{"ID":2922204,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T21:47:10.793334713Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00910","arxiv_id":"2606.00910","title":"Reason, Retrieve, Re-rank: A Zero-Shot Reasoning-Aware Framework for Composed Video Retrieval","abstract":"Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video. We address the \\emph{Reason-Aware} CoVR (CoVR-R) challenge at the CVPR~2026 VidLLMs workshop, where retrieval is strictly zero-shot. We present \\textbf{R3-CoVR} (\\emph{Reason, Retrieve, Re-rank}), a training-free pipeline built entirely from frozen foundation models. A multimodal large language model (Qwen3-VL-8B) reasons about the \\emph{after-effects} an edit implies -- state transitions, action phases, scene, camera and tempo -- and verbalises a concise post-edit description; a contrastive video--text encoder (SigLIP-2) embeds this description and the gallery for first-stage retrieval; finally a constraint-aware re-ranking stage uses the same multimodal model as a judge that scores each shortlisted candidate against the intended edited result. On the challenge test set, R3-CoVR attains \\textbf{91.9\\% R@1} and \\textbf{98.2\\% R@10}. Two findings drive these results: (i)~matching the description length to the contrastive encoder's text window lifts \\Rk{1} from $67.5$ to $72.7$; and (ii)~the constraint-aware re-ranker, which reorders only the shortlist, lifts \\Rk{1} from $72.7$ to $91.9$ -- the single largest gain. We analyse the re-ranker's behaviour, the retrieve/re-rank blend, and the shortlist depth, and we release a clean three-layer implementation.","short_abstract":"Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video. We address the \\emph{Reason-Aware} CoVR (CoVR-R) challenge at the CVPR~2026 VidLLMs workshop, where retrieval is strictly zero-shot. We present \\textbf{R3-CoVR} (\\emph{Reason, Retriev...","url_abs":"https://arxiv.org/abs/2606.00910","url_pdf":"https://arxiv.org/pdf/2606.00910v1","authors":"[\"Ali Alavi\"]","published":"2026-05-30T22:21:42Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}