@inproceedings{dong-etal-2026-retrieval,
title = "From Retrieval-Augmented Generation ({RAG}) to Agentic Deep Research",
author = "Dong, Xin Luna and
Sharma, Sanat and
Sun, Kai and
Wang, Jiaqi and
Xia, Yinglong and
Yang, Xiao and
Yih, Scott Wen-tau and
Tang, Nan and
Zhang, Franklin",
editor = "Okazaki, Naoaki and
Wang, Zhongqing",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics: Tutorial Abstracts",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-tutorials.5/",
pages = "32--43",
ISBN = "979-8-89176-443-9",
abstract = "Despite well-known hallucination issues, LLMs have become an increasingly indispensable source of information, and the underlying technology has advanced rapidly to make their answers far more reliable. Early on, \textit{Retrieval-Augmented Generation (RAG)} emerged as the dominant remedy, grounding LLM responses in external knowledge and evolving from simple retrieve-then-read pipelines into modular, graph-enhanced, and agentic systems. More recently, \textit{Agentic Deep Research} has pushed the frontier further, equipping LLMs with autonomous planning, multi-hop investigation, and iterative synthesis to tackle open-ended questions that no single retrieval pass can answer. This tutorial offers an in-depth treatment of modern RAG and Deep Research, grounded in an AI-assisted systematic analysis of 2,000+ recent papers (2020{--}2026). Attendees will leave with a structured roadmap, evidence-backed practical recommendations, and a clear map of open research opportunities."
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%0 Conference Proceedings
%T From Retrieval-Augmented Generation (RAG) to Agentic Deep Research
%A Dong, Xin Luna
%A Sharma, Sanat
%A Sun, Kai
%A Wang, Jiaqi
%A Xia, Yinglong
%A Yang, Xiao
%A Yih, Scott Wen-tau
%A Tang, Nan
%A Zhang, Franklin
%Y Okazaki, Naoaki
%Y Wang, Zhongqing
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Tutorial Abstracts
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-443-9
%F dong-etal-2026-retrieval
%X Despite well-known hallucination issues, LLMs have become an increasingly indispensable source of information, and the underlying technology has advanced rapidly to make their answers far more reliable. Early on, Retrieval-Augmented Generation (RAG) emerged as the dominant remedy, grounding LLM responses in external knowledge and evolving from simple retrieve-then-read pipelines into modular, graph-enhanced, and agentic systems. More recently, Agentic Deep Research has pushed the frontier further, equipping LLMs with autonomous planning, multi-hop investigation, and iterative synthesis to tackle open-ended questions that no single retrieval pass can answer. This tutorial offers an in-depth treatment of modern RAG and Deep Research, grounded in an AI-assisted systematic analysis of 2,000+ recent papers (2020–2026). Attendees will leave with a structured roadmap, evidence-backed practical recommendations, and a clear map of open research opportunities.
%U https://aclanthology.org/2026.aacl-tutorials.5/
%P 32-43
Markdown (Informal)
[From Retrieval-Augmented Generation (RAG) to Agentic Deep Research](https://aclanthology.org/2026.aacl-tutorials.5/) (Dong et al., AACL-IJCNLP 2026)
ACL
- Xin Luna Dong, Sanat Sharma, Kai Sun, Jiaqi Wang, Yinglong Xia, Xiao Yang, Scott Wen-tau Yih, Nan Tang, and Franklin Zhang. 2026. From Retrieval-Augmented Generation (RAG) to Agentic Deep Research. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Tutorial Abstracts, pages 32–43, Hengqin, China. Association for Computational Linguistics.