Information Retrieval

Also known as IR

Information retrieval develops methods for finding useful documents, passages, products, tables, code, and multimedia in response to a user's query. Systems range from lexical methods such as BM25 and learned sparse retrieval to dense embeddings, late-interaction models, hybrid ranking, and graph-based or agentic search. These methods support applications including web search, recommendation, and retrieval-augmented generation, where retrieved evidence must be both relevant and sufficient for the task. A central challenge is balancing accuracy with speed and cost: semantic similarity can miss structured, compositional, or domain-specific evidence, and strong retrieval scores do not always lead to useful answers.

Latest papers

476 papers61 papers in the last four weeks, up 36% on the four weeks before. 0.4% of all new papers.

Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needs
Feb 16, 2026Christos KoutsiarisDocument Chunking Information Retrieval
Dept. of Computer Science & Information Systems University of Limerick, Ireland · Cloud ERP - UX Foundation SAP P&E
BETA-Labeling for Multilingual Dataset Construction in Low-Resource IR
Feb 16, 2026Md. Najib Hasan, Mst. Jannatun Ferdous Rain, Fyad Mohammed +1Low-Resource Language Processing LLM-Assisted Annotation
Wichita State University · Begum Rokeya University, Rangpur · Khulna University of Engineering & Technology +1
High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace
Feb 16, 2026Dillon Davis, Huiji Gao, Thomas Legrand +8E-Commerce Search Information Retrieval
Relevance Airbnb, Inc. San Francisco, CA, USA · Search Airbnb, Inc. San Francisco, CA, USA
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
Feb 11, 2026Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon +8Contrastive Learning Information Retrieval
Seoul National University of Science and Technology Seoul, Republic of Korea · LinqAlpha Cambridge, MA, United States · Massachusetts Institute of Technology Cambridge, MA, United States +7
ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge
Feb 10, 2026Yijie Lin, Guofeng Ding, Haochen Zhou +3Multimodal IR Visual Spatial Reasoning
College of Computer Science, Sichuan University, Chengdu, China · National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University, China
With Argus Eyes: Assessing Retrieval Gaps via Uncertainty Scoring to Detect and Remedy Retrieval Blind Spots
Feb 10, 2026Zeinab Sadat Taghavi, Ali Modarressi, Hinrich Schutze +1Retrieval-Augmented Generation Retrieval Robustness
Lucerne University of Applied Sciences and Arts (HSLU) · Center for Information and Language Processing (CIS), Ludwig Maximilian University of Munich (LMU) · Munich Center for Machine Learning (MCML)
Impact of AI Search Summaries on Website Traffic: Evidence from Google AI Overviews and Wikipedia
Feb 5, 2026Mehrzad Khosravi, Hema YoganarasimhanInformation Retrieval
University of Washington
ShardMemo: Scope-Before-Routing for Agentic Memory Retrieval
Jan 29, 2026Yang Zhao, Chengxiao Dai, Mengying Kou +2Agentic Retrieval Information Retrieval
Anonymous
The $\mathbf{P}$ -Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs
Jan 26, 2026Amir AavaniQuery Complexity Information Retrieval
Apple Inc., USA
NewsRECON: News Article Retrieval for Image Contextualization
Jan 20, 2026Jonathan Tonglet, Iryna Gurevych, Tinne Tuytelaars +1Multimodal IR Digital Image Forensics
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE · Department of Electrical Engineering, KU Leuven · Department of Computer Science, KU Leuven
CORE-T: COherent REtrieval of Tables for Text-to-SQL
Jan 19, 2026Hassan Soliman, Vivek Gupta, Dan Roth +1Text-to-SQL Information Retrieval
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany · Arizona State University · University of Pennsylvania
PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization
Jan 15, 2026Tingyue Pan, Jie Ouyang, Mingyue Cheng +7Agentic RL Policy Optimization
State Key Lab of Cognitive Intelligence, University of Science and Technology of China
MTRACE: Multilingual Retrieval-Augmented Generation for Temporally Diverse Text Corpora
Dec 14, 2025Souhail Bakkali, Anthony MudetRetrieval-Augmented Generation Multilingual IR
Univ Rennes, CNRS, IRISA - UMR 6074, Rennes, France · L3i-lab, La Rochelle Universit´e, France
From Crawled to Published: A Publisher-Controlled Web Architecture for an AI-Native Internet
Nov 23, 2025Yixi Chen, Muhammad Bilal, Tie Ma +3Information Retrieval Agentic Retrieval
PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
Nov 18, 2025Chun Chet Ng, Jia Yu Lim, Zhen Hao Chu +2Information Retrieval LLM Reranking
AI Lens, Kuala Lumpur, Malaysia
Rethinking On-policy Optimization for Query Augmentation
Oct 20, 2025Zhichao Xu, Shengyao Zhuang, Xueguang Ma +6RL for Language Models LLM Prompting
University of Utah · 2The University of Queensland · University of Waterloo +5
TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
Oct 9, 2025Jianhui Yang, Yiming Jin, Pengkun Jiao +6RL for Language Models Reward Shaping
Tsinghua University Beijing, China · Taobao & Tmall Group of Alibaba Beijing, China · Taobao & Tmall Group of Alibaba Hangzhou, China +1
How Do LLM-Generated Texts Impact Term-Based Retrieval Models?
Aug 25, 2025Wei Huang, Keping Bi, Yinqiong Cai +3Information Retrieval Lexical Retrieval
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China · Baidu Inc., Beijing, China
ChEmbed: Enhancing Chemical Literature Search Through Domain-Specific Text Embeddings
Aug 3, 2025Ali Shiraee Kasmaee, Mohammad Khodadad, Mahdi Astaraki +4Text Embedding Evaluation Information Retrieval
Department of Computational Science and Engineering, McMaster University, Canada · BASF Canada Inc., Canada · BASF Corporation, USA
Open-Attribute Person Retrieval: Finding People Through Distinctive and Novel Attributes
Aug 2, 2025Minjeong Park, Hongbeen Park, Sangwon Lee +1Information Retrieval Image Retrieval
The Pennsylvania State University · Korea Telecom Research · Korea University +1
Chain of Retrieval: Multi-Aspect Iterative Search Expansion and Post-Order Search Aggregation for Full Paper Retrieval
Jul 14, 2025Sangwoo Park, Jinheon Baek, Soyeong Jeong +1Scientific Document Retrieval Information Retrieval
KAIST
Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation
Jun 13, 2025Mahmoud Amiri, Thomas BocklitzDocument Chunking Text Embedding Evaluation
Leibniz Institute of Photonic Technology, Albert-Einstein-Strasse 9, 07745 Jena, Germany · Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
Query Brand Entity Linking in E-Commerce Search
Feb 3, 2025Dong Liu, Sreyashi NagE-Commerce Search Information Retrieval
Amazon Luxembourg, Luxembourg · Amazon Palo Alto, California, United States
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Feb 1, 2024Xinyue Chen, Pengyu Gao, Jiangjiang Song +1Retrieval-Augmented Generation Multi-Document QA
1Nanjing University of Aeronautics and Astronautics. · 2Southeast University, Nanjing, Jiangsu, China.
Research on Domain Information Mining and Theme Evolution of Scientific Papers
Apr 18, 2022Changwei Zheng, Zhe Xue, Meiyu Liang +2Representation Learning Topic Modeling
School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications Beijing, China
Retrieval of Scientific and Technological Resources for Experts and Scholars
Apr 13, 2022Suyu Ouyang, Yingxia Shao, Ang LiInformation Retrieval
School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China

