Machine Unlearning

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Machine unlearning asks how to remove a person’s data, a class, or a learned concept from a model after training, often to support privacy, safety, or changing data rights. Approaches range from exact or certified deletion methods, such as data partitioning and noisy or influence-based updates, to approximate parameter edits, representation changes, and inference-time steering; some systems are designed from the outset to make deletion easier. Researchers evaluate methods against retrained models and test forgetting, retained utility, privacy attacks, recovery, and repeated deletion across language, vision, diffusion, graph, and multimodal systems. A central tension is that suppressed outputs do not necessarily mean information is erased, while stronger removal can damage related knowledge; scalable methods with reliable guarantees and low collateral harm remain a challenge.

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354 papers58 papers in the last four weeks, up 76% on the four weeks before. 0.3% of all new papers.

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
May 5, 2026JuneHyoung Kwon, JungMin Yun, YoungBin KimVLM Evaluation VLM Unlearning
Department of Artificial Intelligence, Chung-Ang University · Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
May 5, 2026Jingjing Zhou, Yongshuai Yang, Qing Qing +5Graph Unlearning Privacy-Preserving ML
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China · College of Computer Science and Technology, Jilin University, Changchun 130012, China · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia +1
Graph Federated Unlearning for Privacy Preservation
May 4, 2026Ruotong Ma, Wentao Yu, Qizhou Wang +2Federated Unlearning Federated Graph Learning
Shanghai Jiao Tong University · Nanjing University of Science and Technology · Hong Kong Baptist University China
Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score
May 4, 2026Abdullah Ahmad Khan, Hamid Laga, Ferdous SohelVLM Evaluation VLM Unlearning
Murdoch University
DurableUn: Quantization-Induced Recovery Attacks in Machine Unlearning
May 4, 2026Abdullah Ahmad Khan, Ferdous SohelQuantization-Aware Training 4-Bit Quantization
Murdoch University
Adversarial Update-Based Federated Unlearning for Poisoned Model Recovery
May 4, 2026Wenwei Zhao, Xiaowen Li, Yao Liu +1Federated Unlearning Machine Unlearning
Department of Electrical Engineering, University of South Florida,Tampa, FL, USA · Bellini College, University of South Florida, Tampa, FL, USA
Less is More: Geometric Unlearning for LLMs with Minimal Data Disclosure
May 3, 2026Chenchen Tan, Xinghao Li, Shujie Cui +3Privacy-Preserving Language Models Machine Unlearning
Faculty of Information Technology, Monash University, Clayton, Victoria, Australia. · Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China. · Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China.
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
May 1, 2026Zihao Ding, Beining Wu, Jun HuangCross-Modal Learning Multimodal Federated Learning
Department of Electrical Engineering and Computer Science South Dakota State University Brookings, SD 57007, USA
Unlearning Offline Stochastic Multi-Armed Bandits
May 1, 2026Zichun Ye, Runqi Wang, Xuchuang Wang +3Multi-Armed Bandits Robust Multi-Armed Bandits
Shanghai Jiao Tong University, Shanghai, China · University of Massachusetts Amherst, Massachusetts, United States · University of Washington, Tacoma, United States
Network Digital Untwinning: Towards Backward Optimization of Digital Twins
Apr 30, 2026Zifan Zhang, Dianwei Chen, Anjun Gao +5Digital Twins Machine Unlearning
North Carolina State University, USA · University of Maryland, USA · University of Louisville, USA +2
Machine Unlearning for Class Removal through SISA-based Deep Neural Network Architectures
Apr 30, 2026Ishrak Hamim Mahi, Siam Ferdous, Md Sakib Sadman Badhon +4Privacy-Preserving ML Machine Unlearning
Department of Computer Science and Engineering Brac University Dhaka, Bangladesh
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
Apr 29, 2026Zhaoyuan Cai, Xinglin ZhangFederated Unlearning Medical Imaging
School of Computer Science and Engineering South China University of Technology Guangzhou, China
Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification
Apr 26, 2026Andreza M. C. Falcao, Filipe R. CordeiroMedical Image Classification Machine Unlearning
Visual Computing Lab, Departamento de Computacao, Universidade Federal Rural de Pernambuco (UFRPE), Brasil
Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers
Apr 24, 2026Kennon StewartSecond-Order Optimization Machine Unlearning
Second Street Labs, Detroit, MI, USA · Department of Statistics, University of Michigan, Ann Arbor, MI, USA
PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning
Apr 23, 2026Xiaoyi Chen, Haoyuan Wang, Siyuan Tang +4LLM Auditing Privacy Leakage in Language Models
