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.

From Intent to Improvement: Agentic Dataset Search for Better Model Training
Oct 29, 2026Qiaosheng Chen, Kaijia Huang, Fanqi Zhou +1Agentic Retrieval Information Retrieval
Nanjing University State Key Laboratory for Novel Software Technology Nanjing, Jiangsu, China
Nothing Left Behind: Embedding Retrieval and Language Model Screening for High Recall Evidence Search
Oct 27, 2026Giuliano Martinelli, Abelardo Carlos Martinez Lorenzo, Satvik Garg +4Evidence Retrieval Information Retrieval
Development Impact AI Lab, World Bank Group
Retriever-Free Retrieval: Navigating Documents with Decision Models
Oct 26, 2026Chao Wan, Paul Jünger, Beixi Du +3Information Retrieval Language Model-Guided Search Heuristics
Department of Computer Science, Cornell University, New York, USA · ASAPP, New York, USA
CRIM: Composable and Reusable Item Memory for Efficient Personalized Retrieval
Oct 26, 2026Haolin Li, Haipeng Zhang, Mang Li +5Information Retrieval Memory-Augmented Language Models
Tsinghua University, Beijing, China · Alibaba International Digital Commerce Group, Beijing, China
CA-HNM: Constraint-Aware Hard Negative Mining for Dense Retrieval with Structured Domain Models
Oct 26, 2026Ngoc Luyen Le, Marie-Hélène Abel, Bertrand LaforgeHard Negative Mining Information Retrieval
Gamaizer, 93340 Le Raincy, France · Université de technologie de Compiègne, CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), CS 60319, 60203 Compiègne Cedex, France · Sorbonne Université, CNRS UMR 7585, LPNHE (Laboratoire de Physique Nucléaire et des Hautes Énergies), 75252 Paris Cedex 05, France
PatentAgentBench: Benchmarking LLM Agents with Professional Patent Task Suites
Oct 26, 2026Yijie Hu, Wei Li, Junjie Zhou +6LLM Agent Evaluation AI Agent Benchmarks
AI Eval & Alignment, PatSnap Ltd. Co.
Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction
Oct 25, 2026Zhe Xu, Tianhao Liao, Zhenqing Ling +3Long-Context Retrieval Information Retrieval
Sun Yat-sen University · Alibaba Group
Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model
Oct 23, 2026Yalda Taheri, Mohammad Hassan Heydari, Armon RasooliText Embedding Models Information Retrieval
Faculty of Engineering, Azad University · Faculty of Computer Engineering, University of Isfahan · Department of Electrical Engineering, Iran University of Science and Technology
Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves
Oct 23, 2026Garima Agrawal, Prasun Das, Akshay Sarvade +9Agentic RAG Information Retrieval
Cognizant, Plano, Texas, USA
System One models Are Not Better Rerankers
Oct 23, 2026Michael Dinzinger, Kanishka Ghosh Dastidar, Michael GranitzerLLM Reranking Information Retrieval
University of Passau, Passau, Germany · Interdisciplinary Transformation University Austria (IT:U), Linz, Austria
Folding Pooled Usage Examples into the Tool Index Lowers Retrieval of Unused Tools
Oct 22, 2026Abhijit ChakrabortyTool Selection for Language Model Agents Tool-Augmented Language Model Agents
Arizona State University
IFC-MCQ: Automatically Generating Multiple-Choice Benchmarks for Evaluating LLM Agents on IFC Data Retrieval
Oct 21, 2026Rabindra Lamsal, Sisi Zlatanova, Xuesong ShenSynthetic Benchmark Generation AI Agent Benchmarks
GRID Lab, School of Built Environment, The University of New South Wales, Sydney, Australia · School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
When PTQ Calibration Helps or Hurts: Dose-Driven Collapse and Validation-Gated Abstention in Quantized Text Embedders
Oct 19, 2026Hyojung Han, Ryangkyung Kang, Youngeun Kim +1Post-Training Quantization Neural Network Quantization
ThakiCloud · Korea University
Conditional information Retrieval for Longitudinal Survey Harmonisation
Oct 19, 2026Wing Yan Li, Zeqiang Wang, Michelle Lin +2Information Retrieval Computational Social Science
University of Surrey · University College London
Cross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature
Oct 19, 2026Seokwon Jung, Yerang Kim, Yejin Kim +5Information Retrieval Scientific Document Retrieval
KAIST · Tübingen AI Center, University of Tübingen · NAVER AI Lab
PACE-Mem: Prefix-Aware Complementary Evidence for Multimodal Agents
Oct 17, 2026Shidu Ren, Rong-cheng Tu, Hang Zhou +1Memory-Augmented Agents Memory-Augmented Video Understanding
Shidu Ren is with the Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada · Rong-cheng Tu is with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 · Hang Zhou is with the Department of Statistics and Operations Research and the School of Data and Information Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA +1
IRAH: Hierarchical Agentic Tree Search for Retrieval Pipeline Optimization
Oct 16, 2026Maria Ana Cardei, Aniket Singh, Amir Moghadam +2Information Retrieval Hyperparameter Optimization
University of Virginia · University of California San Diego · Capital One
SAG2: Embedding-Free Multi-Hop Retrieval with BM25 and JEV
Oct 15, 2026Yuchao WuMulti-Hop Retrieval Information Retrieval
Zleap AI
Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
Oct 15, 2026Anikait Singh, Teresa Zhang, Yoonho Lee +3Agentic RAG Memory-Augmented Language Model Agents
Stanford University · Carnegie Mellon University
Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search
Oct 14, 2026Diana Turmakhan, Aseem Srivastava, Vivek Gupta +2Information Retrieval Visual Document Retrieval
Mohamed bin Zayed University of Artificial Intelligence · Arizona State University · Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science
Beyond Black Boxes: Visual Grounding for Text Embeddings
Oct 13, 2026Slimane LarabiInformation Retrieval Text Embedding Models
Computer Science Faculty, USTHB University
Web Scale on a Budget: Tail-Aware Retrieval Evaluation
Oct 13, 2026Boaz Meivar, Shai AvidanIR Evaluation Information Retrieval
Tel Aviv University
RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval
Oct 13, 2026Yuchen Hui, Fengran Mo, Jia Ao Sun +3Information Retrieval Conversational Search
RALI, Université de Montréal Montréal, Québec, Canada
From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction
Oct 12, 2026Tomasz Gniazdowski, Mohammadreza Momenifar, Nadia Vase +6Information Retrieval Knowledge Distillation
eBay Tooploox Wroclaw, Poland · eBay San Jose, USA · eBay Aachen, Germany
Local Citation Recommendation Without Graph Signals
Oct 11, 2026Benjamin Basseri, Pavlos Protopapas, Kemeng Zhang +3Information Retrieval Recommender Systems
Harvard University · Center for Astrophysics | Harvard & Smithsonian
Recall What Is Missing: Composing Evidence from Long-Term Agent Memory
Oct 11, 2026Qi Feng, Jicong FanLong-Term Conversational Memory Information Retrieval
School of Data Science, The Chinese University of Hong Kong, Shenzhen
What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Oct 10, 2026Duo Li, Haojun Pan, Zuhao Yang +4Multimodal Embedding Multimodal Contrastive Learning
CCDS, Nanyang Technological University · Alibaba Group · Universität Hamburg +1
UltraBERT: Beyond the Limit of BERT Pre-Training
Oct 10, 2026Jungwoo Park, Taewhoo Lee, Jaewoo KangLanguage Model Pretraining Masked Language Modeling
Korea University · AIGEN Sciences · Amazon
Self-improving data synthesis enables scalable adaptation of embedding models
Oct 10, 2026Zucan Lyu, Rui Tang, Jianlyu Chen +2Synthetic Data Generation Information Retrieval
University of Science and Technology of China, Hefei, China. · State Key Laboratory of Cognitive Intelligence, Hefei, China.
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval
Oct 10, 2026Zhisheng Qi, Haoyu Han, Jiaxi Li +9Information Retrieval Adaptive Retrieval
University of Georgia · Michigan State University · Georgia Institute of Technology +4

