Awesome-RAG-Reasoning

[EMNLP 2025] Awesome RAG Reasoning Resources

Last updated Jul 23, 2026

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#agent #agentic-ai #agentic-rag #llm #llms #multi-agent-systems #rag #reasoning #reasoning-language-models #search

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Awesome-RAG-Reasoning

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RAG and Reasoning System Overview

A curated collection of resources, papers, tools, and implementations that bridge the gap between Retrieval-Augmented Generation (RAG) and Reasoning in Large Language Models and Agents. This repository brings together traditionally separate research domains to enable more powerful Agentic AI systems.

📖 Related Survey: This repository is based on the taxonomy and framework presented in "Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs", featured 🏆 in Hugging Face Daily Papers.

🔍 Dive Deeper: For researchers interested in the latest developments in Agentic Deep Research, including cutting-edge papers and industry-leading deep research products, we recommend exploring our comprehensive collection at Awesome-Deep-Research 🔥🔥🔥.

If you find this repository useful, please cite our papers:

@article{li2025towards,
  title={Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs},
  author={Li, Yangning and Zhang, Weizhi and Yang, Yuyao and Huang, Wei-Chieh and Wu, Yaozu and Luo, Junyu and Bei, Yuanchen and Zou, Henry Peng and Luo, Xiao and Zhao, Yusheng and others},
  journal={arXiv preprint arXiv:2507.09477},
  year={2025}
}
@article{zhang2025web,   title={From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents},   author={Zhang, Weizhi and Li, Yangning and Bei, Yuanchen and Luo, Junyu and Wan, Guancheng and Yang, Liangwei and Xie, Chenxuan and Yang, Yuyao and Huang, Wei-Chieh and Miao, Chunyu and others},   journal={arXiv preprint arXiv:2506.18959},   year={2025} }

📖 Introduction

🔍 Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm that combines the strengths of large language models with external knowledge retrieval. By augmenting language models with relevant information from external sources, RAG systems can provide more accurate, up-to-date, and factual responses while maintaining the generative capabilities of modern LLMs.

Limitations:

- May retrieve irrelevant or inaccurate information - Limited by the quality and coverage of external knowledge bases

🧠 Reasoning has recently gained significant popularity as a complementary approach to enhance LLM performance. Reasoning techniques focus on improving the model's ability to process information, perform logical analysis, and arrive at conclusions through structured thinking processes. These methods enable LLMs to tackle complex problems that require multi-step inference, causal understanding, and systematic problem-solving.

Limitations:

- Often hallucinates or mis-grounds facts - Struggles with up-to-date or domain-specific information

Although RAG and Reasoning address different aspects of the model's capabilities. they have been developed largely independently, with separate research communities, methodologies, and evaluation benchmarks:

This repository serves as a comprehensive collection that bridges these traditionally separate domains, providing resources for researchers and practitioners interested in combining the strengths of both approaches.

RAG and Reasoning Taxonomy

Why RAG + Reasoning?

Large Language Models (LLMs) serve as the foundation for modern AI systems, but they face significant limitations in both knowledge access and reasoning capabilities. While RAG excels at providing factual knowledge and reasoning excels at logical processing, real-world problems often require both capabilities simultaneously. Complex queries demand not just access to relevant information, but also the ability to reason through that information systematically.

Real-World Impact: This combination enables AI systems to tackle complex problems that require both knowledge retrieval and sophisticated reasoning, such as scientific research, legal analysis, medical diagnosis, and strategic planning.

RAG and Reasoning Framework

The Reasoning-Enhanced RAG methods and RAG-Enhanced Reasoning methods represent one-way enhancements. In contrast, the Synergized RAG-Reasoning System performs reasoning and retrieval iteratively, enabling mutual enhancements.

What This Repository Covers

Below you will find a curated selection of research papers, open-source implementations, and benchmarking datasets that drive progress in RAG and Reasoning.

📚 Research Papers & Frameworks

Latest academic publications and open-source implementations that advance the integration of RAG and Reasoning.

Reasoning-Enhanced RAG

- Retrieval Optimization - Integration Enhancement - Generation Enhancement

RAG-Enhanced Reasoning

- External Knowledge Retrieval - Knowledge Base - Web Retrieval - Tool Using - In-context Retrieval - Prior Experience - Example or Training Data

Synergized RAG and Reasoning

- Reasoning Workflow - Chain-based - Tree-based - Graph-based - Walk-on-Graph - Think-on-Graph - Agentic Orchestration - Single-Agent - Prompting - Supervised Fine-Tuning - Reinforcement Learning - Multi-Agent

📊 Benchmarks & Datasets

The table linked below covers a diverse range of tasks. Each benchmark is annotated with its domain, knowledge type, reasoning capability, and dataset size.

Single-hop QA
Multi-hop QA
Multi-choice QA
Multi-step QA
Multimodal QA
Long-form QA
Graph QA
Code
Dialog
Fact Checking
Text Summarization
Deep Research
Retrieval Evaluation

🤝 Contributing

Guidelines for contributing to this repository and adding citation information.

📚 Research Papers and Frameworks: This section is organized according to the taxonomy in our research paper, providing resources for researchers and practitioners to explore, implement, and motivate new methods in the field.