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Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needs

Christos Koutsiaris

Dept. of Computer Science & Information Systems University of Limerick, Ireland · Cloud ERP - UX Foundation SAP P&E

Document Chunking Information Retrieval

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Breaking long documents into smaller segments is a fundamental challenge in information retrieval. Whether for search engines, question-answering systems, or retrieval-augmented generation (RAG), effective segmentation determines how well systems can locate and return relevant information. However, traditional methods, such as fixed-length or coherence-based segmentation, ignore user intent, leading to chunks that split answers or contain irrelevant noise. We introduce Intent-Driven Dynamic Chunking (IDC), a novel approach that uses predicted user queries to guide document segmentation. IDC leverages a Large Language Model to generate likely user intents for a document and then employs a dynamic programming algorithm to find the globally optimal chunk boundaries. This represents a novel application of DP to intent-aware segmentation that avoids greedy pitfalls. We evaluated IDC on six diverse question-answering datasets, including news articles, Wikipedia, academic papers, and technical documentation. IDC outperformed traditional chunking strategies on five datasets, improving top-1 retrieval accuracy by 5% to 67%, and matched the best baseline on the sixth. Additionally, IDC produced 40-60% fewer chunks than baseline methods while achieving 93-100% answer coverage. These results demonstrate that aligning document structure with anticipated information needs significantly boosts retrieval performance, particularly for long and heterogeneous documents.