underexplored. To address this gap, we introduce RANKVIDEO, a reasoning-based reranker for video retrieval that explicitly reasons over query-video pairs using video content to assess relevance. RANKVIDEO is trained using a two-stage curriculum consisting of perception-grounded supervised fine-tuning followed by reranking training that combines pointwise, pairwise, and teacher confidence distillation objectives, and is supported by a data synthesis pipeline for constructing reasoning-intensive query-video pairs. Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework, yielding an average improvement of 31% on nDCG@10 and outperforming text-only and vision-language reranking alternatives, while more efficient.

翻译：重排序是现代检索系统的关键组成部分，其通常将高效的一阶段检索器与更具表达能力的模型相结合以优化结果。尽管大型推理模型已在以文本为中心的重排序领域推动快速进展，但面向视频检索的基于推理的重排序仍未得到充分探索。为填补这一空白，我们提出了RANKVIDEO，一种面向视频检索的基于推理的重排序器，其利用视频内容对查询-视频对进行显式推理以评估相关性。RANKVIDEO采用两阶段课程进行训练，包括基于感知的监督微调，以及结合了点态、对态和教师置信度蒸馏目标的重排序训练，并辅以一个用于构建推理密集型查询-视频对的数据合成流程。在大规模MultiVENT 2.0基准上的实验表明，RANKVIDEO在双阶段框架内持续提升检索性能，在nDCG@10指标上平均提升31%，并优于纯文本及视觉-语言重排序替代方案，同时保持更高的效率。