Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment and thus safe use. However, effective unlearning in LLMs is difficult due to the fuzzy boundary between knowledge retention and forgetting. This challenge is exacerbated by entangled parameter spaces from continuous multi-domain training, often resulting in collateral damage, especially under aggressive unlearning strategies. Furthermore, the computational overhead required to optimize State-of-the-Art (SOTA) models with billions of parameters poses an additional barrier. In this work, we present ALTER, a lightweight unlearning framework for LLMs to address both the challenges of knowledge entanglement and unlearning efficiency. ALTER operates through two phases: (I) high entropy tokens are captured and learned via the shared A matrix in LoRA, followed by (II) an asymmetric LoRA architecture that achieves a specified forgetting objective by parameter isolation and unlearning tokens within the target subdomains. Serving as a new research direction for achieving unlearning via token-level isolation in the asymmetric framework. ALTER achieves SOTA performance on TOFU, WMDP, and MUSE benchmarks with over 95% forget quality and shows minimal side effects through preserving foundational tokens. By decoupling unlearning from LLMs' billion-scale parameters, this framework delivers excellent efficiency while preserving over 90% of model utility, exceeding baseline preservation rates of 47.8-83.6%.
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications（网络系统架构与融合北京市重点实验室，北京邮电大学）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）
Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University（北京交通大学智能交通信息安全与隐私保护北京市重点实验室）；College of Computer Science and Technology, Taiyuan University of Technology（太原理工大学计算机科学与技术学院）；Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited（中国铁道科学研究院集团有限公司计算技术研究所）
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Zhejiang University（浙江大学）；Hefei University of Technology（合肥工业大学）；Institute of Fundamental and Transdisciplinary Research, Zhejiang University（浙江大学基础与交叉学科研究院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）；The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Key Laboratory of Big Data Intelligent Computing（浙江省大数据智能计算重点实验室）
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Shanxi University, China（山西大学）；Cardiff University, UK（卡地夫大学）；Singapore University of Technology and Design, Singapore（新加坡科技设计大学）；ILCC, School of Informatics, University of Edinburgh, Edinburgh, UK（爱丁堡大学信息学院ILCC）
Leveraging Machine Unlearning for Cost-Efficient Preference Alignment
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑技术大学）；Siemens AG（西门子股份公司）；University of Oxford（牛津大学）
Dissecting Fine-Tuning Unlearning in Large Language Models
International Digital Economy Academy (IDEA)（国际数字经济学院（IDEA））；South China University of Technology（华南理工大学）；Columbia University（哥伦比亚大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning
Southeast University（东南大学）；University of Sydney（悉尼大学）；Northwestern University（西北大学）；Together AI；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
Fast Exact Unlearning for In-Context Learning Data for LLMs
Department of Computer Science, University of Waterloo, Waterloo, Canada（滑铁卢大学计算机科学系）；Department of Computer Science, University of Toronto, Toronto, Canada（多伦多大学计算机科学系）；Vector Institute, Toronto, Canada（向量研究所）
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
Beijing University of Posts and Telecommunications（北京邮电大学）；Huazhong University of Science and Technology（华中科技大学）；Shandong University（山东大学）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（神经网络计划、人工智能研究所、中国科学院自动化研究所）
DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs
Universitat Rovira i Virgili, Department of Computer Engineering（罗维拉-维格利大学计算机工程系）；Mathematics, CYBERCAT-Center for Cybersecurity Research of Catalonia（数学，加泰罗尼亚网络安全研究中心）
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Stony Brook University（石英布鲁克大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Columbia University（哥伦比亚大学）；Institute of Science and Technology Austria（奥地利科学技术研究院）；Sapienza University of Rome（罗马大学 sapienza）；National University of Singapore（新加坡国立大学）
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机科学学院）；Shandong University（山东大学）；New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
针对 LLM 遗忘受知识表达形式制约的问题，本文提出 ORT 基准量化形式依赖偏差，并给出免训练的 ROCR 方法，通过秩一概念重定向实现更有效且自然的遗忘。
Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Glasgow（格拉斯哥大学）
State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）；Mila – Québec AI Institute（米拉魁北克人工智能研究所）；McGill University（麦吉尔大学）；RIKEN AIP（日本理化学研究所先进智能研究中心）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Google Research（谷歌研究院）
Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
