Retrieval-Augmented Reasoning

Latest papers

47 papers16 papers in the last four weeks, up 433% on the four weeks before. 0.1% of all new papers.

ARMOR: Adaptive Curriculum Meta-Learning for Noise-Robust RAG Reasoning
Nov 9, 2026Yan Wang, Yuxin Zhang, Shenyu Zhang +3RAG Robustness Retrieval-Augmented Reasoning
School of Computer Science and Engineering, Southeast University, China · Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · School of Law, Southeast University, China
PatternBloom: Empowering Agentic RAG with Externalized RL-Distilled Graph Patterns
Nov 7, 2026Yi Ge, Heng HuangAgentic RAG Retrieval-Augmented Reasoning
Department of Computer Science, University of Maryland, College Park
Embodied RAG++: Retrieval-Augmented Generation From What Exists to What Works in Embodied Scenes
Nov 5, 2026Fan Yang, Tatsuya Kikuzuki, Jun Liang +7Retrieval-Augmented Generation Retrieval-Augmented Reasoning
Fujitsu · Carnegie Mellon University
Selective Retrieval Routing for Multi-Violation Knowledge Graph Repair
Nov 5, 2026Tung-Wei Lin, Gabe Fierro, Han Li +3Knowledge Graphs Retrieval-Augmented Reasoning
UC Berkeley, Berkeley, USA · Colorado School of Mines, Golden, USA · Lawrence Berkeley National Laboratory, Berkeley, USA
Where Search Agents Go Off the Rails: Graph-Grounded Step-Level Verification
Nov 4, 2026Hyeseo Jeon, Seonho Yoo, Jay-Yoon LeeReasoning Verification LLM Auditing
Seoul National University · Korea University
ShiftRAG: Bypassing the Textual Bottleneck via Decoupled Learning and Continuous Soft Tokens
Oct 31, 2026Youmin Ko, Jihong Jeong, Hyunjoon KimRetrieval-Augmented Generation Retrieval-Augmented Reasoning
Department of Artificial Intelligence, Hanyang University · Department of Data Science, Hanyang University
ColaBench: Evaluating Memory-Guided Knowledge Acquisition in Personal Agents
Oct 22, 2026Wenjie Zhang, Zhichao Shi, Xuhui Jiang +4Memory-Augmented Agents Personal AI Agents
Jiangnan University, Wuxi, China · School of Advanced Interdisciplinary Sciences, UCAS, Beijing, China · State Key Lab of AI Safety, Institute of Computing Technology, CAS, Beijing, China +3
Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models
Oct 21, 2026Taewhoo Lee, Minju Song, Jungwoo Park +6State Tracking in Language Models RL for RAG
Korea University · Amazon · AIGEN Sciences +2
Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation
Oct 21, 2026Yufeng Li, Zaijing Li, Fuchen Zhang +3Graph Retrieval-Augmented Generation Retrieval-Augmented Reasoning
Central South University, Changsha, China · Harbin Institute of Technology, Shenzhen, China
Reasoning-Search-Augmented LLMs: An Operational Taxonomy, Benchmark Analysis and Controlled Evidence Synthesis
Oct 21, 2026Biswas Poudel, Nilson Chapagain, Amit Kumar +1Retrieval-Augmented Reasoning Language Model Reasoning Evaluation
Texas A&M University, College Station, TX, USA · Texas A&M University–Corpus Christi, Corpus Christi, TX, USA
RESIN: Multi-Chain Reasoning with Entity-Structured Indexing
Oct 19, 2026Xuanyu Meng, Jash Rajesh Parekh, Jiawei HanLLM Reasoning Retrieval-Augmented Reasoning
University of Illinois Urbana-Champaign
CoMem: Learning to Anticipate Failures from Retrieved Experience
Oct 19, 2026Yun Shen, Ming Shen, Tianjun YaoMemory-Augmented Language Model Agents Self-Reflection in Language Model Agents
The Ohio State University · Shenzhen Loop Area Institute · Shanghai Jiaotong University
Where and Why Search-Augmented LLMs Fail When Reasoning over Rapidly Evolving Knowledge
Oct 18, 2026Tianyi Hu, Bhavyajeet Singh, Niket Tandon +1Retrieval-Augmented Reasoning LLM Reasoning
Aarhus University · Microsoft Research · Technische Universität Darmstadt
Visual Scaffolding: Toward Native Multimodal Reasoning
Oct 18, 2026Run Shao, Zhaoyang Zhang, Ziyu Li +6Multimodal Reasoning VLM Reasoning
Central South University
Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning
Oct 17, 2026Kyuyoung Kim, Zilin Xiao, Zhiyuan Wang +4Retrieval-Augmented Reasoning RL for RAG
KAIST AI · Meta Superintelligence Labs
SteerSearch: Interactive Search Reasoning with Mid-Response Steering
Oct 16, 2026Udit Thakur, Baohao Liao, Matteo Nulli +4Language Model Steering Retrieval-Augmented Reasoning
Language Technology Lab, University of Amsterdam · eBay
From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents
Oct 15, 2026Shixuan Li, Wei Yang, Peiyu Zhang +7LLM Agent Memory LLM Agents
University of Southern California · Georgia Institute of Technology
MemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
Oct 11, 2026Hexiang Gu, Saihui Hou, Changlan Liu +4Retrieval-Augmented Reasoning Harmful Meme Detection
Beijing University of Posts and Telecommunications, Beijing 100876, China · the School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China · the College of Electrical Engineering, Zhejiang University, Zhejiang 310027, China +2
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
Learning to Retrieve Missing Evidence for Long-Term Memory QA
Sep 29, 2026Yi-Xuan Deng, Yi Zhang, Wei Liu +2RL for IR Evidence Retrieval
Tsinghua University
PRO-Step: Step-level Process Reward Optimization for Retrieval-Augmented Generation
Aug 31, 2026MinKeon Kim, Namjun Lee, Jaekwang KimRetrieval-Augmented Generation Retrieval-Augmented Reasoning
Department of Applied Artificial Intelligence, Convergence Program for Social Innovation, Sungkyunkwan University, Seoul, South Korea
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Aug 23, 2026Jun Chen, Yongchao Liu, Pengyu Qiu +6Multi-Hop QA Agentic RAG
1Shenzhen University, Shenzhen, China · 2Great Bay University, Dongguan, China · 3Ant Group, Hangzhou, China +1
GRIP: Grounded Reasoning via Information-Restricted Premises
Aug 17, 2026Lirui TengRetrieval-Augmented Generation LLM Grounding
University of Waterloo, Waterloo ON, Canada
ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling
Aug 11, 2026Vaibhav Singh, Soumya Suvra Ghosal, Sarvesh Gharat +3Numerical Reasoning in Language Models Retrieval-Augmented Reasoning
IIT Bombay · University of Maryland, College Park · Adobe Research
CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
Jul 2, 2026Dingling Xu, Ruobing Wang, Qingfei Zhao +8Retrieval-Augmented Reasoning LLM Reliability
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China · Beijing Key Laboratory of Artificial Intelligence for Education, Beijing 100875, China · Institute of Information Engineering, Chinese Academy of Sciences +3
Only Ask What You Don't Know: Grounded Delta Planning for Efficient Multi-step RAG
Jun 21, 2026Wei-Chieh Chou, Xuanjun Chen, Jian-Ren Lin +3Multi-Hop QA Retrieval-Augmented Generation
Dept. of Computer Science and Information Engineering, National Taiwan University · Graduate Institute of Communication Engineering, National Taiwan University · Department of Economics, National Taiwan University +2
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
Jun 11, 2026Zilin Xiao, Qi Ma, Chun-cheng Jason Chen +4Analogical Reasoning RL for Language Model Reasoning
Meta Superintelligence Labs · Rice University · Work partially done during internship at Meta
Self-Augmenting Retrieval for Diffusion Language Models
Jun 4, 2026Paul Jünger, Justin Lovelace, Linxi Zhao +2Diffusion Language Models Retrieval-Augmented Generation
Department of Computer Science, Cornell University.
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
May 29, 2026Zheng Yuan, Chuang Zhou, Linhao Luo +4Retrieval-Augmented Generation Graph Retrieval-Augmented Generation
The Hong Kong Polytechnic University · Monash University · Tencent Youtu Lab
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
May 13, 2026Jiashuo Sun, Jimeng Shi, Yixuan Xie +10Multi-Hop QA Retrieval-Augmented Generation
University of Illinois Urbana-Champaign · Hong Kong University of Science and Technology · Texas A&M University +1

