Machine Unlearning Evaluation

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8 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning
Oct 8, 2026Xunlei Chen, Qinghui Gong, Jingkun Xue +3Machine Unlearning LLM Unlearning
University of Electronic Science and Technology of China · Southwest Jiaotong University
Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning
Oct 7, 2026Junkai Chen, Yuhao He, Qianshan Wei +19MLLM Unlearning Machine Unlearning Evaluation
Institute of Automation, Chinese Academy of Sciences
Exact Unlearning via Quantized Sufficient Statistics
Oct 5, 2026Ami Tavory, Shripad Gade, Tal Sarig +2Machine Unlearning Machine Unlearning Evaluation
Meta Platforms, Inc.
Your Unlearning Gives You Away: Identifying Erased Concepts in Diffusion Models
Oct 4, 2026Kaiyuan Deng, Yuchen Li, Yang Xiao +3Diffusion Model Unlearning Concept Erasure
The University of Arizona · Peking University · The University of Tulsa +1
Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning
Sep 30, 2026Tyler Skow, Shravan Chaudhari, Rama Chellappa +1Machine Unlearning LLM Unlearning
Johns Hopkins University
Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning
Sep 28, 2026Can Polat, Mustafa Kurban, Erchin Serpedin +1Machine Unlearning Machine Unlearning Evaluation
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA · Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar · Department of Prosthetics and Orthotics, Ankara University, Ankara, Turkey +1
eval-unlearn: Benchmarking unlearning in Text-to-Image Diffusion Models
Sep 28, 2026Mansi, Nikhil Raghavan, Zixia Huang +4Diffusion Model Unlearning T2I Generation Evaluation
Imperial College London
Evaluating Machine Unlearning in ASR
Sep 28, 2026Diogo Dinis, Francisco Teixeira, Bhiksha Raj +2Automatic Speech Recognition Machine Unlearning
INESC-ID · Instituto Superior T´ecnico, Universidade de Lisboa · LTI, Carnegie Mellon University
Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning
Sep 21, 2026Donghyun Kim, Taehyuk Lee, Jinyeong Kim +4Memorization in Generative Models Diffusion Model Unlearning
Department of Computer Science and Engineering, Konkuk University Trustworthy Machine Learning Lab Seoul, Republic of Korea
Do Influence-Derived Data Perturbations Enable Machine Unlearning? A Controlled Study of Three Plausible Roles
Sep 14, 2026Chenkai Wu, Chrispine Kambimbi, Qinyang Zeng +1Machine Unlearning Machine Unlearning Evaluation
Monash University · LibrAI · Tongji University +1
K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments
Sep 11, 2026Guangsheng Yu, Yanna Jiang, Qin Wang +2LLM Agent Evaluation Privacy Leakage in Language Models
University of Technology Sydney, Australia · CSIRO, Australia
Forget or Fine-tune? A Comparative Study of Machine Unlearning Strategies for Noisy Label Correction
Aug 30, 2026João L. P. Santana, Filipe R. CordeiroNoisy-Label Learning Classification
Department of Computing Universidade Federal Rural de Pernambuco, Brazil
Stress Testing Unlearning Algorithms
Aug 23, 2026Noam Diamant, Neta Glazer, Ethan FetayaLLM Safety Benchmarks Machine Unlearning
Bar-Ilan University
Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning
Aug 11, 2026Zirui Song, Huaxing Liu, Xiang Wang +8LLM Auditing Machine Unlearning
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · AMAP, Alibaba Group · Peking University
PRMU: A Corpus-Free Benchmark for Person-Centric Knowledge Unlearning in Multimodal Large Language Models
Aug 11, 2026Huafeng Chen, Yueming Lyu, Ziyuan Chen +4MLLM Unlearning Machine Unlearning
School of Intelligence Science and Technology, Nanjing University · Department of Computing, Imperial College London
Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness
Aug 5, 2026Haoting Qian, Qingjie Zhang, Zhicong Huang +2Multi-Hop Reasoning Language Model Robustness
Tsinghua University, China. · Ant Group, China.
Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation
Aug 4, 2026Chunlin Liu, Junnian Chen, Haitong Jiang +7VLM Evaluation VLM Unlearning
1Shenzhen University · 2Hebei University of Technology · 3Ludwig Maximilian University of Munich +1
Exploring and Bridging Knowledge Holes in Unlearned Multimodal Large Language Models
Aug 3, 2026Junxiang You, Junkai Chen, Yuhao He +3Multimodal Large Language Models MLLM Unlearning
