# Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach

arXiv: https://arxiv.org/abs/2407.13101 · HTML: https://mlsanity.com/paper/2407.13101/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 26 of 26)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://mlsanity.com/paper/2305.15294/) (2023)
- [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://mlsanity.com/paper/2405.13576/) (2024)
- [Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://mlsanity.com/paper/2210.02627/) (2022)
- [WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences](https://mlsanity.com/paper/2306.07906/) (2023)
- [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://mlsanity.com/paper/2305.06849/) (2023)
- [SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs](https://mlsanity.com/paper/2404.13081/) (2024)
- [Multi-hop Question Answering](https://mlsanity.com/paper/2204.09140/) (2022)
- [A Survey for Efficient Open Domain Question Answering](https://mlsanity.com/paper/2211.07886/) (2022)
- [FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering](https://mlsanity.com/paper/2406.13779/) (2024)
- [Efficient Knowledge Infusion via KG-LLM Alignment](https://mlsanity.com/paper/2406.03746/) (2024)
- [LMGQS: A Large-scale Dataset for Query-focused Summarization](https://mlsanity.com/paper/2305.13086/) (2023)

## Built on by (most cited first, 30 of 40)

- [In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents](https://mlsanity.com/paper/2503.08026/) (2025)
- [Sufficient Context: A New Lens on Retrieval Augmented Generation Systems](https://mlsanity.com/paper/2411.06037/) (2024)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges](https://mlsanity.com/paper/2508.05668/) (2025)
- [Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions](https://mlsanity.com/paper/2408.00727/) (2024)
- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [PropRAG: Guiding Retrieval with Beam Search over Proposition Paths](https://mlsanity.com/paper/2504.18070/) (2025)
- [Supervising the search process produces reliable and generalizable information-seeking agents](https://mlsanity.com/paper/2502.13957/) (2025)
- [U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack](https://mlsanity.com/paper/2503.00353/) (2025)
- [Credible Plan-Driven RAG Method for Multi-Hop Question Answering](https://mlsanity.com/paper/2504.16787/) (2025)
- [LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers](https://mlsanity.com/paper/2502.18139/) (2025)
- [InfMem: Learning System-2 Memory Control for Long-Context Agent](https://mlsanity.com/paper/2602.02704/) (2026)
- [S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA](https://mlsanity.com/paper/2604.23783/) (2026)
- [QAgent: A modular Search Agent with Interactive Query Understanding](https://mlsanity.com/paper/2510.08383/) (2025)
- [Reducing Latency of LLM Search Agent via Speculation-based Algorithm-System Co-Design](https://mlsanity.com/paper/2511.20048/) (2025)
- [JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG](https://mlsanity.com/paper/2601.21916/) (2026)
- [GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning](https://mlsanity.com/paper/2510.20548/) (2025)
- [BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering](https://mlsanity.com/paper/2505.11811/) (2025)
- [Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning](https://mlsanity.com/paper/2505.17086/) (2025)
- [Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2509.00798/) (2025)
- [StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering](https://mlsanity.com/paper/2510.02827/) (2025)
- [From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development](https://mlsanity.com/paper/2509.23071/) (2025)
- [HyCE-RAG: Hypergraph Chain-of-Evidence Retrieval-Augmented Generation for Explainable Multi-hop Question Answering](https://mlsanity.com/paper/2607.22597/) (2026)
- [ENCO: Life-Cycle Management of Enterprise-Grade Copilots](https://mlsanity.com/paper/2412.06099/) (2024)
- [Continually Self-Improving Language Models for Bariatric Surgery Question--Answering](https://mlsanity.com/paper/2505.16102/) (2025)
- [Small Encoders Can Rival Large Decoders in Detecting Groundedness](https://mlsanity.com/paper/2506.21288/) (2025)
- [Transforming Questions and Documents for Semantically Aligned Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.09755/) (2025)
- [HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering](https://mlsanity.com/paper/2509.09713/) (2025)
- [STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models](https://mlsanity.com/paper/2510.07923/) (2025)
- [Think Straight, Stop Smart: Structured Reasoning for Efficient Multi-Hop RAG](https://mlsanity.com/paper/2510.19171/) (2025)

## Abstract (arXiv)

Multi-hop question answering is a challenging task with distinct industrial relevance, and Retrieval-Augmented Generation (RAG) methods based on large language models (LLMs) have become a popular approach to tackle this task. Owing to the potential inability to retrieve all necessary information in a single iteration, a series of iterative RAG methods has been recently developed, showing significant performance improvements. However, existing methods still face two critical challenges: context overload resulting from multiple rounds of retrieval, and over-planning and repetitive planning due to the lack of a recorded retrieval trajectory. In this paper, we propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. This summarizer compresses information from retrieved documents, targeting both the overarching question and the current sub-question concurrently. Experimental results on the multi-hop question-answering datasets HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art, and exhibits excellent robustness concerning context length.