Reasoning-Enhanced RAG

Retrieval Optimization

(EACL 2026) Over-Searching in Search-Augmented Large Language Models [[Paper]](https://arxiv.org/abs/2601.05503)
(arXiv 2026) CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks [[Paper]](https://arxiv.org/abs/2606.21777) [[Code]](https://github.com/ashwinn-v/CalVerT)
(KDD 2026 Workshop) TASR: Training-Free Adaptive Stopping for Iterative Retrieval [[Paper]](https://arxiv.org/abs/2606.13814)
(SIGIR 2026) Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery [[Paper]](https://arxiv.org/abs/2605.10530) [[Code]](https://github.com/Applied-Machine-Learning-Lab/SIGIR2026PDR)

(SIGIR 2026) When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models [[Paper]](https://arxiv.org/abs/2604.26649)

(ACL 2026 Findings) CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG [[Paper]](https://arxiv.org/abs/2604.25676)

(ACL 2026) S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA [[Paper]](https://arxiv.org/abs/2604.23783)

(WWW 2026) AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation [[Paper]](https://arxiv.org/abs/2604.09617) [[Code]](https://github.com/haoxuan-unt2024/AdaQE-CG)

(CVPR 2026 Workshop) HIVE: Query, Hypothesize, Verify An LLM Framework for Multimodal Reasoning-Intensive Retrieval [[Paper]](https://arxiv.org/abs/2604.07220) [[Code]](https://github.com/mm-bright/multimodal-reasoning-retrieval)

(ACL 2026 Findings) Region-R1: Reinforcing Query-Side Region Cropping for Multi-Modal Re-Ranking [[Paper]](https://arxiv.org/abs/2604.05268) [[Code]](https://github.com/taco-group/Region-R1)

(CIKM 2025) Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval [[Paper]](https://arxiv.org/abs/2603.21024)

(arXiv 2026) AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2602.24134)

(arXiv 2026) Query Suggestion for Retrieval-Augmented Generation via Dynamic In-Context Learning [[Paper]](https://arxiv.org/pdf/2601.08105)

(NeurIPS 2025) R3: Optimizing Retrieval for RAG via Reinforcement Learning [Paper] [Code]

(ArXiv 2025) Graph-Guided Concept Selection for Efficient Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2510.24120)

(ArXiv 2025) RL-QR: Generalized Reinforcement Learning for Retriever-Specific Query Rewriter with Unstructured Real-World Documents [[Paper]](https://arxiv.org/abs/2507.23242)

(AAAI 2025) MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/34732) [[Code]](https://github.com/yjEugenia/MaFeRw)

(ArXiv 2025) Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration [[Paper]](https://arxiv.org/abs/2504.04915) [[Code]](https://github.com/ritaranx/Collab-RAG/)

(ArXiv 2025) DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning [[Paper]](https://arxiv.org/abs/2503.00223) [[Code]](https://github.com/pat-jj/DeepRetrieval)

(ArXiv 2025) Credible plan-driven rag method for multi-hop question answering [[Paper]](https://arxiv.org/abs/2504.16787)

(ArXiv 2025) FIND: Fine-grained Information Density Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis [[Paper]](https://arxiv.org/abs/2502.14614)

(ArXiv 2025) LLM-Independent Adaptive RAG: Let the Question Speak for Itself [[Paper]](https://arxiv.org/abs/2505.04253) [[Code]](https://github.com/marialysyuk/ExternalAdaptiveRetrieval)

(ACL 2024) Chain-of-Verification Reduces Hallucination in Large Language Models [[Paper]](https://aclanthology.org/2024.findings-acl.212/)

(EMNLP 2024) Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs [[Paper]](https://aclanthology.org/2024.findings-emnlp.459/) [[Code]](https://github.com/zjukg/LPKG)

(EMNLP 2024) Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering [[Paper]](https://aclanthology.org/2024.findings-emnlp.446/) [[Code]](https://github.com/Dereck0602/Retrieval-and-Reasoning-on-KGs)

(NAACL 2024) Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [[Paper]](https://arxiv.org/abs/2403.14403) [[Code]](https://github.com/starsuzi/Adaptive-RAG)

(SIGIR 2024) Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers? [[Paper]](https://arxiv.org/abs/2311.09175)

(LREC-COLING 2024) RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion [[Paper]](https://aclanthology.org/2024.lrec-main.1182/) [[Code]](https://github.com/ZIZUN/RADCoT)

(ArXiv 2024) GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning [[Paper]](https://arxiv.org/abs/2405.20139) [[Code]](https://github.com/cmavro/GNN-RAG)

(ArXiv 2024) RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering [[Paper]](https://arxiv.org/abs/2410.22353) [[Code]](https://anonymous.4open.science/r/RuleRAG)

Integration Enhancement

(arXiv 2026) Trust Before Fusion: QIMG-7 and Source-Aware Resolution for Polluted Multimodal RAG [[Paper]](https://arxiv.org/abs/2607.10798)

(arXiv 2026) MIRAGE: Defending Long-Form RAG Against Misinformation Pollution [[Paper]](https://arxiv.org/abs/2607.05069) [[Code]](https://github.com/SaadElDine/MIRAGE)

(arXiv 2026) SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2606.27786) [[Code]](https://github.com/OpenBMB/SHIFT)

