Iterative RAG

RAG: Retrieval-Augmented Generation

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15 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation
Nov 2, 2026Yingtao Ren, Xiao Luo, Yiwei Fu +3Retrieval-Augmented Generation Iterative RAG
University of Technology Sydney · University of Wisconsin–Madison · Peking University
Retrieving from Your Own Mistakes: On-Policy Training of Embedding Models for Multi-Hop Retrieval-Augmented Generation
Oct 29, 2026Chenjie Qiu, Zixuan Zeng, Shaobin Shi +3Multi-Hop RAG Retrieval-Augmented Generation
Singapore Management University Singapore · Guangdong University of Technology Guangzhou, China
DeFT-RAG: Decomposing Multi-Hop Questions into Self-Contained Sub-Questions with Reusable Answers
Oct 27, 2026Dingyi Kang, Bingzhe LiMulti-Hop QA Retrieval-Augmented Generation
University of Texas at Dallas Richardson, Texas, USA
MAPS: Macro-Adaptive Parallel Reasoning for Regulating Search Momentum in Retrieval-Augmented Generation
Oct 21, 2026Xinze Li, Dingwen Zhang, Zhenghao Liu +5Retrieval-Augmented Generation Iterative RAG
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
DeclineCraft: A RAG Abstention Benchmark for Jointly Measuring Combined Retrieval and Parametric Knowledge
Oct 20, 2026Atharva Thakur, Abulhair Saparov, Bruno RibeiroRAG Evaluation LLM Abstention
Purdue University
Efficient Iterative Retrieval with Heterogeneous Batching
Sep 21, 2026Dohyun Park, Hubertus Franke, Daniel G. Waddington +2Retrieval-Augmented Generation LLM Serving
University of Illinois Urbana-Champaign · IBM Research
The Laws of Context Allocation: Causal Measurement and Closed-Loop Orchestration in Generative Search
Aug 24, 2026Peiyang Liu, Xi Wang, Di Liang +1Iterative RAG Retrieval-Augmented Generation
National Engineering Research Center for Software Engineering, Peking University, China · Peking University, China · Tencent, China
Hybrid-IR: Dual-Path Hybrid Retrieval with Iterative Reasoning for Complex Medical Question Answering
Jun 24, 2026Sheng Wan, Jiahui Zhang, Zicheng Zhao +1Retrieval-Augmented Generation Iterative RAG
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation
May 2, 2026Peiyang Liu, Ziqiang Cui, Xi Wang +2Retrieval-Augmented Generation Iterative RAG
National Engineering Research Center for Software Engineering, Peking University Beijing, China · City University of Hong Kong Hong Kong SAR, China · Tencent Technology Beijing, China
Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering
Apr 30, 2026Xupeng Chen, Binbin Shi, Chenqian Le +5Multimodal RAG Retrieval-Augmented Generation
New York University, New York, USA · Tsinghua University, Beijing, China · Independent Researcher +1
S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
Apr 26, 2026Minghan Li, Junjie Zou, Xinxuan Lv +2Multi-Hop QA Retrieval-Augmented Generation
Soochow University, Suzhou, China
PAR $^2$ -RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering
Mar 30, 2026Xingyu Li, Rongguang Wang, Yuying Wang +5Multi-Hop RAG Retrieval-Augmented Generation
Oracle AI
Gradually Excavating External Knowledge for Implicit Complex Question Answering
Mar 9, 2026Chang Liu, Xiaoguang Li, Lifeng Shang +4Iterative RAG Question Answering
The University of Hong Kong · Huawei Noah’s Ark Lab
KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries
Mar 2, 2026Isabela Iacob, Melisa Marian, Gheorghe Cosmin SilaghiIterative RAG Retrieval-Augmented Generation
Babes-Bolyai University, Business Informatics Research Center, Cluj-Napoca, Romania
RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation
Feb 6, 2026Tim Schlippe, Simon Martin, Michael MarketsmüllerRetrieval-Augmented Generation RAG Evaluation
IU International University of Applied Sciences, Germany

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TRIO: Task Refinement with Individual Optimization for Retrieval-Augmented Generation

Yingtao Ren, Xiao Luo, Yiwei Fu, Jinzhao Zhou+2

University of Technology Sydney · University of Wisconsin–Madison · Peking University

Retrieval-Augmented Generation Iterative RAG Agentic RAG RL for RAG

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This paper studies retrieval-augmented generation (RAG), which leverages external knowledge to improve the performance of language models. Despite the remarkable progress, current RAG approaches are still far from satisfactory due to inadequate retrieval and potential hallucinations. Towards this end, we propose a novel approach named Task Refinement with Individual Optimization (TRIO) for RAG. The core idea of our TRIO is to equip three well-designed agents with an iterative refinement paradigm by aligning their individual optimization objectives. Specifically, we first expand ambiguous queries with a query extension agent and quantify the reward based on the improved answer quality, which can be utilized to refine our agent for higher expansion efficiency. To enhance the retrieval quality, we include a unanimity voting to annotate the expanded query as insufficient or sufficient, and their generative outcomes are utilized as ground truth to optimize the judge agent. To further improve evidence utilization and answer accuracy, an answer agent is optimized with dynamic matching-based rewards with curriculum learning for final outputs. Extensive experiments across multiple benchmark datasets validate the effectiveness of the proposed TRIO in comparison with state-of-the-art approaches. Our code is available at https://github.com/yingtaoren/TRIO.