mlsanityData to 14 Oct 2026

A Reproducibility Study of Metacognitive Retrieval-Augmented Generation

arXiv 2604.19899 ↗First posted 21 Apr 2026cs.IRcited by 0

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

Abstract (arXiv)

Recently, Retrieval Augmented Generation (RAG) has shifted focus to multi-retrieval approaches to tackle complex tasks such as multi-hop question answering. However, these systems struggle to decide when to stop searching once enough information has been gathered. To address this, citetzhou2024metacognitive introduced Metacognitive Retrieval Augmented Generation (MetaRAG), a framework inspired by metacognition that enables Large Language Models to critique and refine their reasoning. In this reproducibility paper, we reproduce MetaRAG following its original experimental setup and extend it in two directions: (i) by evaluating the effect of PointWise and ListWise rerankers, and (ii) by comparing with SIM-RAG, which employs a lightweight critic model to stop retrieval. Our results confirm MetaRAG's relative improvements over standard RAG and reasoning-based baselines, but also reveal lower absolute scores than reported, reflecting challenges with closed-source LLM updates, missing implementation details, and unreleased prompts. We show that MetaRAG is partially reproduced, gains substantially from reranking, and is more robust than SIM-RAG when extended with additional retrieval features.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 31 of its 47 references that are in the map. First posted on arXiv 21 Apr 2026.

Builds on

31 of 47 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Metacognitive Retrieval-Augmented Large Language Models18 Feb
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 29

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  8. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  9. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  11. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  12. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
17 more
  1. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  2. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 723
  3. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 558
  4. 2023Active Retrieval Augmented Generationcited by 516
  5. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  6. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 236
  7. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  8. 2023RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!cited by 155
  9. 2022Training Language Models with Memory Augmentationcited by 113
  10. 2021Efficient Nearest Neighbor Language Modelscited by 93
  11. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 85
  12. 2022Multi-hop Question Answeringcited by 64
  13. 2023Predicting Question-Answering Performance of Large Language Models through Semantic Consistencycited by 28
  14. 2025Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicingcited by 25
  15. 2025RankLLM: A Python Package for Reranking with LLMscited by 16
  16. 2023Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Modelscited by 11
  17. 2024SMART-RAG: Selection using Determinantal Matrices for Augmented Retrievalcited by 7

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