(arXiv 2026) Trust or Abstain? A Self-Aware RAG Approach [[Paper]](https://arxiv.org/abs/2605.18792) [[Code]](https://github.com/xizhu1022/SABER)

(ACL 2026) Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.13277)

(arXiv 2026) AdaGATE: Adaptive Gap-Aware Token-Efficient Evidence Assembly for Multi-Hop Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.05245) [[Code]](https://github.com/eliguo/AdaGATE)

(ACL 2026) Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning [[Paper]](https://arxiv.org/abs/2605.01399)

(arXiv 2026) Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.01302)

(arXiv 2026) ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2604.18362) [[Code]](https://github.com/1212Judy/ArbGraph)

(arXiv 2026) Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem [[Paper]](https://arxiv.org/abs/2602.18734) [[Code]](https://github.com/CoderrrSong/CoRAG)

(WACV 2026) Relevance-aware Multi-context Contrastive Decoding for Retrieval-augmented Visual Question Answering [[Paper]](https://arxiv.org/abs/2602.06050) [[Code]](https://github.com/mlvlab/RMCD)

(arXiv 2026) Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2602.03689) [[Code]](https://github.com/GasolSun36/BAR-RAG)

(ArXiv 2025) TruthfulRAG: Resolving Factual-level Conflicts in Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2511.10375) [Code]

(OpenReview 2025) Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation [[Paper]](https://openreview.net/forum?id=NhO6iEUISf)

(COLM 2025) Retrieval-Augmented Generation with Conflicting Evidence (RAMDocs + MADAM-RAG) [[Paper]](https://arxiv.org/abs/2504.13079) [[Code]](https://github.com/HanNight/RAMDocs)

(ArXiv 2025) DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering [[Paper]](https://arxiv.org/abs/2504.18243)

(EMNLP 2024) SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation [[Paper]](https://aclanthology.org/2024.emnlp-main.178/) [[Code]](https://github.com/HITsz-TMG/SEER)

(ICLR 2024) Making Retrieval-Augmented Language Models Robust to Irrelevant Context [[Paper]](https://openreview.net/forum?id=ZS4m74kZpH) [[Code]](https://github.com/oriyor/ret-robust)

(ACL 2024) BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering [[Paper]](https://aclanthology.org/2024.acl-long.67/)

Generation Enhancement

(ICPR 2026) SERC: LDPC-Inspired Semantic Error Correction for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.28837) [[Code]](https://github.com/labhai/SERC)

(ICLR 2026 Workshop) PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs [[Paper]](https://arxiv.org/abs/2603.20673)

(ACL 2026 Workshop) CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering [[Paper]](https://arxiv.org/abs/2603.16091)

(arXiv 2026) RAGShaper: Eliciting Sophisticated Agentic RAG Skills via Automated Data Synthesis [Paper]

(arXiv 2026) Disco-RAG: Discourse-Aware Retrieval-Augmented Generation for Long-Form Question Answering [Paper]

(ArXiv 2026) EviOmni: Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2507.15586) [[Code]](https://huggingface.co/HIT-TMG/EviOmni-nq_train-1.5B)

(ArXiv 2026) TreePS-RAG: Tree-based Process Supervision for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2601.06922)

(ACL 2025) RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation [[Paper]](https://aclanthology.org/2025.acl-long.179/) [[Code]](https://github.com/RUC-NLPIR/RAG-Critic)

(AAAI 2025) Improving Retrieval Augmented Language Model with Self-Reasoning [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/34743)

(ArXiv 2025) RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models [[Paper]](https://arxiv.org/abs/2503.23513)

(ArXiv 2025) AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning [[Paper]](https://arxiv.org/pdf/2504.14858v3) [[Code]](https://github.com/QQW-ing/RAG-ReasonAlignment)

(EMNLP 2024) Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models [[Paper]](https://aclanthology.org/2024.findings-emnlp.831/) [[Code]](https://github.com/ShayekhBinIslam/openrag)

(EMNLP 2024) TRACE the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation [[Paper]](https://arxiv.org/abs/2406.11460) [[Code]](https://github.com/jyfang6/trace)

⬆️ Back to Table of Contents
RAG-Enhanced Reasoning

External Knowledge Retrieval

Knowledge Base

(arXiv 2025) T-GRAG: A Dynamic GraphRAG Framework for Resolving Temporal Conflicts and Redundancy in Knowledge Retrieval [Paper] [[Code]](https://github.com/Arvin0313/T-GRAG)

(ICLR 2025) KBLaM: Knowledge Base augmented Language Model [[Paper]](https://arxiv.org/pdf/2410.10450) [[Code]](https://github.com/microsoft/KBLaM/)

(ArXiv 2025) Assisting Mathematical Formalization with A Learning-based Premise Retriever [[Paper]](https://arxiv.org/pdf/2501.13959) [[Code]](https://github.com/ruc-ai4math/Premise-Retrieval)

(ArXiv 2025) ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation [[Paper]](https://arxiv.org/pdf/2503.21729) [[Code]](https://github.com/THU-KEG/ReaRAG)

(ArXiv 2025) Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding [[Paper]](https://arxiv.org/pdf/2504.01281?)

