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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351 papers56 papers in the last four weeks, up 70% on the four weeks before. 0.3% of all new papers.

Interference-Aware Multi-Task Unlearning
May 18, 2026Ying-Hua Huang, Rui Fang, Hsi-Wen Chen +1Gradient Projection Machine Unlearning
National Taiwan University
CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic
May 18, 2026Shen Lin, Junhao Dong, Rongjie Chen +3VLM Unlearning Vision-Language Models
1Fujian Normal University · 2Nanyang Technological University · 3Xidian University
Machine Unlearning for Masked Diffusion Language Models
May 18, 2026Georu Lee, Seungwon Jeong, Hoki Kim +2Masked Diffusion Models Diffusion Model Unlearning
1Dongguk University-Seoul · 2Chung-Ang University · 3Korea Institute for Advanced Study
Form and Function: Machine Unlearning as a Problem of Misaligned States
May 17, 2026Kennon StewartQuasi-Newton Methods Machine Unlearning
Second Street Labs, Detroit, MI, USA · Department of Statistics, University of Michigan, Ann Arbor, MI, USA
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
May 16, 2026Puning Yang, Junchi Yu, Qizhou Wang +3LLM Refusal LLM Safety
Department of Natural Language Processing, MBZUAI. · University of Oxford. · RIKEN Center for Advanced Intelligence Project. +1
ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
May 16, 2026Yujie Lin, Chengyi Yang, Zhishang Xiang +2Privacy Leakage in Language Models Model Editing
School of Informatics, Xiamen University, China · Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China · School of Film, Xiamen University, China +2
Statistical Unlearning of Distributions: A Hypothesis Testing Approach
May 15, 2026Aaradhya Pandey, Sanjeev KulkarniStatistical Learning Theory Machine Unlearning
Princeton University
BARRIER: Bounded Activation Regions for Robust Information Erasure
May 15, 2026Jan Miksa, Patryk Krukowski, Przemysław Spurek +2Machine Unlearning
Jagiellonian University IDEAS Research Institute · Jagiellonian University NASK - National Research Institute
ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
May 15, 2026Jiahui Guang, Haiyan Wang, Yingjie Zhu +4VLM Unlearning Multimodal Large Language Models
Harbin Institute of Technology, Shenzhen, China · Peng Cheng Laboratory, Shenzhen, China · The Hong Kong Polytechnic University, Hong Kong, China +1
Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution
May 14, 2026Saisab Sadhu, Pratinav Seth, Vinay Kumar SankarapuMachine Unlearning Post-Training Quantization
Lexsi Labs
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
May 14, 2026Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1Multilingual Language Model Evaluation Privacy Leakage in Language Models
GSCST, Seoul National University · Department of Artificial Intelligence, Chung-Ang University · Korean Surgical Researcher Foundation, Republic of Korea +1
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
May 14, 2026Shen Lin, Jing Lin, Junhao Dong +2VLM Unlearning MLLM Unlearning
1Fujian Normal University · 2Nanyang Technological University · University of New South Wales +1
Inference-Time Machine Unlearning via Gated Activation Redirection
May 12, 2026Vinícius Conte Turani, Otávio Parraga, João Vitor Boer Abitante +7Activation Steering Machine Unlearning
MALTA, Machine Learning Theory and Applications Lab, PUCRS, Porto Alegre, Brazil · Harvard University · Kunumi Institute, Brazil
Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning
May 12, 2026Hyeonjin Kim, Hangyeol Jung, Heechan Yun +2Disentangled Representation Learning Sparse Autoencoders
Yonsei University · KAIST
SoK: Unlearnability and Unlearning for Model Dememorization
May 12, 2026Mengying Zhang, Derui Wang, Ruoxi Sun +3Privacy-Preserving ML Machine Unlearning
RMIT University Australia · CSIRO Australia · University of Texas at Dallas USA +1
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
May 11, 2026Ahmed Mehdi Inane, Vincent Quirion, Gintare Karolina Dziugaite +1Machine Unlearning
Universit´e de Montr´eal · Mila, Quebec AI Institute · Google DeepMind
Behavioral Guarantees for Proxy-Based Unlearning
May 11, 2026Virgile Dine, Teddy FuronMachine Unlearning
Centre Inria de l’Université de Rennes France
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
May 9, 2026Jiahui Guang, Zexun Zhan, Zhenlin Xu +5VLM Evaluation VLM Unlearning
Harbin Institute of Technology, Shenzhen, China · Sichuan University, Chengdu, China · Harbin Institute of Technology, Weihai, China +3
Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
May 9, 2026Weidong Zheng, Kongyang Chen, Yuanwei Guo +1Classification Machine Unlearning
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510335, China · School of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China · Pazhou Lab, Guangzhou 510006, China +1
Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models
May 8, 2026Kaidi Jia, Yujie Lin, Chengyi Yang +2VLM Unlearning Object Hallucination in VLMs
1Xiamen University
Position: The Term "Machine Unlearning" Is Overused in LLMs
May 8, 2026Sangyeon Yoon, Yeachan Jun, Albert NoLLM Evaluation Machine Unlearning
Department of Artificial Intelligence, Yonsei University, Seoul, Korea.
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
May 8, 2026Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei +3Machine Unlearning LLM Unlearning
University of Southern California · USC Information Sciences Institute
ICU-Bench:Benchmarking Continual Unlearning in Multimodal Large Language Models
May 7, 2026Yuhang Wang, Wenjie Mei, Junkai Zhang +3Privacy-Preserving Language Models Multimodal Large Language Models
School of Computer Science and Technology Xidian University
Null Space Constrained Contrastive Visual Forgetting for MLLM Unlearning
May 7, 2026Yuhang Wang, Zhenxing Niu, Haoxuan Ji +3VLM Unlearning Multimodal Large Language Models
School of Computer Science and Technology, Xidian University · Xi’an Jiaotong University
Retain-Neutral Surrogates for Min-Max Unlearning
May 7, 2026Junhao Cai, Dohun Kim, Dowon Kim +4Machine Unlearning LLM Unlearning
Korea University
Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition
May 6, 2026Alexander VedernikovMachine Unlearning Machine Unlearning Evaluation
Helsinki, Finland
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks
May 5, 2026JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3VLM Evaluation VLM Unlearning
Department of Artificial Intelligence, Chung-Ang University · KT Corporation · Graduate School of Advanced Imaging Sciences, Multimedia and Film, Chung-Ang University
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

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Interference-Aware Multi-Task Unlearning

Ying-Hua Huang, Rui Fang, Hsi-Wen Chen, Ming-Syan Chen

National Taiwan University

Gradient Projection Machine Unlearning

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Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data. Existing work mainly focuses on single-task settings, whereas modern models often operate in multi-task setups with shared backbones, where removing supervision for one task or instance can unintentionally affect others. We introduce multi-task unlearning with two settings: full-task unlearning, which removes a target instance from all tasks, and partial-task unlearning, which removes supervision only from selected tasks. We show that shared parameters couple the forget and retain sets, causing task-level interference on non-target tasks and instance-level interference on other instances. To address this issue, we propose an interference-aware framework that combines task-aware gradient projection, which constrains updates within task-specific subspaces, with instance-level gradient orthogonalization, which reduces conflicts between forget and retain signals. Experiments on two multi-task computer vision benchmarks across five tasks show that our method achieves effective unlearning while maintaining strong generalization, reducing UIS compared with the strongest baseline by 30.3% in full-task unlearning and 52.9% in partial-task unlearning.