Agentic RAG

RAG: Retrieval-Augmented Generation

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15 papers in the last four weeks, up 88% on the four weeks before. 0.1% of all new papers.

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LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG
May 7, 2026Yijia Zheng, Marcel WorringLLM Inference Acceleration Agentic RAG
University of Amsterdam, Amsterdam, the Netherlands
Architecture Matters: Comparing RAG Systems under Knowledge Base Poisoning
May 7, 2026Samuel KornRetrieval-Augmented Generation RAG Poisoning Attacks
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
May 7, 2026Susheel Suresh, Hazel Mak, Shangpo Chou +2Tool-Augmented Language Model Agents Agentic Search
Microsoft Corporation
Agentic Retrieval-Augmented Generation for Financial Document Question Answering
May 6, 2026Yang Shu, Yingmin Liu, Zequn XieFinancial QA Agentic RAG
College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
Architectural Constraints Alignment in AI-assisted, Platform-based Service Development
May 6, 2026Julius Irion, Moritz Leugers, Paul Hartwig +5Software Engineering Code Generation
1Technische Universität Berlin, Germany · 2Information Systems Engineering, Technische Universität Berlin, Germany
Career-Aware Resume Tailoring via Multi-Source Retrieval-Augmented Generation with Provenance Tracking: A Case Study
May 6, 2026Kumar AbhinavRetrieval-Augmented Generation Agentic RAG
An Agent-Oriented Pluggable Experience-RAG Skill for Experience-Driven Retrieval Strategy Orchestration
May 5, 2026Dutao Zhang, Tian LiaoAdaptive Retrieval Agentic Retrieval
Macao Polytechnic University
DocSync: Agentic Documentation Maintenance via Critic-Guided Reflexion
May 4, 2026Sidhesh Badrinarayan, Adithya ParthasarathySoftware Engineering Agentic RAG
California, USA
Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization
May 3, 2026Weihang Su, Xuanyi Chen, Yueyue Wu +2Legal Document Analysis Agentic RAG
Tsinghua University · Beijing, China · Quan Cheng Laboratory +1
MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation
May 2, 2026Qinchuan Cheng, Jiaqi Liu, Ruixuan Xie +2Adaptive Retrieval Agentic RAG
1Xi’an Jiaotong University · 2Sichuan University · 3Southwestern University of Finance and Economics +1
Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning
May 2, 2026Sangkwon Park, Donghun Kang, Jisoo Mok +1Retrieval-Augmented Generation LLM Reranking
Department of Electrical and Computer Engineering, Seoul National University · DGIST · Interdisciplinary Program in Artificial Intelligence, Seoul National University +1
$S^3$ -R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
May 2, 2026Harsh Goel, Akhil Udathu, Susmija Jabbireddy +2Multi-Hop QA Synthetic Data Generation
The University of Texas at Austin · Work done during an internship at Google DeepMind · Google DeepMind
LLM-Oriented Information Retrieval: A Denoising-First Perspective
May 1, 2026Lu Dai, Liang Sun, Fanpu Cao +4Agentic Retrieval Agentic RAG
Hong Kong University of Science and Technology · Hong Kong University of Science and Technology (Guangzhou)
ARIS: Agentic and Relationship Intelligence System for Social Robots
May 1, 2026Stavya Datta, Fucai Ke, Leimin Tian +1Agentic RAG Human-Robot Interaction
Monash University
Agentic AI for Substance Use Education: Integrating Regulatory and Scientific Knowledge Sources
May 1, 2026Kosar Haghani, Zahra Kolagar, Mohammed AtiquzzamanAI Agent Evaluation Generative AI in Education
Department of Social Science, Texas Woman's University, Denton, Texas, USA. · Department of Computer Science, Augsburg University of Applied Sciences, Augsburg, Germany · School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA
Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning
Apr 30, 2026Wilder Baldwin, Sepideh GhanavatiKG Construction Agentic RAG
University of Maine
CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG
Apr 28, 2026Nayeon Lee, Jiwoo Song, Byeongcheol KangRetrieval-Augmented Generation Adaptive RAG
Naver · Samsung Research
SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition
Apr 22, 2026Jielong Tang, Xujie Yuan, Jiayang Liu +6Multimodal Grounding Named Entity Recognition
Sun Yat-sen University Zhuhai, China · Shandong Normal University Jinan, China · University of the Chinese Academy of Sciences Beijing, China +2
NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
Apr 21, 2026Quentin Fever, Naziha AslamMulti-Agent LLM Systems Grounded Text Generation
Team NightFeats
Semantic Entanglement in Vector-Based Retrieval: A Formal Framework and Context-Conditioned Disentanglement Pipeline for Agentic RAG Systems
Apr 20, 2026Nick LoghmaniDocument Chunking Text Embeddings
Salesforce · School of Information Studies, Syracuse University
STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
Apr 19, 2026Wei Chen, Lili Zhao, Zhi Zheng +2Multi-Hop QA Self-Training for Language Models
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei, Anhui, China · NIO Hefei, Anhui, China
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
Apr 19, 2026Jingbo Sun, Wenyue Chong, Songjun Tu +7Agentic Retrieval Agentic RAG
SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +1
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
Apr 17, 2026Yi Lin, Yihao Ding, Yonghui Wu +1Radiology Report Generation Multi-Agent Collaboration
Weill Cornell Medicine, New York, USA · University of Western Australia, Crawley, Australia · University of Florida, Florida, USA
Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
Apr 16, 2026Gongbo Zhang, Yifan Peng, Chunhua WengLLM Self-Correction Retrieval-Augmented Generation
Columbia University · Weill Cornell Medicine
Toward Agentic RAG for Ukrainian
Apr 16, 2026Marta Sumyk, Oleksandr KosovanAgentic RAG Document QA
Ukrainian Catholic University, Lviv, Ukraine
Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis
Apr 15, 2026Arthur Capozzi, Dirk HelbingKG Construction Entity Resolution
Computational Social Science, ETH Zurich, Zurich, Switzerland · Complexity Science Hub, Vienna, Austria
Learning to Search: A Decision-Based Agent for Knowledge-Based Visual Question Answering
Apr 8, 2026Zhuohong Chen, Zhenxian Wu, Yunyao Yu +6Visual Question Answering Tool Use in VLMs
Tsinghua University · Hefei University of Technology · University of Arizona
Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
Feb 26, 2026Chris Samarinas, Haw-Shiuan Chang, Hamed ZamaniReinforcement Learning Process Reward Models
Center for Intelligent Information Retrieval University of Massachusetts Amherst Amherst, MA, United States
AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
Feb 22, 2026Qijie You, Wenkai Yu, Wentao ZhangMulti-Hop QA LLM Agent Evaluation
University of Science and Technology Beijing · 2Peking University · 3Zhongguancun Academy +1
When Is Enough Not Enough? Illusory Completion in Search Agents
Feb 7, 2026Dayoon Ko, Jihyuk Kim, Sohyeon Kim +5AI Agent Reliability LLM Answer Verification
Seoul National University · LG AI Research · University of Illinois Chicago