(ArXiv 2025) PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation [[Paper]](https://arxiv.org/pdf/2501.11551) [[Code]](https://github.com/microsoft/PIKE-RAG?tab=readme-ov-file)

(SIGIR 2024) Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering [[Paper]](https://dl.acm.org/doi/abs/10.1145/3626772.3661370)

(ICCBR 2024) CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering [[Paper]](https://link.springer.com/chapter/10.1007/978-3-031-63646-2_29) [[Code]](https://github.com/rgu-iit-bt/cbr-for-legal-rag)

(LLM4Code 2024) LLM-based and Retrieval-Augmented Control Code Generation [[Paper]](https://dl.acm.org/doi/abs/10.1145/3643795.3648384)

(ArXiv 2024) MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries [[Paper]](https://arxiv.org/pdf/2401.15391) [[Code]](https://github.com/yixuantt/MultiHop-RAG/)

(MDPI 2024) CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning [[Paper]](https://www.mdpi.com/2079-9292/14/1/47)

Web Retrieval

(SIGIR 2026) Revisiting Text Ranking in Deep Research [[Paper]](https://arxiv.org/abs/2602.21456)

(arXiv 2026) SmartSearch: Enhancing Dense Retrieval with Dynamic LLM-Guided Iterative Search [Paper] [[Code]](https://github.com/RUC-NLPIR/SmartSearch)

(arXiv 2026) D2Plan: A Dual-Agent Framework for Dynamic Global Planning in Web Search [Paper]

(arXiv 2025) HiPRAG: A Hybrid Inference Framework for Retrieval-Augmented Generation [Paper] [[Code]](https://github.com/qualidea1217/HiPRAG)

(ICTIR 2025) Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking [[Paper]](https://arxiv.org/pdf/2504.03947) [[Code]](https://github.com/algoprog/InteRank)

(NAACL 2025) Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning [[Paper]](https://aclanthology.org/2025.naacl-short.68.pdf) [[Code]](https://github.com/jvladika/StepByStepFV)

(COLM 2024) Web Retrieval Agents for Evidence-Based Misinformation Detection [[Paper]](https://openreview.net/pdf?id=pKMxO0wBYZ) [[Code]](https://github.com/ComplexData-MILA/webretrieval)

(EMNLP 2024) OPEN-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models [[Paper]](https://aclanthology.org/2024.findings-emnlp.831.pdf) [[Code]](https://github.com/ShayekhBinIslam/openrag?tab=readme-ov-file)

(ACL 2024) FRVA: Fact-Retrieval and Verification Augmented Entailment Tree Generation for Explainable Question Answering [[Paper]](https://aclanthology.org/2024.findings-acl.540.pdf)

(FEVER 2024) Ragar, your falsehood radar: Rag-augmented reasoning for political fact-checking using multimodal large language models [[Paper]](https://arxiv.org/pdf/2404.12065)

(LREC-COLING 2024) PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning using Large Language Models [[Paper]](https://aclanthology.org/2024.lrec-main.1099.pdf)

Tool Using

(arXiv 2026) Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model [[Paper]](https://arxiv.org/abs/2604.07816) [[Code]](https://github.com/kfchenhn/TRB)

(arXiv 2026) Beyond Single-Shot: Multi-Step Tool Retrieval via Query Planning [Paper]

(arXiv 2025) Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems [Paper]

(COLING 2025) Efficient Tool Use with Chain-of-Abstraction Reasoning [[Paper]](https://aclanthology.org/2025.coling-main.185.pdf)

(NAACL 2025) Meta-Reasoning Improves Tool Use in Large Language Models [[Paper]](https://arxiv.org/pdf/2411.04535) [[Code]](https://github.com/lisaalaz/tecton?tab=readme-ov-file)

(ArXiv 2025) Self-Training Large Language Models for Tool-Use Without Demonstrations [[Paper]](https://arxiv.org/pdf/2502.05867) [[Code]](https://github.com/neneluo/llm-tool-use)

(ICLR 2024) Large Language Models As Tool Makers [[Paper]](https://arxiv.org/pdf/2305.17126) [[Code]](https://github.com/ctlllll/LLM-ToolMaker)

(ICLR 2024) ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [[Paper]](https://openreview.net/pdf?id=dHng2O0Jjr) [[Code]](https://github.com/OpenBMB/ToolBench)

(NeurIPS 2024) AVATAR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2024/file/2db8ce969b000fe0b3fb172490c33ce8-Paper-Conference.pdf) [[Code]](https://github.com/zou-group/avatar)

(EMNLP 2024) Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval [[Paper]](https://aclanthology.org/2024.findings-emnlp.270.pdf)

(EMNLP 2024) SCIAGENT: Tool-augmented Language Models for Scientific Reasoning [[Paper]](https://aclanthology.org/2024.emnlp-main.880.pdf)

(EMNLP 2024) RAR: Retrieval-augmented retrieval for code generation in low-resource languages [[Paper]](https://aclanthology.org/2024.emnlp-main.1199.pdf)

(ACL 2024) MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning [[Paper]](https://aclanthology.org/2024.findings-acl.69.pdf) [[Code]](https://github.com/VickiCui/MORE)

