@inproceedings{deng-etal-2026-panoramarag,
title = "{P}anorama{RAG}: Enabling Consistent Global Topic Awareness in Graph-Based {RAG}",
author = "Deng, Ding and
Li, Xiang and
Zhang, Yaqing and
Li, Meng and
Wang, Xiting",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1998/",
pages = "40205--40217",
ISBN = "979-8-89176-395-1",
abstract = "Graph-based Retrieval-Augmented Generation (RAG), which models relationships between fine-grained semantic units as a graph, effectively facilitates multi-hop reasoning to enhance large language model generation. However, its design focuses on local relationships, resulting in suboptimal performance for tasks that require global context, and the separation of query refinement from indexing limits the system{'}s ability to capture high-level implicit relationships within the graph. This paper proposes a **Panorama**-guided **RAG** paradigm (PanoramaRAG) that integrates a light yet comprehensive ``panorama'' of the corpus to guide all stages of the retrieval process. This hub bridges the knowledge graph, language models, and queries in a computationally efficient manner, applicable to both open-source and closed-source models. Experimental results demonstrate that our method exhibits strong performance across five datasets and a variety of tasks."
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%0 Conference Proceedings
%T PanoramaRAG: Enabling Consistent Global Topic Awareness in Graph-Based RAG
%A Deng, Ding
%A Li, Xiang
%A Zhang, Yaqing
%A Li, Meng
%A Wang, Xiting
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F deng-etal-2026-panoramarag
%X Graph-based Retrieval-Augmented Generation (RAG), which models relationships between fine-grained semantic units as a graph, effectively facilitates multi-hop reasoning to enhance large language model generation. However, its design focuses on local relationships, resulting in suboptimal performance for tasks that require global context, and the separation of query refinement from indexing limits the system’s ability to capture high-level implicit relationships within the graph. This paper proposes a **Panorama**-guided **RAG** paradigm (PanoramaRAG) that integrates a light yet comprehensive “panorama” of the corpus to guide all stages of the retrieval process. This hub bridges the knowledge graph, language models, and queries in a computationally efficient manner, applicable to both open-source and closed-source models. Experimental results demonstrate that our method exhibits strong performance across five datasets and a variety of tasks.
%U https://aclanthology.org/2026.findings-acl.1998/
%P 40205-40217
Markdown (Informal)
[PanoramaRAG: Enabling Consistent Global Topic Awareness in Graph-Based RAG](https://aclanthology.org/2026.findings-acl.1998/) (Deng et al., Findings 2026)
ACL