Nov 9, 2026J/K move · Enter open · S save

ARMOR: Adaptive Curriculum Meta-Learning for Noise-Robust RAG Reasoning

Yan Wang, Yuxin Zhang, Shenyu Zhang, Yongrui Chen+2

School of Computer Science and Engineering, Southeast University, China · Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China · School of Law, Southeast University, China

RAG Robustness Retrieval-Augmented Reasoning Curriculum Learning Meta-Learning

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Retrieval-Augmented Generation (RAG) systems have demonstrated remarkable effectiveness in mitigating hallucinations by incorporating external knowledge. However, the retrieval process inevitably introduces noise, posing significant challenges to RAG robustness. Fundamentally, noise robustness is a composite competency encompassing noise identification, retrieval filtering, and robust reasoning. Unfortunately, existing methods often fail to capture this holistic nature, typically restricted to single-dimensional enhancements or hampered by noise-induced optimization interference. To address these limitations, we decompose RAG robustness into three sub-tasks: knowledge boundary perception, retrieval verification, and robust reasoning. Accordingly, we propose ARMOR, an Adaptive Curriculum Meta-Learning for Noise Robust RAG Reasoning. Inspired by the teaching process, ARMOR introduces an adaptive task scheduler as a teacher to curate training plans according to the absolute learning progress of RAG generators (Student). Distinct from GRPO, ARMOR utilizes inter-group advantage estimation during learning to effectively mitigate mode collapse caused by limited output spaces. Experiments on three complex reasoning datasets (2WikiMultiHopQA, MusiQue, and HotPotQA) indicate that ARMOR achieves synergistic improvements in retrieval noise robustness, significantly outperforming baselines in output quality.