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LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG

Yijia Zheng, Marcel Worring

University of Amsterdam, Amsterdam, the Netherlands

LLM Inference Acceleration Agentic RAG Latent Reasoning

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Single-step retrieval-augmented generation (RAG) provides an efficient way to incorporate external information for simple question answering tasks but struggles with complex questions. Agentic RAG extends this paradigm by replacing single-step retrieval with a multi-step process, in which the large language model (LLM) acts as a search agent that generates intermediate thoughts and subqueries to iteratively interact with the retrieval system. This iterative process incurs substantial latency due to the autoregressive generation of lengthy thoughts and subqueries. To address this limitation, we propose LatentRAG, a novel framework that shifts both reasoning and retrieval from discrete language space to continuous latent space. Unlike existing explicit methods that generate natural language thoughts or subqueries token-by-token, LatentRAG produces latent tokens for thoughts and subqueries directly from the hidden states in a single forward pass. We align LLMs with dense retrieval models in the latent space, enabling retrieval over latent subquery tokens and supporting end-to-end joint optimization. To improve transparency and encourage semantically meaningful latent representations, we incorporate a parallel latent decoding mechanism that translates latent tokens back into natural language. Extensive experiments on seven benchmark datasets show that LatentRAG achieves performance comparable to explicit agentic RAG methods while reducing inference latency by approximately 90%, substantially narrowing the latency gap with traditional single-step RAG.

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Latest papers 153