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.
Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size. Under such a multi-stage architecture, previous studies mainly focused on optimizing single stage of the framework thus improving the overall retrieval performance. However, how to directly couple multi-stage features for optimization has not been well studied. In this paper, we design Hybrid…
We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We…
University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）；Lanzhou University（兰州大学）；University of Glasgow（格拉斯哥大学）；City University of Hong Kong（香港城市大学）；The Ohio State University（俄亥俄州立大学）
1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval (LeSeR) for Regulatory Question Answering
Pulchowk Campus, IoE（伊奥学院普尔乔克校区）；Cohere For AI Community（Cohere AI社区）；IIT Roorkee（鲁基印度理工学院）；NUST（巴基斯坦国家科技大学）；University of Wisconsin（威斯康星大学）；University of Maryland（马里兰大学）
LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
Faculty of Data Science（数据科学学院）；City University of Macau（澳门城市大学）；Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究所）；Chinese Academy of Sciences（中国科学院）；Southern University of Science and Technology（南方科技大学）；Artificial Intelligence Research Institute（人工智能研究院）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）
PankRAG: Enhancing Graph Retrieval via Globally Aware Query Resolution and Dependency-Aware Reranking Mechanism
College of Computer Science, Beijing university of technology, Beijing, China（北京理工大学计算机科学学院）；Department of Computer Science and Engineering, Bologna University, Bologna, Italy（博洛尼亚大学计算机科学与工程系）
When Should Queries Be Decomposed? A Stage-Aware Study of Query Decomposition for Multi-Condition Retrieval
Eastern Institute of Technology, Ningbo（宁波东方理工学院）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin（浙江省工业智能与数字孪生重点实验室）
University of Mannheim（曼海姆大学）；University of Cambridge（剑桥大学）；University of Würzburg（维尔茨堡大学）
State-of-the-art neural (re)rankers are notoriously data-hungry which -- given the lack of large-scale training data in languages other than English -- makes them rarely used in multilingual and cross-lingual retrieval settings. Current approaches therefore commonly transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all parameters of pretrained massively multilingual Transformers (MMTs, e.g., multilingual BERT) on English relevance judgments, and then deploy them in the target language(s). In this work, we show…
GRADA: Graph-based Reranking against Adversarial Documents Attack
University of Melbourne（墨尔本大学）；University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Macquarie University（麦觉里大学）
