@inproceedings{li-etal-2025-survey,
title = "A Survey of {RAG}-Reasoning Systems in Large Language Models",
author = "Li, Yangning and
Zhang, Weizhi and
Yang, Yuyao and
Huang, Wei-Chieh and
Wu, Yaozu and
Luo, Junyu and
Bei, Yuanchen and
Zou, Henry Peng and
Luo, Xiao and
Zhao, Yusheng and
Chan, Chunkit and
Chen, Yankai and
Deng, Zhongfen and
Li, Yinghui and
Zheng, Hai-Tao and
Li, Dongyuan and
Jiang, Renhe and
Zhang, Ming and
Song, Yangqiu and
Yu, Philip S.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.648/",
pages = "12120--12145",
ISBN = "979-8-89176-335-7",
abstract = "Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-search perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and thought to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric."
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<abstract>Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-search perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and thought to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric.</abstract>
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%0 Conference Proceedings
%T A Survey of RAG-Reasoning Systems in Large Language Models
%A Li, Yangning
%A Zhang, Weizhi
%A Yang, Yuyao
%A Huang, Wei-Chieh
%A Wu, Yaozu
%A Luo, Junyu
%A Bei, Yuanchen
%A Zou, Henry Peng
%A Luo, Xiao
%A Zhao, Yusheng
%A Chan, Chunkit
%A Chen, Yankai
%A Deng, Zhongfen
%A Li, Yinghui
%A Zheng, Hai-Tao
%A Li, Dongyuan
%A Jiang, Renhe
%A Zhang, Ming
%A Song, Yangqiu
%A Yu, Philip S.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-survey
%X Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-search perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and thought to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric.
%U https://aclanthology.org/2025.findings-emnlp.648/
%P 12120-12145
Markdown (Informal)
[A Survey of RAG-Reasoning Systems in Large Language Models](https://aclanthology.org/2025.findings-emnlp.648/) (Li et al., Findings 2025)
ACL
- Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, and Philip S. Yu. 2025. A Survey of RAG-Reasoning Systems in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12120–12145, Suzhou, China. Association for Computational Linguistics.