@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://preview.aclanthology.org/ingest-acl/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)."
}