Agentic RAG

RAG: Retrieval-Augmented Generation

Agentic RAG studies systems where a language model actively searches, inspects evidence, and decides whether to retrieve again before answering, rather than relying on a single retrieval pass. Common approaches include query decomposition, tool-using or multi-agent workflows, graph and hierarchical retrieval, and reinforcement learning that rewards useful evidence gathering and efficient stopping. These methods are tested on multi-hop question answering, long and multimodal documents, and domain-specific tasks such as clinical, legal, and financial reasoning. A central challenge is balancing answer quality with retrieval cost and latency while ensuring that evidence is sufficient, faithfully used, and not undermined by errors that accumulate across steps.

Latest papers

246 papers24 papers in the last four weeks, up 14% on the four weeks before. 0.1% of all new papers.

RouteDRAG: Learning to Route and Control Agentic Execution for Multimodal Document RAG
Oct 27, 2026Zekai Chen, Hao Zhu, Yu Liu +5Agentic RAG Multimodal RAG
Beijing Institute of Technology, Beijing, China · Chinese Academy of Sciences, China · Zhejiang University, Hangzhou, China +3
LM-DSB: A Lightweight Multi-agent System for Automated Daily Safety Bulletin Generation in Construction
Oct 27, 2026Xianxiang Zhao, Advik Mehta, Falak Sethi +3Construction Safety Agentic RAG
Department of Construction Management, University of Washington, United States · PCL Industrial Management Inc, Canada · Department of Computer Science, University of Alberta, Canada +1
OA-RAG: Ontology-based Agentic RAG for Faithful LLM Reasoning
Oct 26, 2026Hui Yang, Jiaoyan Chen, Yongsheng GaoAgentic RAG Retrieval-Augmented Generation
The University of Manchester, Manchester, United Kingdom · SNOMED International, London, United Kingdom
Adaptive Retrieval, Selective Answers: Evaluating Conformal Factuality in Agentic RAG
Oct 26, 2026Muhammad Aimal Rehman, Chi-Kuang YehRAG Evaluation Agentic RAG
Department of Mathematics & Statistics, Georgia State University, Atlanta, GA, USA
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
The LLENS Platform for On-Premises Large Language Model Agents in a Japanese University Hospital: Development and Evaluation Study
Oct 23, 2026Ken Enda, Jien Saito, Masami Mukai +12LLM Agents LLM Agent Harnesses
Division of AI Support for Medical Research, Faculty of Medicine, Hokkaido University, Sapporo, Japan · Department of Cancer Pathology, Faculty of Medicine, Hokkaido University, Sapporo, Japan · Technoplasm Inc., Sapporo, Japan +5
Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy
Oct 19, 2026Ke Zhang, Sahchit Chundur, Jerry Peprah Owusu +2LLM Tool Use LLM Agent Reliability
University of California, Riverside · University of California, Irvine
Toward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources
Oct 18, 2026Jeonghwan Choi, Jibin Hwang, Hayoon Park +2Agentic RAG RAG Evaluation
Korea Advanced Institute of Science and Technology (KAIST) · GS Retail · Cluvion
MuninAI: A Self-Hosted Agentic Framework for Independent Research Groups
Oct 16, 2026Markus Fischer, Jens Meiler, Matthias ElgetiScientific QA Agentic RAG
Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany · Institute for Medical Physics and Biophysics, Leipzig University Medical School, Leipzig, Germany · Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany +3
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
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
Oct 11, 2026Xiangqing Lu, Tao Zhu, Liang Yu +6Human Activity Recognition Retrieval-Augmented Classification
School of Computer Science, University of South China, Hengyang, China · School of Computer Science and Technology, Dalian University of Technology, Dalian, China · Hunan University, Changsha, China +1
Frustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support
Oct 10, 2026Istok Bako, Bonan Min, Tiffany Shao +2Agentic RAG Tool-Augmented Language Model Agents
Airbnb, Inc., USA
Locate, Judge, Read: Role-Specialized Tool Agents over a Knowledge-Point Index for Open-Domain Multimodal Document QA
Oct 9, 2026Wenlin Lin, Fanzhe WeiDocument QA Agentic RAG
Metask Lab
LongHarness: Advancing Agentic Reasoning over 100M-Token Corpora via Stateful Evidence Management
Oct 9, 2026Songtao Huang, Zhijie Zhong, Xiaohan He +8LLM Agent Harnesses Long-Context Inference
Fudan University · Shanghai Artificial Intelligence Laboratory · Shanghai Jiaotong University +3
Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System
Oct 8, 2026Irene WeberLLM Agents LLM Agent Orchestration
University of Applied Sciences Kempten, Germany
RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
Oct 8, 2026Meghanadh Pulivarthi, Swaraj Kumar Biswal, Kushagra Bhushan +3Retrieval-Augmented Generation Structured RAG
IBM · IIT Kharagpur
From Retrieval to Reconstruction: Constructing Evolvable Cognitive Memory for Long-Term Dialogue
Oct 8, 2026Zirui Liao, Zhengxian Wu, Zhuohong Chen +4Long-Term Conversational Memory Agentic RAG
Tsinghua University Shenzhen International Graduate School, Shenzhen, China
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Oct 7, 2026Yilun Hao, Krishna Sayana, Isabella Ye +4Retrieval-Augmented Generation Agentic RAG
MIT · Google Research
From Retrieval to Customer Context: Evaluating Frontier-Model Systems for Voice-of-Customer Analysis
Oct 7, 2026Raviraja G, Viraj Bagal, Prabhath ChellingiData Analysis Agents LLM Agent Evaluation
Enterpret
Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
Oct 6, 2026Lasse B. Strand, Robert Jakob, Kevin O'Sullivan +1Retrieval-Augmented Generation Large Language Model-Guided Optimization
ETH Zurich
Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation
Oct 2, 2026Oliver Hauck, Mario Sanz-Guerrero, Katharina von der WenseCross-Lingual Reasoning in Language Models Retrieval-Augmented Reasoning
Johannes Gutenberg University Mainz, Germany
LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence
Oct 1, 2026Xiaoxia Cheng, Linnan Wang, Jiahao Ma +5Multi-Agent LLM Systems Legal IR
Anhui University · Tsinghua University
BELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence
Sep 30, 2026Hongji PuAdaptive RAG Evidence Selection
University of Illinois Urbana-Champaign
BITEM at the NTCIR-19 R2C2 Task: Predicting Confidence from Agentic RAG Pipeline Signals
Sep 29, 2026Julien Knafou, Luc Mottin, Alexandre Flament +3Retrieval-Augmented Generation Agentic RAG
HES-SO, Geneva, Switzerland · SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
Complementary Retrieval-Augmented Prompting for Consistent Long-Form Video Generation
Sep 29, 2026Xianghan Wei, Xiaoda Yang, Zhi Wang +7Long Video Generation Agentic RAG
BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning
Sep 29, 2026Quan Xiao, Mingda Liu, Gaowen Liu +2Reinforcement Learning Agentic RAG
Department of Electrical and Computer Engineering, Cornell University · Institute of Integrated Research, Institute of Science Tokyo · Cisco Research
ARCagent: An Adaptive Retrieval Calibration Agent for Clinical Question Answering
Sep 28, 2026Yuyan ChenClinical QA Agentic RAG
Just-In-Time Agent Memory with Runtime Agentic Research
Sep 28, 2026Bingyu Yan, Chaofan Li, Hongjin Qian +3Agent Memory Agentic RAG
Beijing Academy of Artificial Intelligence · Peking University · Hong Kong Polytechnic University
Evidence-Inference Reconstruction: When The Evidence Is Recalled But The Reasoning Goes Wrong
Sep 27, 2026Megan Diehl, Ser-Nam LimMulti-Hop QA Evidence-Grounded Reasoning
University of Central Florida
Backdoor in the Loop: Compromising Agentic Search via Malicious Retrievers
Sep 26, 2026Beining Xu, Peichun Hua, Yunming XiaoBackdoor Attacks RAG Security
Shenzhen MSU-BIT University · The Chinese University of Hong Kong, Shenzhen

