# DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering

arXiv: https://arxiv.org/abs/2504.18243 · HTML: https://mlsanity.com/paper/2504.18243/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 30 of 34)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [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)
- [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)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (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)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://mlsanity.com/paper/2310.06839/) (2023)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://mlsanity.com/paper/2305.15294/) (2023)
- [ASQA: Factoid Questions Meet Long-Form Answers](https://mlsanity.com/paper/2204.06092/) (2022)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://mlsanity.com/paper/2405.13576/) (2024)
- [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://mlsanity.com/paper/2011.02593/) (2020)
- [Evaluation of Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2405.07437/) (2024)
- [Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering](https://mlsanity.com/paper/2304.12102/) (2023)
- [Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering](https://mlsanity.com/paper/2406.14891/) (2024)
- [PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers](https://mlsanity.com/paper/2406.12430/) (2024)
- [CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis](https://mlsanity.com/paper/2407.09811/) (2024)
- [Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs](https://mlsanity.com/paper/2402.12052/) (2024)

## Built on by (most cited first, 20 of 20)

- [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://mlsanity.com/paper/2507.09477/) (2025)
- [Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains](https://mlsanity.com/paper/2505.16014/) (2025)
- [GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning](https://mlsanity.com/paper/2510.20548/) (2025)
- [HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores](https://mlsanity.com/paper/2509.21336/) (2025)
- [CogDoc: Towards Unified thinking in Documents](https://mlsanity.com/paper/2512.12658/) (2025)
- [Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review](https://mlsanity.com/paper/2508.05660/) (2025)
- [CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering](https://mlsanity.com/paper/2601.06799/) (2026)
- [PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering](https://mlsanity.com/paper/2603.29085/) (2026)
- [Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts](https://mlsanity.com/paper/2606.09105/) (2026)
- [Disco-RAG: Discourse-Aware Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.04377/) (2026)
- [PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop Question Answering](https://mlsanity.com/paper/2601.05465/) (2026)
- [CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering](https://mlsanity.com/paper/2602.05728/) (2026)
- [STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering](https://mlsanity.com/paper/2604.17405/) (2026)
- [EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation](https://mlsanity.com/paper/2605.30961/) (2026)
- [FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A](https://mlsanity.com/paper/2606.07235/) (2026)
- [GPG: Generalized Policy Gradient Theorem for Transformer-based Policies](https://mlsanity.com/paper/2512.10365/) (2025)
- [MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits](https://mlsanity.com/paper/2604.08952/) (2026)
- [RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm](https://mlsanity.com/paper/2604.20860/) (2026)
- [Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval](https://mlsanity.com/paper/2605.24454/) (2026)
- [D2F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation](https://mlsanity.com/paper/2608.04444/) (2026)

## Abstract (arXiv)

Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing approaches have been improved with iterative retrieval, they still struggle to identify and organize dynamic knowledge. To address this, we propose DualRAG, a synergistic dual-process framework that seamlessly integrates reasoning and retrieval. DualRAG operates through two tightly coupled processes: Reasoning-augmented Querying (RaQ) and progressive Knowledge Aggregation (pKA). They work in concert: as RaQ navigates the reasoning path and generates targeted queries, pKA ensures that newly acquired knowledge is systematically integrated to support coherent reasoning. This creates a virtuous cycle of knowledge enrichment and reasoning refinement. Through targeted fine-tuning, DualRAG preserves its sophisticated reasoning and retrieval capabilities even in smaller-scale models, demonstrating its versatility and core advantages across different scales. Extensive experiments demonstrate that this dual-process approach substantially improves answer accuracy and coherence, approaching, and in some cases surpassing, the performance achieved with oracle knowledge access. These results establish DualRAG as a robust and efficient solution for complex multi-hop reasoning tasks.