Indiana University Bloomington · Independent Researcher · Michigan State University +2
Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks
Apr 22, 2026Aljalila Aladawi, Mohammed Talha Alam, Fakhri KarrayDiffusion Model Fine-Tuning Diffusion Model Unlearning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE. · University of Waterloo, Ontario, Canada.
ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval
Apr 22, 2026Zixu Li, Yupeng Hu, Zhiwei Chen +3Learning with Noisy Labels Machine Unlearning
1Shandong University · 2Harbin Institute of Technology (Shenzhen)
Robust Continual Unlearning against Knowledge Erosion and Forgetting Reversal
Apr 21, 2026Eun-Ju Park, Youjin Shin, Simon S. WooMachine Unlearning Catastrophic Forgetting
1Sungkyunkwan University · 2The Catholic University of Korea · 3Secure Machines Lab
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens
Apr 20, 2026Seunghee Koh, Sunghyun Baek, Youngdong Kim +1Machine Unlearning LLM Unlearning
Korea Advanced Institute of Science and Technology, South Korea · Hanbat National University, South Korea
Representation-Guided Parameter-Efficient LLM Unlearning
Apr 19, 2026Zeguan Xiao, Lang Mo, Yun Chen +4Machine Unlearning LLM Unlearning
Shanghai University of Finance and Economics · Southern University of Science and Technology · Deepexi Technology Co. Ltd.
Randomized Antipodal Search Done Right for Data Pareto Improvement of LLM Unlearning
Apr 17, 2026Ziwen Liu, Huawei Lin, Yide Ran +5Machine Unlearning LLM Unlearning
Rice University · Rochester Institute of Technology · Stevens Institute of Technology +1
CiPO: Counterfactual Unlearning for Large Reasoning Models through Iterative Preference Optimization
Apr 17, 2026Junyi Li, Yongqiang Chen, Ningning DingMachine Unlearning LLM Unlearning
The Hong Kong University of Science and Technology (Guangzhou) · The Chinese University of Hong Kong
Towards Reliable Testing of Machine Unlearning
Apr 16, 2026Anna Mazhar, Sainyam GalhotraData Leakage Automated Software Testing
Cornell University Ithaca, NY, USA
Class Unlearning via Depth-Aware Removal of Forget-Specific Directions
Apr 16, 2026Arman Hatami, Romina Aalishah, Ilya E. MonosovMachine Unlearning
Johns Hopkins University
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Apr 16, 2026Zeguan Xiao, Siqing Li, Yong Wang +4Gradient Interference Multi-Task Learning
1Shanghai University of Finance and Economics · 2Southern University of Science and Technology · 3Alibaba Group +2
From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models
Apr 15, 2026Wenxuan Li, Zhenfei Zhang, Mi Zhang +4LLM Unlearning Machine Unlearning
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Shanghai University of Electric Power, Shanghai, China · School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework
Apr 15, 2026Xingjian Zhao, Mohammad Mohammadi Amiri, Malik Magdon-IsmailMachine Unlearning LLM Unlearning
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair
Apr 14, 2026Jagadeesh Rachapudi, Pranav Singh, Ritali Vatsi +2Machine Unlearning LLM Unlearning
Indian Institute of Technology Mandi, Mandi, India
BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning
Apr 14, 2026Jagadeesh Rachapudi, Ritali Vatsi, Praful Hambarde +1Continual Learning Machine Unlearning
Indian Institute of Technology Mandi, Mandi, India
Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
Apr 14, 2026Yogachandran Rahulamathavan, Nasir Iqbal, Juncheng Hu +1Machine Unlearning Low-Rank Adaptation
Institute for Digital Technologies, Loughborough University, UK · School of Artificial Intelligent Medicine, Guilin Medical University, Guilin 541199, CN

May 5, 2026J/K move · Enter open · S save

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

JuneHyoung Kwon, JungMin Yun, YoungBin Kim

Department of Artificial Intelligence, Chung-Ang University · Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University

VLM Evaluation VLM Unlearning MLLM Unlearning Machine Unlearning

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Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logos, creating significant challenges. Machine unlearning offers a path to mitigate these risks by removing specific content post-training, but evaluating its effectiveness, especially in the complex multimodal setting of LVLMs, remains an open problem. Current evaluation methods often lack robustness or fail to capture the nuances of cross-modal concept erasure. To address this critical gap, we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations spanning compositional changes and diverse domain manifestations to ensure realistic and robust evaluation of unlearning generalization. Our comprehensive multimodal evaluation protocol assesses both forgetting efficacy from the copyright holder perspective and the preservation of general model utility from the deployer viewpoint. By rigorously measuring this crucial trade-off, CoVUBench provides a standardized tool to advance the development of responsible and effective unlearning methods for LVLMs.