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.

LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Jul 2, 2026Matteo Boglioni, Thibault Rousset, Siva Reddy +2Privacy Leakage in Language Models Machine Unlearning
Mila – Quebec Artificial Intelligence Institute · McGill University · McGill-NLP/LACUNA
DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation
Jun 30, 2026Héctor Laria, Yiping Han, Julian D. Santamaria +4Diffusion Model Adaptation Diffusion Model Unlearning
Computer Vision Center, Barcelona, Spain · Universitat Autonoma de Barcelona, Barcelona, Spain · Program of Computer Science, City University of Hong Kong (Dongguan), China +1
Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models
Jun 30, 2026Enrico Cassano, Riccardo Renzulli, Rayyan Ahmed +2Sparse Autoencoders Diffusion Model Unlearning
University of Turin, Computer Science Department, Italy · LTCI, Télécom Paris, Institut Polytechnique de Paris, France
IREU: Identity-Related Encoder-Only Unlearning for Customized Portrait Generation
Jun 29, 2026Chaoyi Shi, Shanshan Zhang, Jian YangSubject-Driven Image Generation Machine Unlearning
PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology · PCA Lab, School of Intelligence Science and Technology, Nanjing University
The Forgetting-Retention Dilemma: Certified Unlearning Theory in Continual Learning
Jun 29, 2026Yiting Hu, Lingjie Duan, Qian ZhangContinual Learning Statistical Learning Theory
Singapore University of Technology and Design, Singapore · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.
CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence
Jun 26, 2026Zhiqiang Xie, Yijing Lin, Zhipeng Gao +1Machine Unlearning LLM Unlearning
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing · Department of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea
DFMU: Data-Frugal Machine Unlearning
Jun 24, 2026Sajith U, Prateek KeserwaniMachine Unlearning
Samsung R&D Institute India-Bangalore, India
Erased, but Not Gone: Output Forgetting Is Not True Forgetting
Jun 23, 2026Teresa Pui Yee Yong, Win Kent Ong, Chee Seng ChanMachine Unlearning Machine Unlearning Evaluation
Universiti Malaya, Kuala Lumpur, Malaysia · VinUniversity, Hanoi, Vietnam
Co-occurring associated retained concepts in Diffusion Unlearning
Jun 23, 2026Miso Kim, Georu Lee, Yunji Kim +3Diffusion Model Unlearning Concept Erasure
Dongguk University-Seoul · Chung-Ang University · Korea Institute for Advanced Study
FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters
Jun 23, 2026Feihong Nan, Zhengyi Zhong, Pan Wang +4Federated Unlearning Machine Unlearning
1National Key Laboratory of Big Data and Decision, National University of Defense Technology, China.
Selective Capability Unlearning in End-to-End Spoken Language Understanding
Jun 23, 2026Akanksha Singh, Vinod Kumar KurmiSpoken Language Understanding Machine Unlearning
Indian Institute of Science Education and Research Bhopal, India
GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
Jun 17, 2026Nanhong Liu, Mucun Sun, Jie ZhangGraph Neural Networks Graph Unlearning
Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA · Idaho National Laboratory, Idaho Falls, ID 83415, USA · Department of Mechanical Engineering and (Affilated) Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Mechanism-Guided Selective Unlearning for RLVR-Induced Reasoning
Jun 17, 2026Chenyu Zhou, Qiliang Jiang, Shuning Wu +1Machine Unlearning LLM Unlearning
School of Engineering, Institute of Science Tokyo, Japan · College of Control Science and Engineering, Zhejiang University, China · Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
Jun 17, 2026Diana Magalhães, Eva Maia, João Vitorino +1Machine Unlearning
GECAD, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Jun 16, 2026Bo Su, Ankit Shah, Thai LeLLM Auditing Machine Unlearning
Indiana University Bloomington, IN, USA
SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
Jun 16, 2026Jingyuan Zhang, Yucheng Bai, Peixi Wen +6Machine Unlearning LLM Unlearning
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University
RepSelect: Robust LLM Unlearning via Representation Selectivity
Jun 15, 2026Filip Sondej, Yushi Yang, Adam MahdiLLM Safety Representation Geometry in Language Models
Independent · University of Oxford
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
Jun 15, 2026Rutger Hendrix, Leonardo G. Russo, Concetto Spampinato +2Privacy-Preserving ML Machine Unlearning
University of Catania Catania, Italy
To forget is to preserve: Machine Unlearning for 3D medical image segmentation
Jun 15, 2026Nitesh Kumar Singh, Akhilesh Singh, Arjun AroraPrivacy-Preserving ML 3D Medical Image Segmentation
School of Computer Science UPES, University of Tomorrow Dehradun, India.
Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
Jun 15, 2026Dayong Ye, Tianqing Zhu, Ruiding Huang +5Machine Unlearning LLM Unlearning
InstantForget: Update-Free Backdoor Unlearning with Inference-Time Feature Reset
Jun 14, 2026Zhenyu YuBackdoor Defense Machine Unlearning
College of Computer Science and Artificial Intelligence Fudan University
Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning
Jun 13, 2026Zirui Pang, Chenlong Zhang, Haosheng Tan +3Experience Replay Machine Unlearning
The Hong Kong University of Science and Technology (Guangzhou) · Institute of Automation, Chinese Academy of Sciences · University of Glasgow
Behavioral Audit of Machine Unlearning Has a Privacy Cost
Jun 12, 2026Liou Tang, James Joshi, Ashish KunduPrivacy Auditing Machine Unlearning
University of Pittsburgh, Pittsburgh, PA, USA · Cisco Research, San Jose, CA, USA
Rethinking Backdoor Adversarial Unlearning through the Lens of Catastrophic Forgetting in Continual Learning
Jun 12, 2026Zhenqian Zhu, Yamin Hu, Yujiang Liu +5Backdoor Defense Machine Unlearning
Harbin Institute of Technology, Shenzhen Shenzhen, Guangdong, China · Shenzhen Key Laboratory of Media Security, Shenzhen University Shenzhen, Guangdong, China
Natively Unlearnable Large Language Models
Jun 11, 2026Gaurav R. Ghosal, Pratyush Maini, Aditi RaghunathanActivation Sparsity Machine Unlearning
Carnegie Mellon University · DatologyAI
Reinforcement Learning for Neural Model Editing
Jun 11, 2026Shaivi MalikReinforcement Learning Model Editing
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
Jun 11, 2026He Li, Haoang Chi, Qizhou Wang +6VLM Unlearning Multimodal Large Language Models
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · TMLR Group, Hong Kong Baptist University, Hong Kong, China · School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai, China +2
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
Jun 9, 2026Jingyi Xie, Yijun Lin, Yinjiang Xiong +2Mixture-of-Experts Language Models Machine Unlearning
Renmin University of China · Lightstandard · Zhejiang University +1
REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
Jun 5, 2026Anurag Sharma, Sai Teja Chunchu, Prasenjit Mitra +2Multi-Label Classification Machine Unlearning
IIT Kharagpur · India · Carnegie Mellon University +4
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Jun 5, 2026Ziheng Chen, Jiali Cheng, Zezhong Fan +4Sequential Recommendation Concept Erasure
Stony Brook University · University of Massachusetts Lowell · Columbia University +3

