LLM Unlearning

LLM: Large Language Model

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20 papers in the last four weeks, up 150% on the four weeks before. 0.2% 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
Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
Oct 7, 2026Wei Zhai, Xiang Liu, Qiang Huang +6LLM Unlearning Privacy Leakage in Language Models
College of Computer Science and Artificial Intelligence, Fudan University · BEDI Cloud · School of Electrical and Information Engineering, Tianjin University +3
How Learning Governs Unlearning across the Memorization-Generalization Spectrum
Oct 6, 2026Hwiyeong Lee, Hyelim Lim, Ingyu Bang +2Memorization in Language Models Machine Unlearning
Seoul National University · Hanyang University · Chung-Ang University
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
LLM Persona Unlearning
Sep 30, 2026Kemou Li, Zhuan Shi, Qizhou Wang +4Model Editing Persona-Conditioned Generation
State Key Laboratory of Internet of Things for Smart City, University of Macau · Mila – Québec AI Institute · McGill University +3
Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing
Sep 30, 2026Kemou Li, Qizhou Wang, Yue Wang +6LLM Security LLM Safety
State Key Laboratory of Internet of Things for Smart City, University of Macau · RIKEN Center for Advanced Intelligence Project · The University of Melbourne +5
Faithful Dual-constrained Erasure for Robust LLM Safety Alignment
Sep 30, 2026Jiaqing Li, Shide Zhou, Zhibo Zhang +3LLM Safety Alignment Machine Unlearning
Huazhong University of Science and Technology · Hubei University
Learning What to Forget: Distributional Unlearning for LLM Representation Spaces
Sep 30, 2026Pinaki Mohanty, Haoran Tang, Maggie Makar +1Density Ratio Estimation Machine Unlearning
Department of Computer Science, College of Science & College of Engineering, Purdue University, West Lafayette, IN, USA · Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI, USA
Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets
Sep 30, 2026Haoran Tang, Rajiv KhannaDeception in Language Models Machine Unlearning
Department of Computer Science Purdue University
Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning
Sep 29, 2026Tianhao Qian, Ziming Hong, Chongyang Gao +2Machine Unlearning LLM Unlearning
Southeast University (seu.edu.cn) · University of Sydney (usyd.edu.au) · Northwestern University (northwestern.edu) +2
LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
Sep 29, 2026Kajetan Ożóg, Alicja Wojciechowska, Dawid Malarz +3Prompt Optimization LLM Unlearning
Jagiellonian University · IDEAS Research Institute · Heinrich Heine Universität Düsseldorf
UNBIND: UNlearning By INference-time Directional Steering for Code LLMs
Sep 28, 2026Zhengyang Shan, Jiayun Xin, Yanjun Lin +7Activation Steering LLM Unlearning
Department of Computer Science and Technology Shandong University Qingdao, China
TULIP: Targeted LLM Unlearning at Layers Identified Per-Input
Sep 28, 2026Yejin Kim, William F. Shen, Seokwon Jung +2Machine Unlearning LLM Unlearning
Korea Advanced Institute of Science & Technology (KAIST) · University of Cambridge · Sookmyung Women’s University
Causal Routing for Unlearning
Sep 28, 2026Bardh Prenkaj, Andrea D'Angelo, Davide Mottin +4Causal Interventions in Language Models Machine Unlearning
Sapienza University of Rome · Aarhus University
Making LLMs Truly Forget: Deep Unlearning by Searching, Selecting, and Severing Knowledge Paths
Sep 28, 2026Jialu Wang, Peizhi Niu, Haoteng Yin +3Factual Knowledge in Language Models Machine Unlearning
Tongji University · University of Illinois Urbana-Champaign · Purdue University +1
Trajectory Unlearning on LLM-based Agents
Sep 27, 2026Yingdan Shi, Ren WangLong-Horizon Agent Tasks LLM Unlearning
Illinois Institute of Technology, Chicago, USA
The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning
Sep 24, 2026Manit Baser, Aditya Nawal, Dinil Mon Divakaran +1Adversarial Attacks on LLMs Privacy Leakage in Language Models
National University of Singapore · A*STAR Institute of Advanced Intelligence and Computing, Singapore
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
Sep 23, 2026Jialu Wang, Jianing Deng, Shuqing Luo +6Machine Unlearning LLM Unlearning
University of North Carolina at Chapel Hill · University of Pittsburgh · University of Arizona +1
GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
Sep 18, 2026Zeyu Yan, Guanghao Zhou, Minghui Qiu +2Privacy Leakage in Language Models Machine Unlearning
East China Normal University, Shanghai, China · Unaffiliated
Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning
Sep 15, 2026Qingchen Yu, Shiying Duan, Xiaodong Li +5Privacy Leakage in Language Models Machine Unlearning
1Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University · School of Artificial Intelligence, Beihang University · Center for Applied Statistics, School of Statistics, Renmin University of China +1
Test-Time Unlearning via Sparse Autoencoder
Sep 14, 2026Pingzhi Li, Jinhao Duan, Vaishnav Tadiparthi +6Machine Unlearning LLM Unlearning
University of North Carolina at Chapel Hill · 2Honda Research Institute · 3Michigan State University
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
Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Sep 9, 2026Ravi Ranjan, Olivera Kotevska, Agoritsa PolyzouMemorization in Language Models Language Model Robustness
Florida International University (FIU), Miami, FL, USA · Oak Ridge National Laboratory (ORNL), Oak Ridge, TN, USA
Extracting Forgotten Prompts from Targeted Unlearned Models
Sep 3, 2026Au Ashley Hoi-Ting, Meghdad Kurmanji, William F. Shen +2Privacy Leakage in Language Models LLM Unlearning
University of Warwick · University of Cambridge
Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning
Sep 1, 2026Miso Kim, Georu Lee, Seungwon Jeong +1Privacy Leakage in Language Models Machine Unlearning
Dongguk University-Seoul
On the Recoverability of Private Information Unlearning in Large Language Models
Aug 30, 2026Shicheng Hu, Runzhi Tian, Ziqiao Wang +1LLM Auditing Privacy Leakage in Language Models
University of Illinois Urbana-Champaign · University of Ottawa · Tongji University
Stress Testing Unlearning Algorithms
Aug 23, 2026Noam Diamant, Neta Glazer, Ethan FetayaLLM Safety Benchmarks Machine Unlearning
Bar-Ilan University
An Empirical Study of Reward Specification and Benchmark Reliability in GRPO-based LLM Unlearning
Aug 18, 2026Rubén Balbastre, Juan Manuel Orduña, Mariano PérezGroup Relative Policy Optimization Rubric-Based RL
University of Valencia
Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs
Aug 13, 2026Beining Xu, Hairui Wang, Jiaxin Wang +2Document Understanding Privacy-Preserving Language Models
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

Oct 8, 2026, cs.CLJ/K move · Enter open · S save

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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