mlsanityData to 14 Oct 2026

Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers

arXiv 2506.00054 ↗First posted 28 May 2025cs.IR cs.CLcited by 27

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance large language models (LLMs) by conditioning generation on external evidence retrieved at inference time. While RAG addresses critical limitations of parametric knowledge storage-such as factual inconsistency and domain inflexibility-it introduces new challenges in retrieval quality, grounding fidelity, pipeline efficiency, and robustness against noisy or adversarial inputs. This survey provides a comprehensive synthesis of recent advances in RAG systems, offering a taxonomy that categorizes architectures into retriever-centric, generator-centric, hybrid, and robustness-oriented designs. We systematically analyze enhancements across retrieval optimization, context filtering, decoding control, and efficiency improvements, supported by comparative performance analyses on short-form and multi-hop question answering tasks. Furthermore, we review state-of-the-art evaluation frameworks and benchmarks, highlighting trends in retrieval-aware evaluation, robustness testing, and federated retrieval settings. Our analysis reveals recurring trade-offs between retrieval precision and generation flexibility, efficiency and faithfulness, and modularity and coordination. We conclude by identifying open challenges and future research directions, including adaptive retrieval architectures, real-time retrieval integration, structured reasoning over multi-hop evidence, and privacy-preserving retrieval mechanisms. This survey aims to consolidate current knowledge in RAG research and serve as a foundation for the next generation of retrieval-augmented language modeling systems.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 49 of its 65 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

49 of 65 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systems9 Nov
  2. 2024SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains23 Oct
  3. 2024SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation15 Oct
  4. 2024Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training31 May
  5. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation31 Mar
  6. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 43

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  6. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  10. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  11. 2024Mixtral of Expertscited by 1,823
  12. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
31 more
  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  2. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  3. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  4. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  5. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  6. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  7. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  8. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  9. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  10. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 283
  11. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  12. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systemscited by 265
  13. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 234
  14. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  15. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  16. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 139
  17. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 125
  18. 2024Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approachcited by 119
  19. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 108
  20. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  21. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 96
  22. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 93
  23. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generationcited by 63
  24. 2024Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answeringcited by 51
  25. 2024ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrievalcited by 41
  26. 2024IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologuescited by 40
  27. 2024M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitionscited by 38
  28. 2024Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximizationcited by 29
  29. 2024Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selectioncited by 25
  30. 2024Ranked List Truncation for Large Language Model-based Re-Rankingcited by 20
  31. 2024Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domaincited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 22.

27 papers.

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  1. 2025Retrieval as a Decision: Training-Free Adaptive Gating for Efficient RAGcited by 5
  2. 2026Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behaviorcited by 5
  3. 2026Retrieval-Augmented LLMs for Security Incident Analysiscited by 4
  4. 2026A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challengescited by 3
  5. 2026Context-CoT: Enhancing Context Learning via High-Quality Reasoning Synthesiscited by 3
  6. 2025Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generationcited by 2
  7. 2026A Unified Language Model for Large Scale Search, Recommendation, and Reasoningcited by 2
  8. 2026ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoningcited by 2
  9. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directionscited by 2
  10. 2026Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classificationcited by 1
  11. 2026InterDeepResearch: Enabling Human-Agent Collaborative Information Seeking through Interactive Deep Researchcited by 1
  12. 2026HCAG: Hierarchical Abstraction and Retrieval-Augmented Generation on Theoretical Repositories with LLMscited by 1
15 more
  1. 2026When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systemscited by 1
  2. 2026Schema-First Retrieval: Embedding Catalogs for Natural Language Analyticscited by 1
  3. 2025Reliable Evaluation Protocol for Low-Precision Retrievalcited by 0
  4. 2025Democratizing LLM Efficiency: From Hyperscale Optimizations to Universal Deployabilitycited by 0
  5. 2025M3KG-RAG: Multi-hop Multimodal Knowledge Graph-enhanced Retrieval-Augmented Generationcited by 0
  6. 2026Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problemcited by 0
  7. 2026Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Examcited by 0
  8. 2026An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QAcited by 0
  9. 2026Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answeringcited by 0
  10. 2026A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturingcited by 0
  11. 2026RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Searchcited by 0
  12. 2026An Auditable Symbolic-RAG-Generative AI Architecture for Goal-Oriented Conversation Orchestrationcited by 0
  13. 2026FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Explorationcited by 0
  14. 2026EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceabilitycited by 0
  15. 2026Finding the Right Balance: Relevance and Diversity in LLM Retrievalcited by 0

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  1. 2025Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
  2. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  5. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  6. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  7. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey