In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.
Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval
Dalian University of Technology（大连理工大学）；City University of Hong Kong（香港城市大学）；Huawei Technologies Ltd.（华为技术有限公司）；University of Science and Technology of China（中国科学技术大学）
IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memory
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；School of Engineering, Westlake University（西湖大学工学院）；State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人机混合增强智能全国重点实验室、视觉信息与应用国家工程研究中心、人工智能与机器人研究所）
Modelling-based experiment retrieval: A case study with gene expression clustering
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所（HIIT））；Aalto University（阿尔托大学）；European Molecular Biology Laboratory（欧洲分子生物学实验室）；European Bioinformatics Institute（欧洲生物信息研究所）
Long Context Modeling with Ranked Memory-Augmented Retrieval
University of New South Wales（新南威尔士大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Southampton（南安普顿大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
State Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室）；Alibaba Group, Hangzhou, China（阿里巴巴集团，杭州，中国）；National Institute of Healthcare Data Science, Nanjing University, China（南京大学健康数据科学国家研究院）
Contrastive Quantization with Code Memory for Unsupervised Image Retrieval
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学清华-深圳国际研究生院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）