Oct 29, 2026J/K move · Enter open · S save

From Intent to Improvement: Agentic Dataset Search for Better Model Training

Qiaosheng Chen, Kaijia Huang, Fanqi Zhou, Gong Cheng

Nanjing University State Key Laboratory for Novel Software Technology Nanjing, Jiangsu, China

Agentic Retrieval Information Retrieval

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Finding training data in million-scale machine learning repositories is a Web search problem where text relevance is not enough. A query couples a semantic training goal with constraints such as task, license, and training stage, and the metadata that would verify them are often missing. Text retrieval ranks dataset cards by similarity but cannot enforce hard constraints or follow the relations that reach a sparsely described dataset. Free-form tool-calling agents are more flexible, but the same model that picks a dataset also declares it a match, and nothing checks that the query's constraints are actually met. Neither approach establishes that the data it returns will improve model training. To address it, we propose AgenticDS, a contract-governed agentic framework in which every language model decision instantiates a typed object before deterministic code consumes it. The framework compiles a query into a typed search plan, executes it through admissible deterministic operators over hybrid retrieval and bounded graph expansion, and verifies the provenance-grounded evidence before accepting a ranking or emitting a typed diagnosis that permits only local repairs. On HuDS-Bench, a new benchmark of 200 queries with 24,908 graded judgments pooled over 15 systems, AgenticDS improves NDCG@10 by 25.7% over the strongest dense--sparse hybrid and by 18.9% over a commercial LLM agent. A controlled search-to-train study further shows that the datasets it retrieves give the best downstream accuracy in mathematics, code, and medicine. Code and data are publicly available at https://github.com/nju-websoft/AgenticDS.