(LREC-COLING 2024) Towards Autonomous Tool Utilization in Language Models: A Unified, Efficient and Scalable Framework [[Paper]](https://aclanthology.org/2024.lrec-main.1427.pdf)

(NAACL 2024) Making Language Models Better Tool Learners with Execution Feedback [[Paper]](https://aclanthology.org/2024.naacl-long.195.pdf) [[Code]](https://github.com/zjunlp/TRICE)

(NeurIPS 2023) ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2023/file/8fd1a81c882cd45f64958da6284f4a3f-Paper-Conference.pdf) [[Code]](https://github.com/Ber666/ToolkenGPT)

In-context Retrieval

Prior Experience

(arXiv 2026) ExpWeaver: LLM Agents Learn from Experience via Latent RAG [[Paper]](https://arxiv.org/abs/2606.01041)

(ICML 2026) From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG [[Paper]](https://arxiv.org/abs/2605.18271) [[Code]](https://github.com/UbiquitousAILab/EPIC)

(arXiv 2026) RAG over Thinking Traces Can Improve Reasoning Tasks [[Paper]](https://arxiv.org/abs/2605.03344) [[Code]](https://github.com/Narabzad/t3)

(arXiv 2026) EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory [[Paper]](https://arxiv.org/abs/2604.27695) [[Code]](https://github.com/AIGeeksGroup/EviMem)

(ACL 2026 Findings) Feedback Adaptation for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2604.06647)

(arXiv 2026) MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [[Paper]](https://arxiv.org/abs/2603.03379) [[Code]](https://github.com/plageon/MemSifter)

(arXiv 2026) Beyond RAG for Agent Memory: Retrieval by Decoupling and Aggregation [[Paper]](https://arxiv.org/abs/2602.02007) [[Code]](https://github.com/HU-xiaobai/xMemory)

(ArXiv 2026) Structured Episodic Event Memory for LLM Agents [Paper]

(arXiv 2026) Synapse: An LLM-Based Cognitive Memory Architecture [Paper]

(arXiv 2026) SwiftMem: Continual Memory Learning for Large Language Models [Paper]

(ICLR 2025) Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning [[Paper]](https://arxiv.org/pdf/2410.19258?) [[Code]](https://github.com/FYYFU/HeadKV/tree/main)

(ICLR 2025) Human-like Episodic Memory for Infinite Context LLMs [[Paper]](https://openreview.net/pdf?id=BI2int5SAC)

(IEEE TPAMI 2025) JARVIS-1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10778628&casatoken=gxJlpiLwrAAAAAA:UCDJkqg5WT7Hr2LSxZUwt6MDTxTH-FHDhL9Dw8XUFJWpcJJSsEMgC5u4wGhE4DvxATX2hK_0CSM)

(ArXiv 2025) Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models [[Paper]](https://arxiv.org/pdf/2504.02273)

(ArXiv 2025) Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration [[Paper]](https://arxiv.org/pdf/2504.06943?)

(NeurIPS 2024) CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing [[Paper]](https://openreview.net/pdf?id=8DLW1saLEY) [[Code]](https://github.com/uclaml/COPS)

(CHI EA 2024) "My agent understands me beter": Integrating Dynamic Human-like Memory Recall and Consolidation in LLM-Based [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3613905.3650839?casatoken=KFkWdHv3aSkAAAAA:lIuVvR1RFiX7HfJ67vl7FVRb5oOKqE1NDl0DSONyvGryZlt5q4WdL8rjp24NHfcvf0KPAVWGtrBsg)

(ArXiv 2024) Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level [[Paper]](https://arxiv.org/pdf/2411.03562)

(ArXiv 2024) RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents [[Paper]](https://arxiv.org/pdf/2402.03610)

Example or Training Data

(ArXiv 2025) Q-RAG: Long Context Multi-Step Retrieval [Paper]

(ICLR 2025) OpenRAG: Optimizing RAG End-to-End viaIn-ContextRetrievalLearning [[Paper]](https://openreview.net/pdf?id=WX0Y0rBsqo)

(COLING 2025) PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation [[Paper]](https://aclanthology.org/2025.coling-main.534.pdf)

(IJCAI 2024) Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction [[Paper]](https://www.ijcai.org/proceedings/2024/0704.pdf)

(NeurIPS 2024) Mixture of Demonstrations for In-Context Learning [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2024/file/a0da098e0031f58269efdcba40eedf47-Paper-Conference.pdf) [[Code]](https://github.com/SongW-SW/MoD?tab=readme-ov-file)

(EACL 2024) Learning to Retrieve In-Context Examples for Large Language Models [[Paper]](https://aclanthology.org/2024.eacl-long.105.pdf) [[Code]](https://github.com/microsoft/LMOps/tree/main/llm_retriever)

(EMNLP 2023) UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation [[Paper]](https://aclanthology.org/2023.emnlp-main.758.pdf) [[Code]](https://github.com/microsoft/LMOps)

(ArXiv 2023) Dr.ICL: Demonstration-Retrieved In-context Learning [[Paper]](https://arxiv.org/pdf/2305.14128)

⬆️ Back to Table of Contents
Synergized RAG and Reasoning

Reasoning Workflow

Chain-based

(arXiv 2026) CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning [[Paper]](https://arxiv.org/abs/2607.02262) [[Code]](https://github.com/AI9Stars/CheckRLM)