University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences · 3Cloudspace Technology
Similarity-Aware Machine Unlearning
Jul 31, 2026Madhavan Citalamangalam Kumaran, Midhun Parakkal Unni, Vicky Kouni +1Machine Unlearning Machine Unlearning Evaluation
Department of Computer Science, University of Manchester, UK · Centre for Machine Intelligence, University of Sheffield, UK · School of Computer Science, University of Sheffield, Sheffield, UK +1
Crossing the Margin Cliff: Toward Relearn-Robust LLM Unlearning via Margin Calibration
Jul 30, 2026Xiangyu Yin, Jiaxu Liu, Zhen Chen +1Privacy Leakage in Language Models LLM Unlearning
Chalmers University of Technology, Gothenburg, Sweden · Imperial College London, London, United Kingdom · University of Liverpool, Liverpool, United Kingdom +1
LU-500: A Logo Benchmark for Concept Unlearning
Jul 27, 2026Keyu Li, Jin Gao, Jialing Zhang +1Diffusion Model Unlearning Concept Erasure
Shanghai Jiao Tong University Shanghai, China
Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning
Jul 23, 2026Lorenzo Orsingher, Thomas De Min, Massimiliano Mancini +2VLM Unlearning Algorithmic Fairness
University of Trento, Trento, Italy · Fondazione Bruno Kessler, Trento, Italy
Unlearning as Distribution Restoration: A Controlled Counterfactual Study, a Validated Selective Screen, and the Limits of Oracle-Free Certification
Jul 21, 2026Sen Yang, Yuen-Hei YeungMachine Unlearning Machine Unlearning Evaluation
Stern School of Business New York University · Courant Institute of Mathematical Sciences New York University
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
Jul 10, 2026Amit Peleg, Naman Deep Singh, Naama Pearl +2Factual Knowledge in Language Models Machine Unlearning
Tübingen AI Center, University of Tübingen
Auditing of Unlearning Algorithms
Jul 7, 2026Sahasrajit Sarmasarkar, Anastasia Koloskova, Sanmi KoyejoMachine Unlearning Machine Unlearning Evaluation
Stanford University · University of Zurich
Association Restoration Test: Revealing Restorable Shortcuts after Unlearning
Jul 7, 2026Amy Lu, Changxiu JiSpurious Correlation Robustness Shortcut Learning
Department of Computer Science, Stanford University
PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
Jul 3, 2026Xianren Zhang, Delvin Ce Zhang, Dongwon Lee +1Privacy Leakage in Language Models MLLM Unlearning
The Pennsylvania State University · University of Sheffield
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
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
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

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Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning

Xunlei Chen, Qinghui Gong, Jingkun Xue, Qihe Liu+2

University of Electronic Science and Technology of China · Southwest Jiaotong University

Machine Unlearning LLM Unlearning Knowledge Editing Machine Unlearning Evaluation

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Machine unlearning in large language models aims to remove unwanted knowledge while preserving the model's remaining capabilities. Although existing methods use retention objectives or restrict where edits occur, achieving the desired forgetting level can still leave collateral changes that impair non-target behavior. Our recovery comparisons suggest that some of these changes can be reversed while preserving observed forgetting performance. In this work, we present Propose-Then-Project Unlearning (PTP-U), a framework that combines targeted forgetting with the recovery of non-target capabilities. PTP-U first applies local analytic edits to weaken target knowledge associations, then aligns non-target output distributions with those of the original model to recover capabilities while maintaining fixed forgetting constraints. Both stages serve a common goal: satisfying the forgetting requirements while preserving fluent generation and performance on non-target tasks. Across three benchmarks, PTP-U achieves the strongest forgetting-retention trade-off among evaluated methods, reaching 81.22%-91.03% forgetting while preserving 94.20% non-target utility on average. At matched forgetting, PTP-U consistently retains higher non-target utility.

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