@inproceedings{luo-etal-2026-graph,
title = "Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations",
author = "Luo, Haitong and
Wang, Fali and
Zhang, Weiyao and
Zhang, Xianren and
Zhang, Zhiwei and
Zhao, Tianxiang and
Lin, Minhua and
Zhang, Jiahao and
Liu, Hui and
Tang, Xianfeng and
He, Qi and
Wang, Suhang and
Meng, Xuying and
Zhang, Yujun",
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.945/",
pages = "18936--18955",
ISBN = "979-8-89176-395-1",
abstract = "Large language models (LLMs) have made progress in knowledge-intensive tasks, reasoning and planning, and collaborative problem solving, yet they exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. Graphs, with their ability to represent relational knowledge and complex dependencies, offer a natural means to address these limitations: they provide structured, high-density knowledge for augmenting or correcting LLMs' generation; enable revisitable inference by organizing intermediate steps as graphs; and support dynamic coordination among experts or agents in collaborative settings. Motivated by these developments, we present the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs' limitations. We introduce a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph-Assisted Reasoning and Planning*, and *Graph-Assisted LLM Collaboration*, and analyze representative methods, summarize common design patterns, and outline open challenges and future directions for advancing LLMs with graph-based enhancements. The collected papers are available in [link here](https://github.com/FairyFali/Graph4LLM-Survey)."
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<abstract>Large language models (LLMs) have made progress in knowledge-intensive tasks, reasoning and planning, and collaborative problem solving, yet they exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. Graphs, with their ability to represent relational knowledge and complex dependencies, offer a natural means to address these limitations: they provide structured, high-density knowledge for augmenting or correcting LLMs’ generation; enable revisitable inference by organizing intermediate steps as graphs; and support dynamic coordination among experts or agents in collaborative settings. Motivated by these developments, we present the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs’ limitations. We introduce a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph-Assisted Reasoning and Planning*, and *Graph-Assisted LLM Collaboration*, and analyze representative methods, summarize common design patterns, and outline open challenges and future directions for advancing LLMs with graph-based enhancements. The collected papers are available in [link here](https://github.com/FairyFali/Graph4LLM-Survey).</abstract>
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%0 Conference Proceedings
%T Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations
%A Luo, Haitong
%A Wang, Fali
%A Zhang, Weiyao
%A Zhang, Xianren
%A Zhang, Zhiwei
%A Zhao, Tianxiang
%A Lin, Minhua
%A Zhang, Jiahao
%A Liu, Hui
%A Tang, Xianfeng
%A He, Qi
%A Wang, Suhang
%A Meng, Xuying
%A Zhang, Yujun
%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 luo-etal-2026-graph
%X Large language models (LLMs) have made progress in knowledge-intensive tasks, reasoning and planning, and collaborative problem solving, yet they exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. Graphs, with their ability to represent relational knowledge and complex dependencies, offer a natural means to address these limitations: they provide structured, high-density knowledge for augmenting or correcting LLMs’ generation; enable revisitable inference by organizing intermediate steps as graphs; and support dynamic coordination among experts or agents in collaborative settings. Motivated by these developments, we present the first systematic survey of graph-assisted LLMs from the perspective of how graph structures mitigate LLMs’ limitations. We introduce a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph-Assisted Reasoning and Planning*, and *Graph-Assisted LLM Collaboration*, and analyze representative methods, summarize common design patterns, and outline open challenges and future directions for advancing LLMs with graph-based enhancements. The collected papers are available in [link here](https://github.com/FairyFali/Graph4LLM-Survey).
%U https://aclanthology.org/2026.findings-acl.945/
%P 18936-18955
Markdown (Informal)
[Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations](https://aclanthology.org/2026.findings-acl.945/) (Luo et al., Findings 2026)
ACL
- Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, and Yujun Zhang. 2026. Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 18936–18955, San Diego, California, United States. Association for Computational Linguistics.