(arXiv 2026) From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG [[Paper]](https://arxiv.org/abs/2603.03292) [[Code]](https://github.com/NJU-RL/MA-RAG)

(ICLR 2025) Long-context llms meet rag: Overcoming challenges for long inputs in rag [[Paper]](https://openreview.net/forum?id=oU3tpaR8fm)

(ArXiv 2025) Chain-of-Retrieval Augmented Generation [[Paper]](https://arxiv.org/abs/2501.14342) [[Code]](https://github.com/microsoft/LMOps/tree/main/corag)

(ArXiv 2025) CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models [[Paper]](https://arxiv.org/abs/2504.13534)

(ArXiv 2025) Rankcot: Refining knowledge for retrieval-augmented generation through ranking chain-of-thoughts [[Paper]](https://arxiv.org/abs/2502.17888) [[Code]](https://github.com/NEUIR/RankCoT)

(EMNLP 2024) Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation [[Paper]](https://arxiv.org/abs/2410.05801)

(EMNLP 2024) Chain-of-note: Enhancing robustness in retrieval-augmented language models [[Paper]](https://arxiv.org/abs/2311.09210)

(COLM 2024) Raft: Adapting language model to domain specific rag [[Paper]](https://openreview.net/pdf?id=rzQGHXNReU) [[Code]](https://github.com/ShishirPatil/gorilla)

(ArXiv 2024) Rat: Retrieval augmented thoughts elicit context-aware reasoning in long-horizon generation [[Paper]](https://arxiv.org/pdf/2403.05313) [[Code]](https://github.com/CraftJarvis/RAT)

(ArXiv 2024) TRACE the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation [[Paper]](https://arxiv.org/abs/2406.11460) [[Code]](https://github.com/jyfang6/trace)

(ACL 2023) Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions [[Paper]](https://arxiv.org/pdf/2212.10509) [[Code]](https://github.com/stonybrooknlp/ircot)

Tree-based

(ICML 2026) Hierarchical Abstract Tree for Cross-Document Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.00529) [[Code]](https://github.com/Newiz430/Psi-RAG)

(arXiv 2026) Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS [[Paper]](https://arxiv.org/abs/2604.10734) [[Code]](https://github.com/xjiacs/Self-Correcting-RAG)

(ACL 2025) ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search [[Paper]](https://arxiv.org/abs/2504.10893) [[Code]](https://github.com/OpenCausaLab/ARise)

(AAAI 2025) RATT: A Thought Structure for Coherent and Correct LLM Reasoning [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/34876) [[Code]](https://github.com/jinghanzhang1998/RATT)

(ArXiv 2025) MCTS-RAG: Enhance Retrieval-Augmented Generation with Monte Carlo Tree Search [[Paper]](https://arxiv.org/pdf/2503.20757?) [[Code]](https://github.com/yale-nlp/MCTS-RAG)

(ArXiv 2025) Airrag: Activating intrinsic reasoning for retrieval augmented generation via tree-based search [[Paper]](https://arxiv.org/abs/2501.10053)

(ArXiv 2025) Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data [[Paper]](https://arxiv.org/abs/2501.02727)

(ArXiv 2024) SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2406.11258)

(ArXiv 2024) CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2411.00744)

(ACL 2023) Tree of clarifications: Answering ambiguous questions with retrieval-augmented large language models [[Paper]](https://aclanthology.org/2023.emnlp-main.63/) [[Code]](https://github.com/gankim/tree-of-clarifications)

(EMNLP 2023) Grove: a retrieval-augmented complex story generation framework with a forest of evidence [[Paper]](https://arxiv.org/abs/2310.05388)

Graph-based

Walk-on-Graph

(arXiv 2026) RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM [[Paper]](https://arxiv.org/abs/2607.11683) [[Code]](https://github.com/RaguTeam/RAGU)

(ICML 2026) RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation [[Paper]](https://arxiv.org/abs/2607.06527)

(arXiv 2026) MAGE-RAG: Multigranular Adaptive Graph Evidence for Agentic Multimodal RAG in Long-Document QA [[Paper]](https://arxiv.org/abs/2606.15906)

(arXiv 2026) IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval [[Paper]](https://arxiv.org/abs/2606.06044) [[Code]](https://github.com/xiaoAugenstern/LogicalRAGTemporalQA)

(KDD 2026) MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2606.00610) [[Code]](https://github.com/XMUDeepLIT/MemGraphRAG)

(arXiv 2026) MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2605.31010) [[Code]](https://github.com/DEEP-PolyU/MoG)

(arXiv 2026) Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG [[Paper]](https://arxiv.org/abs/2605.28004) [[Code]](https://github.com/DonFinliani/CrossAug)

(ACL 2026) STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2604.22282)

(arXiv 2026) AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2604.20844) [[Code]](https://github.com/7HHHHH/AtomicRAG)

(arXiv 2026) Toward Robust GraphRAG: Mitigating Retrieval Drift and Hallucination from Imperfect Knowledge Graphs [[Paper]](https://arxiv.org/abs/2603.14828) [[Code]](https://github.com/myz12138/CS-RAG)

