[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85833-en":3,"doc-seo-85833-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85833,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","UNIT: Unleash Large Language Models Potential for Graph Continual Learning","Real-world multimodal web streams often deliver graph-structured data incrementally, making graph continual learning essential for modeling evolving structures. Existing methods face two key issues: semantic–structural separation, which underutilizes deep semantics, and imbalanced knowledge transfer, which fails to reuse general knowledge from earlier tasks. UNIT fine-tunes a large language model only on the first task and introduces uncertain-aware anchor generation and structural confluence modeling to preserve transferable knowledge and align topology with semantics, achieving state-of-the-art results.","UNIT: Unleash Large Language Models Potential for Graph  \nContinual Learning  \nTairan Huang  \n[tairanhuang@csu.edu.cn](tairanhuang@csu.edu.cn)[ ](tairanhuang@csu.edu.cn)Central South University Changsha, China  \nYili Wang  \n[yili.wang@connect.hkust-gz.edu.cn](yili.wang@connect.hkust-gz.edu.cn)[ ](yili.wang@connect.hkust-gz.edu.cn)Hongkong University of Science and Technology (Guangzhou) Guangzhou, China  \nBeibei Hu  \n[mindyhbb@gmail.com](mindyhbb@gmail.com)[ ](mindyhbb@gmail.com)Hunan Institute of Engineering Changsha, Hunan  \nYiting Shi  \n[shiyiting@csu.edu.cn](shiyiting@csu.edu.cn)[ ](shiyiting@csu.edu.cn)Central South University Changsha, China  \nQiutong Li  \n[qiutonglee@csu.edu.cn](qiutonglee@csu.edu.cn)[ ](qiutonglee@csu.edu.cn)Central South University Changsha, China  \nChanglong He  \n[frankemail@csu.edu.cn](frankemail@csu.edu.cn)[ ](frankemail@csu.edu.cn)Central South University Changsha, China  \narXiv :2607 . 10 159v 1 [ cs .AI] 11 Jul 2026  \nJianliang Gao∗ [gaojianliang@csu.edu.cn](gaojianliang@csu.edu.cn)[ ](gaojianliang@csu.edu.cn)Central South University Changsha, China  \nAbstract  \nIn real-world multimodal web scenarios, graph-structured data often arrives in a streaming manner, making graph continual learning a crucial paradigm for continuously modeling such evolving structures. However, existing graph continual learning methods still face two fundamental challenges. 1) semantic-structural separation, where the graph-based methods excel at modeling topological relationships but neglect deep semantics. 2) imbalanced knowledge transfer, where existing models fail to effectively leverage general knowledge gained from early tasks to benefit subsequent new tasks. To address above issues, we propose a novel framework, UNleash Large Language Models PotentIal for Graph ConTinual Learning (UNIT) . By fine-tuning large language model only on the first task, we bridge the distributional gap between the pre-trained LLM corpus and the target task dataset to enhance the adaptability of LLMs for graph-structured tasks. Meanwhile, we propose an uncertain-aware anchor generation mechanism to effectively preserve representative knowledge across tasks, avoiding the neglect of universal knowledge learned from previous tasks. Additionally, we introduce structural confluence modeling to explicitly integrates graph topology information into semantic information, enhancing the collaborative capabilities between semantic understanding and structural modeling. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance in the graph continual learning task.  \n∗ Corresponding author.  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nConference ACM MM’26, Rio de Janeiro, Brazil  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/2018/06  \n[https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \nCCS Concepts  \n• Computing methodologies → Machine learning; • Information systems → Social networks; Data mining.  \nKeywords  \nMultimodal Fusion; Continual Learning; Large Language Models; Text-attributed Graphs  \nACM Reference Format:  \nTairan Huang, Yili Wang, Beibei Hu, Yiting Shi, Qiutong Li, Changlong He, and Jianliang Gao. 2026. UNIT: Unleash Large Language Models Potential for Graph Continual Learning. 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