mlsanityData to 14 Oct 2026

EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes

arXiv 2509.00877 ↗First posted 31 Aug 2025cs.CLcited by 8

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging external information to enhance reasoning presents the following challenges: (1) low signal-to-noise ratio, where answer-supportive external information is diluted by irrelevant material, and (2) error accumulation, which arises in multi-hop reasoning when incomplete or misleading information is incorporated. To address these challenges, we introduce EviNote-RAG, a framework that follows a retrieve-note-answer workflow. Instead of reasoning directly over raw external information, the model first produces Supportive-Evidence Notes (SENs), which concisely preserve answer-critical information and explicitly mark key and uncertainty information to improve accuracy. We further design an entailment-based Evidence Quality Reward (EQR) to ensure that SENs are logically sufficient to derive the final answer, thereby enhancing SENs' quality. Experiments on both in-domain and out-of-domain QA benchmarks show that EviNote-RAG achieves state-of-the-art performance, improving answer accuracy, training stability, robustness, and efficiency. In particular, it yields relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), benefiting from improvements in the reasoning process.

Key facts. Cited by 8 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 72 of its 82 references that are in the map. First posted on arXiv 31 Aug 2025.

Builds on

72 of 82 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning17 Mar
  2. 2024RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation9 Jun
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity21 Mar
  4. 2024Metacognitive Retrieval-Augmented Large Language Models18 Feb
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct
  6. 2021Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks16 Dec

Other references · 66

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  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. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  7. 2024Qwen2.5 Technical Reportcited by 4,942
  8. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  11. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  12. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
38 more
  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  2. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  3. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  4. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  5. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  6. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  7. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  8. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  9. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  10. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  11. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  12. 2023Active Retrieval Augmented Generationcited by 516
  13. 2023LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compressioncited by 453
  14. 2025Search-o1: Agentic Search-Enhanced Large Reasoning Modelscited by 433
  15. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  16. 2025R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learningcited by 336
  17. 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 286
  18. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  19. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  20. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 256
  21. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 231
  22. 2025ZeroSearch: Incentivize the Search Capability of LLMs without Searchingcited by 208
  23. 2024Chain of Agents: Large Language Models Collaborating on Long-Context Taskscited by 206
  24. 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 165
  25. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGcited by 123
  26. 2025Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Toolscited by 106
  27. 2022Re2G: Retrieve, Rerank, Generatecited by 102
  28. 2023Ghostbuster: Detecting Text Ghostwritten by Large Language Modelscited by 91
  29. 2024Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systemscited by 87
  30. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilotcited by 83
  31. 2025ReasonIR: Training Retrievers for Reasoning Taskscited by 77
  32. 2024SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMscited by 67
  33. 2024Inference Scaling for Long-Context Retrieval Augmented Generationcited by 65
  34. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  35. 2025Open Deep Search: Democratizing Search with Open-source Reasoning Agentscited by 49
  36. 2024Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversationscited by 43
  37. 2024PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makerscited by 42
  38. 2025DeepRAG: Thinking to Retrieve Step by Step for Large Language Modelscited by 38

50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agentscited by 48
  2. 2026GRACE: Reinforcement Learning for Grounded Response and Abstention under Contextual Evidencecited by 7
  3. 2026Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generationcited by 5
  4. 2025Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Modelscited by 2
  5. 2025NCV: A Node-Wise Consistency Verification Approach for Low-Cost Structured Error Localization in LLM Reasoningcited by 1
  6. 2026Query Disambiguation via Answer-Free Context: Doubling Performance on Humanity's Last Examcited by 0
  7. 2026Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Mergingcited by 0
  8. 2026Contextual Information Policy Optimization for Search Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
  2. 2026SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation
  3. 2026AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
  4. 2026Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning
  5. 2025Credible Plan-Driven RAG Method for Multi-Hop Question Answering
  6. 2026S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
  7. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
  8. 2026Why Retrieval-Augmented Generation Fails: A Graph Perspective