(arXiv 2026) MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries [[Paper]](https://arxiv.org/abs/2603.11223) [[Code]](https://github.com/DataSciencePolimi/MDER-DRRAG)

(KDD 2026) Core-based Hierarchies for Efficient GraphRAG [[Paper]](https://arxiv.org/abs/2603.05207)

(ACL 2025) HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval [[Paper]](https://arxiv.org/abs/2603.02248)

(WWW 2026) HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation [[Paper]](https://arxiv.org/abs/2602.14470)

(ICML 2026) CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG [[Paper]](https://arxiv.org/abs/2602.05143) [[Code]](https://github.com/Pwnb/CausalRAG2)

(arXiv 2026) Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation [[Paper]](https://arxiv.org/abs/2602.01965) [[Code]](https://github.com/kwunhang/CatRAG)

(ICLR 2025) Reasoning of Large Language Models over Knowledge Graphs with Super-Relations [[Paper]](https://openreview.net/forum?id=rTCJ29pkuA) [[Code]](https://github.com/SongW-SW/REKNOS)

(ICLR 2025) Simple is Effective: The Roles of Graphs and LLMs in Knowledge-Graph-Based RAG [[Paper]](https://arxiv.org/pdf/2410.20724) [[Code]](https://github.com/Graph-COM/SubgraphRAG)

(ICLR 2025) StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [[Paper]](https://arxiv.org/pdf/2410.08815) [[Code]](https://github.com/icip-cas/StructRAG)

(ArXiv 2025) From RAG to Memory: Non-Parametric Continual Learning for Large Language Models [[Paper]](https://arxiv.org/pdf/2502.14802) [[Code]](https://github.com/OSU-NLP-Group/HippoRAG)

(ArXiv 2025) From Local to Global: A GraphRAG Approach to Query-Focused Summarization [[Paper]](https://arxiv.org/pdf/2404.16130) [[Code]](https://github.com/ksachdeva/langchain-graphrag)

(NeurIPS 2024) G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [[Paper]](https://arxiv.org/pdf/2402.07630) [[Code]](https://github.com/manthan2305/Efficient-G-Retriever)

(NeurIPS 2024) HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models [[Paper]](https://arxiv.org/pdf/2405.14831) [[Code]](https://github.com/OSU-NLP-Group/HippoRAG)

(ArXiv 2024) DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature [[Paper]](https://arxiv.org/pdf/2405.04819) [[Code]](https://github.com/David-Li0406/DALK)

(ArXiv 2024) GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning [[Paper]](https://arxiv.org/pdf/2405.20139) [[Code]](https://github.com/cmavro/GNN-RAG)

(ArXiv 2024) LightRAG: Simple and Fast Retrieval-Augmented Generation [[Paper]](https://arxiv.org/pdf/2410.05779) [[Code]](https://github.com/HKUDS/LightRAG)

(ArXiv 2023) Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering [[Paper]](https://arxiv.org/abs/2309.11206) [[Code]](https://github.com/wuyike2000/Retrieve-Rewrite-Answer)

(ICLR 2022) GreaseLM: Graph REASoning Enhanced Language Models for Question Answering [[Paper]](https://arxiv.org/pdf/2201.08860) [[Code]](https://github.com/snap-stanford/GreaseLM)

(ACL 2022) Subgraph Retrieval Enhanced Model for Multi-hop KBQA [[Paper]](https://arxiv.org/pdf/2202.13296) [[Code]](https://github.com/RUCKBReasoning/SubgraphRetrievalKBQA)

(NAACL 2021) QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering [[Paper]](https://arxiv.org/pdf/2104.06378) [[Code]](https://github.com/michiyasunaga/qagnn)

(ACL 2019) PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text [[Paper]](https://arxiv.org/pdf/1904.09537) [[Code]](https://github.com/BrambleXu/knowledge-graph-learning/issues/255)

Think-on-Graph

(EKAW 2026) MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA [[Paper]](https://arxiv.org/abs/2607.14561) [[Code]](https://github.com/dice-group/mars-kgqa)

(arXiv 2026) KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning [[Paper]](https://arxiv.org/abs/2604.12487) [[Code]](https://github.com/Wangshuaiia/KG-Reasoner)

(arXiv 2026) GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum [[Paper]](https://arxiv.org/abs/2603.28533) [[Code]](https://github.com/XuShuwenn/GraphWalker)

(IJCNN 2026) KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning [[Paper]](https://arxiv.org/abs/2603.21440) [[Code]](https://github.com/Wangshuaiia/KG-Hopper)

(arXiv 2026) RouterKGQA: Specialized--General Model Routing for Constraint-Aware Knowledge Graph Question Answering [[Paper]](https://arxiv.org/abs/2603.20017) [[Code]](https://github.com/Oldcircle/RouterKGQA)

(ICLR 2025) Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation [[Paper]](https://arxiv.org/pdf/2407.10805) [[Code]](https://github.com/IDEA-FinAI/ToG-2)

(ICLR 2024) Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph [[Paper]](https://arxiv.org/pdf/2307.07697) [[Code]](https://github.com/spcl/graph-of-thoughts)

(ICLR 2024) Reasoning on Graphs: Faithful and Interpretable LLM Reasoning (RoG) [[Paper]](https://arxiv.org/pdf/2310.01061)

