大型语言模型的机器遗忘：基础、进展与智能体扩展

Computer Science > Cryptography and Security arXiv:2609.30909 (cs) [Submitted on 25 Sep 2026] Title:Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions Authors:Xiaoyu Xu, Minxin Du, Li Bai, Junxu Liu, Yaxin Xiao, Kun Fang, Liu Yang, Huadi Zheng, Peizhao Hu, Qingqing Ye, Haibo Hu View a PDF of the paper titled Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions, by Xiaoyu Xu and 10 other authors View PDF HTML (experimental) Abstract:Machine unlearning aims to remove target influence while preserving other capabilities. This survey compares methods, benchmarks, and evidence across large language models and systems using retrieval, memory, tools, and interacting agents. A five-layer framework connects removal requests, system boundaries, target locations, interventions, and supported claims. A seven-stage lifecycle and six evidence dimensions guide comparison. The review shows that target construction, retained data, and recovery tests affect reported outcomes. Evidence from model evaluations remains insufficient to establish removal across external state and subsequent updates, motivating evaluation that tracks dependencies and tests whether target influence returns. Comments: Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2609.30909 [cs.CR] (or arXiv:2609.30909v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.30909 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Minxin Du [view email] [v1] Fri, 25 Sep 2026 07:17:20 UTC (33,622 KB) Full-text links: Access Paper: View a PDF of the paper titled Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions, by Xiaoyu Xu and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)