Listwise reranking is a key yet computationally expensive component in vision-centric retrieval and multimodal retrieval-augmented generation (M-RAG) over long documents. While recent VLM-based rerankers achieve strong accuracy, their practicality is often limited by long visual-token sequences and multi-step autoregressive decoding. We propose ZipRerank, a highly efficient listwise multimodal reranker that directly addresses both bottlenecks. It reduces input length via a lightweight query-image early interaction mechanism and eliminates autoregressive decoding by scoring all candidates in a single forward pass. To enable effective learning, ZipRerank adopts a two-stage training strategy: (i) listwise pretraining on large-scale text data rendered as images, and (ii) multimodal finetuning with VLM-teacher-distilled soft-ranking supervision. Extensive experiments on the MMDocIR benchmark show that ZipRerank matches or surpasses state-of-the-art multimodal rerankers while reducing LLM inference latency by up to an order of magnitude, making it well-suited for latency-sensitive real-world systems. The code is available at https://github.com/dukesun99/ZipRerank.
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）
Self-Calibrated Listwise Reranking with Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；NExT++ Research Centre, National University of Singapore（新加坡国立大学NExT++研究中心）；Baidu Inc.（百度公司）
In this document I present an approach to answer validation and reranking for question answering (QA) systems. A cased-based reasoning (CBR) system judges answer candidates for questions from annotated answer candidates for earlier questions. The promise of this approach is that user feedback will result in improved answers of the QA system, due to the growing case base. In the paper, I present the adequate structuring of the case base and the appropriate selection of relevant similarity measures, in order to solve the answer validation problem. The structural case base is built from annotated…
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Information Engineering, Dalian Ocean University（大连海洋大学信息工程学院）；School of Information Engineering, Liaodong University（辽东学院信息工程学院）；Information Technology Center, Qinghai University（青海大学信息技术中心）；College of Health-Preservation and Wellness, Dalian Medical University（大连医科大学养生与健康学院）；Tencent（腾讯）
Generate to Accelerate: Improved Reranking via LLM-Generated Pivot Documents
Centre for Computational and Data Sciences IIT Kharagpur（印度理工学院卡拉格普尔分校计算与数据科学中心）；Department of Natural Language Processing Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学自然语言处理系）；School of Computing Science University of Glasgow（格拉斯哥大学计算机科学学院）；Department of Computer Science and Engineering IIT Kharagpur（印度理工学院卡拉格普尔分校计算机科学与工程系）
QReason: Query-Focused Decoupled Chain-of-Thought for Efficient Passage Reranking
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京市教育人工智能重点实验室）；Engineering Research Center of Intelligent Technology and Educational ApplicationMinistry of Education（教育部智能技术与教育应用工程研究中心）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Numerous neural retrieval models have been proposed in recent years. These models learn to compute a ranking score between the given query and document. The majority of existing models are trained in pairwise fashion using human-judged labels directly without further calibration. The traditional pairwise schemes can be time-consuming and require pre-defined positive-negative document pairs for training, potentially leading to learning bias due to document distribution mismatch between training and test conditions. Some popular existing listwise schemes rely on the strong pre-defined probabilis…
