Mohamed bin Zayed University of AI（莫扎德·本·扎耶德人工智能大学）；University of Chicago（芝加哥大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Linköping University（林奈大学）
Composed Video Retrieval (CoVR) aims to find a target video given a reference video and a textual modification. Prior work assumes the modification text fully specifies the visual changes, overlooking after-effects and implicit consequences (e.g., motion, state transitions, viewpoint or duration cues) that emerge from the edit. We argue that successful CoVR requires reasoning about these after-effects. We introduce a reasoning-first, zero-shot approach that leverages large multimodal models to (i) infer causal and temporal consequences implied by the edit, and (ii) align the resulting reasoned queries to candidate videos without task-specific finetuning. To evaluate reasoning in CoVR, we also propose CoVR-Reason, a benchmark that pairs each (reference, edit, target) triplet with structured internal reasoning traces and challenging distractors that require predicting after-effects rather than keyword matching. Experiments show that our zero-shot method outperforms strong retrieval baselines on recall at K and particularly excels on implicit-effect subsets. Our automatic and human analysis confirm higher step consistency and effect factuality in our retrieved results. Our findings show that incorporating reasoning into general-purpose multimodal models enables effective CoVR by explicitly accounting for causal and temporal after-effects. This reduces dependence on task-specific supervision, improves generalization to challenging implicit-effect cases, and enhances interpretability of retrieval outcomes. These results point toward a scalable and principled framework for explainable video search. The model, code, and benchmark are available at https://github.com/mbzuai-oryx/CoVR-R.
Beyond Simple Edits: Composed Video Retrieval with Dense Modifications
Mohamed bin Zayed University of AI（马尔代夫比兹赞大学人工智能学院）；University of Central Florida（中央佛罗里达大学）；Linköping University（林霍尔姆大学）；Australian National University（澳大利亚国立大学）
Composed Video Retrieval via Enriched Context and Discriminative Embeddings
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Aalto University（阿尔托大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；University of Central Florida（中佛罗里达大学）
Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R
Southeast University（东南大学）；National University of Singapore（新加坡国立大学）；Independent Researcher（独立研究者）；Opus AI Research（Opus AI研究）；University of Science and Technology of China（中国科学技术大学）
Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning
School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院人工智能安全国家重点实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for Training-Free Zero-Shot Composed Image Retrieval
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft（微软公司）；Minzu University of China（中央民族大学）；University of Adelaide（阿德莱德大学）
ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Southeast University（东南大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；National University of Singapore（新加坡国立大学）；Wuhan AI Research（武汉人工智能研究院）；Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University（广东技术师范大学广东省知识产权大数据重点实验室）
FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval
Louis Vuitton（路易威登）；Inria, École normale supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所, 巴黎高等师范学院, 法国国家科学研究中心, 巴黎文理研究大学）；Courant Institute of Mathematical Sciences and Center for Data Science, New York University（纽约大学库朗数学科学研究所与数据科学中心）
Composed Object Retrieval: Object-level Retrieval via Composed Expressions
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Ministry of Education, Jiangsu, China（新一代人工智能技术及跨学科应用国家重点实验室，东南大学，教育部，江苏，中国）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学（MBZUAI），阿布扎赫德，阿联酋）
DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（计算机网络与信息安全部重点实验室，教育部，山东计算机科学中心（济南国家超算中心），青岛科技大学（山东科学院））；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Artificial Intelligence, Mohamed bin Zayed University（Mohamed bin Zayed大学人工智能学院）
Multimodal Reasoning Agent for Zero-Shot Composed Image Retrieval
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China, Hefei, China（中国科学技术大学信息科学与技术学院）；Sun Yat-sen University Shenzhen Campus, School of Cyber Science and Technology, Shenzhen, China（中山大学深圳校区计算机科学与技术学院）
The Multi-Modal Video Reasoning and Analyzing Competition
Singapore University of Technology and Design（新加坡科技设计大学）；Lancaster University（兰卡斯特大学）；University of Melbourne（墨尔本大学）；Xidian University（西安电子科技大学）；Jiangnan University（江南大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）；Sun Yat-sen University（中山大学）；BOE Technology Group Co., Ltd（京东方科技集团股份有限公司）
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Department of Computer Science, University of California, Los Angeles, USA（加州大学洛杉矶分校计算机科学系）；Sun Yat-sen University Shenzhen Campus, School of Cyber Science and Technology, Shenzhen, China（中山大学深圳校区信息科学与技术学院）
Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval
Beijing University of Posts and Telecommunications（北京邮电大学）；SenseTime（商汤科技）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Intereactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部交互技术与体验系统重点实验室）
MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models
A.I.MATICS Inc（A.I.MATICS公司）；Boston University（波士顿大学）；University of Southern California（南加州大学）；Heuron（Heuron公司）；MODULABS, Open Neural Networks Research Lab（MODULABS，开放神经网络研究实验室）
