mlsanityData to 14 Oct 2026

RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models

arXiv 2412.02830 ↗First posted 3 Dec 2024cs.CLcited by 25

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and factual integrity across large language models (LLMs) for complex, knowledge-intensive tasks such as commonsense and medical reasoning. RARE incorporates two innovative actions within the Monte Carlo Tree Search (MCTS) framework: A6, which generates search queries based on the initial problem statement, performs information retrieval using those queries, and augments reasoning with the retrieved data to formulate the final answer; and A7, which leverages information retrieval specifically for generated sub-questions and re-answers these sub-questions with the relevant contextual information. Additionally, a Retrieval-Augmented Factuality Scorer is proposed to replace the original discriminator, prioritizing reasoning paths that meet high standards of factuality. Experimental results with LLaMA 3.1 show that RARE enables open-source LLMs to achieve competitive performance with top open-source models like GPT-4 and GPT-4o. This research establishes RARE as a scalable solution for improving LLMs in domains where logical coherence and factual integrity are critical.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 29 of its 63 references that are in the map. First posted on arXiv 3 Dec 2024.

Builds on

29 of 63 references.

References · 29

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  5. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  6. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  7. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  8. 2023Reasoning with Language Model is Planning with World Modelcited by 861
  9. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 574
  10. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 558
  11. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  12. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 344
17 more
  1. 2023Alphazero-like Tree-Search can Guide Large Language Model Decoding and Trainingcited by 295
  2. 2023Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verificationcited by 166
  3. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 159
  4. 2024Long-form factuality in large language modelscited by 155
  5. 2024Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solverscited by 138
  6. 2024Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizingcited by 125
  7. 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generationcited by 74
  8. 2023Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planningcited by 61
  9. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questionscited by 54
  10. 2024JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capabilitycited by 32
  11. 2024BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicinecited by 31
  12. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  13. 2024MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedbackcited by 14
  14. 2023Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answeringcited by 11
  15. 2024RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicinecited by 7
  16. 2024Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challengescited by 5
  17. 2022Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervisioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 13; 2026: 11.

25 papers.

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  1. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  2. 2025Synergizing RAG and Reasoning: A Systematic Reviewcited by 27
  3. 2025RaDeR: Reasoning-aware Dense Retrieval Modelscited by 27
  4. 2025RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learningcited by 16
  5. 2025RARE: Retrieval-Augmented Reasoning Modelingcited by 11
  6. 2025EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notescited by 8
  7. 2025Uncertainty Quantification for Retrieval-Augmented Reasoningcited by 6
  8. 2025RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answeringcited by 5
  9. 2026EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Modelscited by 5
  10. 2025ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recoverycited by 4
  11. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarkscited by 4
  12. 2025RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrievalcited by 2
13 more
  1. 2026When KV Cache Reuse Fails in Multi-Agent Systems: Cross-Candidate Interaction is Crucial for LLM Judgescited by 2
  2. 2024A Survey of Query Optimization in Large Language Modelscited by 1
  3. 2025π-CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answeringcited by 1
  4. 2025AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entitiescited by 1
  5. 2026Say Anything but This: When Tokenizer Betrays Reasoning in LLMscited by 1
  6. 2026When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answeringcited by 1
  7. 2026CQC-RAG: Robust Retrieval-Augmented Generation via Cross-Query Consistencycited by 1
  8. 2025Iterative Tree Analysis for Medical Criticscited by 0
  9. 2026MARVEL: A Multi Agent-based Research Validator and Enabler using Large Language Modelscited by 0
  10. 2026Rethinking Patient Education as Multi-turn Multi-modal Interactioncited by 0
  11. 2026RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigmcited by 0
  12. 2026SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoningcited by 0
  13. 2026When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
  2. 2025RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems
  3. 2024Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
  4. 2025ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search
  5. 2025Improving Context Fidelity via Native Retrieval-Augmented Reasoning
  6. 2025FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
  7. 2024Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
  8. 2026When to Retrieve During Reasoning: Adaptive Retrieval for Large Reasoning Models