Reason, Retrieve, Re-rank: A Zero-Shot Reasoning-Aware Framework for Composed Video Retrieval
arXiv:2606.00910v1 Announce Type: cross 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
The proliferation of advanced foundation models and large language models (LLMs) enables more complex zero-shot reasoning capabilities for multimodal tasks like video retrieval.
This development pushes the boundaries of zero-shot multimodal intelligence, enabling more intuitive and powerful human-computer interaction for content search and generation, reducing the need for costly labeled data.
Video retrieval systems can now understand and respond to nuanced, free-form textual modifications and 'after-effects' without prior training on such specific queries, improving content accessibility and utility.
- · AI researchers
- · Content platforms
- · Video production studios
- · Foundation model developers
- · Traditional video indexing services
- · Data labeling companies (for specific retrieval tasks)
More sophisticated and nuanced video search capabilities will emerge for end-users and professional applications.
This could lead to new forms of video editing and content creation where textual prompts directly influence highly specific visual outcomes.
Enhanced video understanding could accelerate the development of agentic systems that perceive and interact with digital media environments more intelligently.
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Read at arXiv cs.LG