mlsanityData to 14 Oct 2026

Test-time Corpus Feedback: From Retrieval to RAG

arXiv 2508.15437 ↗First posted 21 Aug 2025cs.IR cs.AI cs.LGcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from external corpora. Despite its widespread use, most RAG pipelines continue to treat retrieval and reasoning as isolated components, retrieving documents once and then generating answers without further interaction. This static design often limits performance on complex tasks that require iterative evidence gathering or high-precision retrieval. Recent work in both the information retrieval (IR) and NLP communities has begun to close this gap by introducing adaptive retrieval and ranking methods that incorporate feedback. In this survey, we present a structured overview of advanced retrieval and ranking mechanisms that integrate such feedback. We categorize feedback signals based on their source and role in improving the query, retrieved context, or document pool. By consolidating these developments, we aim to bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 73 of its 124 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

73 of 124 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Controlled Retrieval-augmented Context Evaluation for Long-form RAG24 Jun
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan
  3. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov

Other references · 70

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  9. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  10. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  11. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  12. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
38 more
  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  2. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  3. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  4. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  5. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  6. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  7. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  8. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  9. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  11. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  12. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
  13. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 558
  14. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  15. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 376
  16. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  17. 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
  18. 2020Overview of the TREC 2019 deep learning trackcited by 286
  19. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 272
  20. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  21. 2025ZeroSearch: Incentivize the Search Capability of LLMs without Searchingcited by 208
  22. 2020HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verificationcited by 201
  23. 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 200
  24. 2021Overview of the TREC 2020 deep learning trackcited by 196
  25. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  26. 2024BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrievalcited by 158
  27. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 155
  28. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  29. 2023Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Taskscited by 73
  30. 2025Rank1: Test-Time Compute for Reranking in Information Retrievalcited by 70
  31. 2024Agents' Room: Narrative Generation through Multi-step Collaborationcited by 57
  32. 2022An Analysis of Fusion Functions for Hybrid Retrievalcited by 51
  33. 2023Information Retrieval: Recent Advances and Beyondcited by 49
  34. 2021Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedbackcited by 47
  35. 2024Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversationscited by 43
  36. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMscited by 42
  37. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  38. 2025Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicingcited by 25

50 most cited of 70 All 70 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Think Before You Retrieve: Learning Test-Time Adaptive Search with Small Language Modelscited by 5
  2. 2026When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMscited by 4
  3. 2026A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answeringcited by 0

Similar, not cited

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  1. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  3. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  4. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  5. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  6. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  7. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  8. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs