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，其通常将高效的第一阶段检索器与表达能力更强的模型相结合以优化结果。尽管大型推理模型在以文本为中心的重排序领域推动了快速进展，但基于推理的视频检索重排序方法仍未得到充分探索。为填补这一空白，本文提出RANKVIDEO，一种基于推理的视频检索重排序模型，其通过显式地利用视频内容对查询-视频对进行推理以评估相关性。RANKVIDEO采用两阶段课程学习进行训练：第一阶段为基于感知的监督微调，第二阶段为重排序训练，该阶段结合了点对、对偶以及教师置信度蒸馏目标，并辅以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%，优于纯文本和视觉-语言重排序替代方案，同时保持更高的效率。