(ACL 2024) Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs [[Paper]](https://arxiv.org/pdf/2404.07103) [[Code]](https://github.com/PeterGriffinJin/Graph-CoT)

(ACL 2024) GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models [[Paper]](https://arxiv.org/pdf/2406.14550)

(AAAI 2024) Knowledge Graph Prompting for Multi-Document Question Answering [[Paper]](https://arxiv.org/pdf/2308.11730) [[Code]](https://github.com/YuWVandy/KG-LLM-MDQA)

(WWW 2025) Kag: Boosting llms in professional domains via knowledge augmented generation [[Paper]](https://arxiv.org/abs/2409.13731) [[Code]](https://github.com/OpenSPG/KAG)

(EMNLP 2022) Empowering Language Models with Knowledge Graph Reasoning for Question Answering [[Paper]](https://arxiv.org/pdf/2211.08380)

(CIS 2024) KnowledgeNavigator: Leveraging Large Language Models for Enhanced Reasoning over Knowledge Graph [[Paper]](https://arxiv.org/pdf/2312.15880) [[Code]](https://github.com/katzurik/KnowledgeNavigator)

(ArXiv 2024) HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses [[Paper]](https://arxiv.org/pdf/2312.15883) [[Code]](https://github.com/Artessay/HyKGE)

(ArXiv 2024) KG-RAG: Bridging the Gap Between Knowledge and Creativity [[Paper]](https://arxiv.org/pdf/2405.12035)

(ArXiv 2024) Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval [[Paper]](https://arxiv.org/pdf/2405.06545)

Agentic Orchestration

Single-Agent

Prompting

(arXiv 2026) Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search [[Paper]](https://arxiv.org/abs/2606.17209) [[Code]](https://github.com/cxcscmu/diverse-query-initialization)

(arXiv 2026) FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search [[Paper]](https://arxiv.org/abs/2606.00660) [[Code]](https://github.com/XuZhao0/fineverify)

(arXiv 2026) Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient? [[Paper]](https://arxiv.org/abs/2605.10848) [[Code]](https://github.com/justram/pi-serini)

(arXiv 2026) Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG [[Paper]](https://arxiv.org/abs/2604.14572) [[Code]](https://github.com/dukesun99/Corpus2Skill)

(arXiv 2026) GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning [[Paper]](https://arxiv.org/abs/2603.06620) [[Code]](https://github.com/FairyFali/GraphSkill)

(arXiv 2026) DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search [[Paper]](https://arxiv.org/abs/2602.05014) [[Code]](https://github.com/Zhanli-Li/DeepRead)

(arXiv 2026) A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces [[Paper]](https://arxiv.org/abs/2602.03442) [[Code]](https://github.com/Ayanami0730/arag)

(ArXiv 2025) Search-o1: Agentic Search-Enhanced Large Reasoning Models [[Paper]](https://arxiv.org/pdf/2501.05366) [[Code]](https://github.com/sunnynexus/Search-o1)

(ArXiv 2025) Plan∗RAG: Efficient Test-Time Planning for Retrieval Augmented Generation [[Paper]](https://arxiv.org/pdf/2410.20753)

(ArXiv 2025) Open Deep Search: Democratizing Search with Open-source Reasoning Agents [[Paper]](https://arxiv.org/pdf/2503.20201) [[Code]](https://github.com/sentient-agi/OpenDeepSearch)

(ArXiv 2025) DeepRAG: Thinking to Retrieval Step by Step for Large Language Models [[Paper]](https://arxiv.org/pdf/2502.01142)

(ArXiv 2025) Enhancing Retrieval Systems with Inference-Time Logical Reasoning [[Paper]](https://arxiv.org/pdf/2503.17860)

(ArXiv 2025) Self-Taught Agentic Long-Context Understanding [[Paper]](https://arxiv.org/pdf/2502.15920)

(ICLR 2024) Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [[Paper]](https://openreview.net/forum?id=hSyW5go0v8) [[Code]](https://github.com/AkariAsai/self-rag)

(KDD Cup 2024) A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning [[Paper]](https://arxiv.org/pdf/2408.05141)

(ICLR 2023) ReAct: Synergizing Reasoning and Acting in Language Models [[Paper]](https://arxiv.org/pdf/2210.03629) [[Code]](https://github.com/ysymyth/ReAct)

(EMNLP 2023) Measuring and Narrowing the Compositionality Gap in Language Models [[Paper]](https://arxiv.org/abs/2210.03350)

(ACL 2023) Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions [[Paper]](https://arxiv.org/pdf/2212.10509)

Supervised Fine-Tuning

(arXiv 2026) FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents [[Paper]](https://arxiv.org/abs/2606.12087) [[Code]](https://github.com/RUCAIBox/FORT-Searcher)

(ACL 2026 Findings) SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering [[Paper]](https://arxiv.org/abs/2604.24515) [[Code]](https://github.com/Applied-Machine-Learning-Lab/ACL2026SEARCH-R)

(arXiv 2026) Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation [[Paper]](https://arxiv.org/abs/2604.10741) [[Code]](https://github.com/fangda-ye/Deep-Report)

(arXiv 2026) OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis [[Paper]](https://arxiv.org/abs/2603.20278

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