Oct 27, 2026J/K move · Enter open · S save

RouteDRAG: Learning to Route and Control Agentic Execution for Multimodal Document RAG

Zekai Chen, Hao Zhu, Yu Liu, Xunkai Li+4

Beijing Institute of Technology, Beijing, China · Chinese Academy of Sciences, China · Zhejiang University, Hangzhou, China+3

Agentic RAG Multimodal RAG LLM Agent Workflow Optimization Multimodal QA Graph Retrieval-Augmented Generation

Open paper AskPDF arXiv

Web-based applications increasingly rely on understanding long multimodal documents, where answering questions often requires combining information across pages, tables, and figures. Agentic retrieval-augmented generation addresses these tasks through iterative retrieval and reasoning. However, Agentic execution is not always necessary, introducing additional computation and potentially incorrect answers. Our empirical analysis reveals that Agentic execution provides query-dependent benefits, while additional operations may improve, preserve, or degrade answer accuracy. These findings motivate controlling both Agent invocation and subsequent execution. To address these challenges, we propose RouteDRAG, a learning-based framework comprising four components. Unified Document Index organizes multimodal content into a graph-backed index and derives query representations from initial retrieval. Adaptive Path Routing preserves exact answering and predicts the relative benefit of Agent over shorter execution paths. Dynamic Agent Execution maintains an evolving workspace and refines Planner proposals to control tool use and termination. Sequential Policy Learning first trains operation control from observed outcomes and then trains path routing using the resulting Agent's performance. Across two benchmarks, RouteDRAG achieves an average accuracy improvement of 3.47 percentage points over shorter-path-only execution while assigning only 79.3% of queries to Agent. Notably, it also surpasses uniform Agent execution on both benchmarks while requiring fewer answering calls. Our code is now available at https://github.com/Ereshkigal1/RouteDRAG.