# HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2502.12442 · HTML: https://mlsanity.com/paper/2502.12442/

## Key facts

- Cited by 24 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 28 of its 47 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Placed in

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

## Builds on (most cited first, 28 of 28)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [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)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval](https://mlsanity.com/paper/2407.12883/) (2024)
- [Certifiably Robust RAG against Retrieval Corruption](https://mlsanity.com/paper/2405.15556/) (2024)
- [jina-embeddings-v3: Multilingual Embeddings With Task LoRA](https://mlsanity.com/paper/2409.10173/) (2024)
- [Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading](https://mlsanity.com/paper/2310.05029/) (2023)
- [GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning](https://mlsanity.com/paper/2405.20139/) (2024)
- [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://mlsanity.com/paper/1906.02916/) (2019)
- [Entities as Experts: Sparse Memory Access with Entity Supervision](https://mlsanity.com/paper/2004.07202/) (2020)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [Scaling Retrieval-Based Language Models with a Trillion-Token Datastore](https://mlsanity.com/paper/2407.12854/) (2024)
- [Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2305.18846/) (2023)
- [SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model](https://mlsanity.com/paper/2501.18636/) (2025)
- [T-RAG: Lessons from the LLM Trenches](https://mlsanity.com/paper/2402.07483/) (2024)
- [SiReRAG: Indexing Similar and Related Information for Multihop Reasoning](https://mlsanity.com/paper/2412.06206/) (2024)
- [Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning](https://mlsanity.com/paper/2405.16933/) (2024)
- [Graph Neural Network Enhanced Retrieval for Question Answering of LLMs](https://mlsanity.com/paper/2406.06572/) (2024)

## Built on by (most cited first, 24 of 24)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving](https://mlsanity.com/paper/2507.06229/) (2025)
- [RARE: Retrieval-Augmented Reasoning Modeling](https://mlsanity.com/paper/2503.23513/) (2025)
- [Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?](https://mlsanity.com/paper/2510.16582/) (2025)
- [Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists](https://mlsanity.com/paper/2604.28158/) (2026)
- [Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations](https://mlsanity.com/paper/2508.02016/) (2025)
- [Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics](https://mlsanity.com/paper/2510.05137/) (2025)
- [Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Case Study](https://mlsanity.com/paper/2504.16414/) (2025)
- [StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering](https://mlsanity.com/paper/2510.02827/) (2025)
- [Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking by Contrasting Layers](https://mlsanity.com/paper/2512.10422/) (2025)
- [PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering](https://mlsanity.com/paper/2603.29085/) (2026)
- [Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents](https://mlsanity.com/paper/2607.12267/) (2026)
- [RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrieval](https://mlsanity.com/paper/2506.08625/) (2025)
- [Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains](https://mlsanity.com/paper/2508.19357/) (2025)
- [S3-Attention:Attention-Aligned Endogenous Retrieval for Memory-Bounded Long-Context Inference](https://mlsanity.com/paper/2601.17702/) (2026)
- [A Human-in-the-Loop, LLM-Centered Architecture for Knowledge-Graph Question Answering](https://mlsanity.com/paper/2602.05512/) (2026)
- [CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://mlsanity.com/paper/2602.05728/) (2026)
- [DOTRAG: Retrieval-Time Reasoning Along Paths](https://mlsanity.com/paper/2605.18760/) (2026)
- [SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges](https://mlsanity.com/paper/2606.15971/) (2026)
- [CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps](https://mlsanity.com/paper/2607.10562/) (2026)
- [Harmonia: End-to-End RAG Serving Optimization](https://mlsanity.com/paper/2505.07833/) (2025)
- [AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction](https://mlsanity.com/paper/2603.05551/) (2026)
- [TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation](https://mlsanity.com/paper/2607.03447/) (2026)
- [SAG: SQL-Retrieval Augmented Generation with Query-Time Dynamic Hyperedges](https://mlsanity.com/paper/2608.12129/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a textitretrieve-reason-prune mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's textitretrieve-reason-prune mechanism can expand the retrieval scope based on logical connections and improve final answer quality.