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LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning

Matteo Boglioni, Thibault Rousset, Siva Reddy, Marius Mosbach+1

Mila – Quebec Artificial Intelligence Institute · McGill University · McGill-NLP/LACUNA

Privacy Leakage in Language Models Machine Unlearning LLM Unlearning Machine Unlearning Evaluation

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LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc removal methods. Unlearning has emerged as a promising solution, with state-of-the-art(SOTA) methods often following a localize-first, unlearn-second paradigm that targets specific model parameters. However, existing benchmarks evaluate unlearning solely at the output level, leaving open the question of whether unlearning truly erases knowledge from a model's parameters or merely obfuscates it, a concern reinforced by the success of resurfacing attacks. To bridge this gap, we introduce LACUNA: the first unlearning testbed with ground-truth parameter-level localization. LACUNA injects PII of synthetic individuals into predefined parameters of 1B and 7B OLMo-based models via masked continual pretraining, enabling direct evaluation of whether unlearning targets the weights responsible for knowledge storage. We use LACUNA to benchmark current SOTA unlearning methods and find that, despite strong output-level performance, existing methods are highly imprecise and susceptible to resurfacing attacks. We further show that when localization is successful, even a simple gradient-based unlearning method achieves strong erasure and robustness to resurfacing attacks, highlighting the importance of precise unlearning. We release LACUNA to complement behavioral evaluations and drive further advances in robust, localization-based unlearning.