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arXivMachine unlearning

UnAct: Gradient-Free Unlearning via Targeted Activation Intervention

UnAct uses gradient-free targeted activation interventions to unlearn model classes from few forget images without gradients, labels, or retained data, matching or exceeding SSD and LFSSD accuracy across datasets and preventing network collapse with scarce data.

Saeed Abdul Muizz, Aayat Rafiq, Iqra Altaf Gillani, Janibul Bashir

Published Oct 3, 2026 · ▲ 4 on Hugging Face · Code ★ 1

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arXivMIREA - Russian TechnologicalRussian Science FoundationMachine unlearning

Data Unlearning via Inverse Distillation

Inverse Distillation Unlearning unifies multi-step model distillation and data removal via a min-max objective that recovers only retained data, reducing forgotten-class generation without retained examples or extra classifiers.

Aleksei Leonov, Nikita Kornilov, Zhenhe Zhang, Evgeny Burnaev and 2 more

Published Sep 28, 2026 · 0 citations · ▲ 16 on Hugging Face

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ACL 2025U Notre DameU PennsylvaniaGeorgia Institute of TechnologyDartmouth CollegeMachine unlearning

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models

Modality-aware neuron pruning removes target knowledge from multimodal LLMs by selectively pruning modality-specific neurons to enable precise unlearning.

Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, Chunhui Zhang and 2 more

Published 2025 · 4 citations

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ICML 2024Machine unlearning

Towards Certified Unlearning for Deep Neural Networks

Simple techniques extend certified unlearning to deep networks via inverse Hessian approximations, preserving guarantees for nonconvergence and sequential requests.

Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li

Published Aug 1, 2024 · 0 citations

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NeurIPS 2026JilinHarbin Institute of TechnologyHarbin Institute of Technology, TsinghuaHarbin Institute of Technology,SMachine unlearning

Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models

Zhiqi Zhang, Xinhao Zhong, Yi Sun, Shuoyang Sun and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Hong Kong University of ScieHong Kong University of Science Central SouthThe Hong Kong University of ScieHong Kong University of Science Machine unlearning

Machine Unlearning in Diffusion LLMs

Yili Wang, Yijie Xu, Lu Dai, Tairan Huang and 4 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Bar-IlanMachine unlearning

TanGCE: Manifold-Aware Concept Erasure

Matan Avitan, Yoav Goldberg, Yanai Elazar

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026RMITRMITRoyal Melbourne Institute of TecMonashU MelbourneMachine unlearning

What Should Remain After Forgetting? Rethinking LLM Unlearning as Predictive Posterior Correction

Jingyue Cong, Andy Song, Alexis Horde-Vo, Kai Wei and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Meta PlatformsMeta Platforms, Inc.FacebookAmazonTechnion - Israel Institute of TMachine unlearning

Exact Unlearning via Quantized Sufficient Statistics

Ami Tavory, Shripad Gade, Tal Sarig, Noam Touitou and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026MBZUAIMohamed bin Zayed University of U Electronic Science and TechnolU Notre DameMohamed bin Zayed University of Machine unlearning

From Facts to Personas: Interpretable Role Unlearning in LLMs via Mixture-of-Experts

Ruihong Zeng, Puning Yang, Jinghui Zhang, Shen Gao and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Mohamed bin Zayed University of RIKENU OxfordHKBU / RIKENMohamed bin Zayed University of Machine unlearning

UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?

Puning Yang, Qizhou Wang, Junchi Yu, Bo Han and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tianjin NormalHangzhou DianziMachine unlearning

CLUE: Closing the Loop on Conflict and Collapse in LLM Unlearning

Mengyang Li, Jingwen Wang, Yu Zhang, Pinlong Zhao

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Tianjin NormalMachine unlearning

KNOT: A Knowledge Entanglement Benchmark for Robust Unlearning Evaluation

Mengyang Li, Jingwen Wang, Yu Zhang, Shuang Liu and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Rensselaer Polytechnic InstituteRPIMachine unlearning

Loss is Not Behavior: A Unified Output-Space Analysis of Gradient-Based Machine Unlearning

Xingjian Zhao, Mohammad Mohammadi Amiri, Malik Magdon-Ismail

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Hangzhou DianziPekingNortheasternNational University of SingaporeMachine unlearning

METAFORGET: Audit-Driven Update-Policy Learning for Reliable Language Model Unlearning

Pinlong Zhao, Xiaoling Zhou, Zhou Zhaoting, Guangyuan Dong

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Korea Advanced Institute of ScieKAISTMachine unlearning

Example-Based Spatial Guidance for Training-Free Concept Erasure in Diffusion Models

Younghwan Kil, Joonhyeong Park, Giung Nam, Jinwoo Shin and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Hangzhou DianziZhejiangMachine unlearning

Revisiting Gradient Ascent: Machine Unlearning from a Geometric Perspective for Source-Free Scenarios

Yufeng Cao, Naen Xu, Xuyang Teng, Tianyu Du and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Hong KongU Technology SydneyJilin University of Finance and SUN YAT-SEN UNIVERSITYMachine unlearning

Hallucination-Guided Unlearning: Using Hallucination Traces to Reveal Overfitted Memories

Jiayu Zhang, Ziqi Zhong, Yuliang Gai, Canran Xiao

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U MacquaireMacquarieMachine unlearning

Hyperspherical Local Margin Retraction for Zero-Shot Instance-Wise Machine Unlearning

Hongyi Lyu, Xuyun Zhang, Xiaoxiao Chi, Guanfeng Liu and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Google DeepMindGoogle ResearchGoogleHPI f. Digit. Engin. GGMBHMachine unlearning

We Should Distinguish Unlearning From Untraining

Eleni Triantafillou, Imtiaz Humayun, Mónica Ribero, Alexander Turner and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Department of Computer Science, RiceU North Carolina at Chapel HillMichigan State University & University of North Carolina atMachine unlearning

Words Before Pixels: Selective Modality Routing for Vision-Language Model Unlearning

Laura Yao, Haochen Zhang, Jinhao Duan, Sijia Liu and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026StanfordStanford University / Virtue AIMachine unlearning

What to Forget in Unlearning? Forget Set Curation for Language Models

Animesh Jha, Arpandeep Khatua, Youssef Allouah, Sanmi Koyejo

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Seoul NationalPohang University of Science andPOSTECHMachine unlearning

Generalized Influence Functions for Better Model Change Estimates

Hyeonsu Lyu, Jonggyu Jang, Sehyun Ryu, Hyun Jong Yang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PurdueAmazon AGIU California RiversideAmazonMachine unlearning

Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models

Gaurav Patel, Jun Fang, Greg Ver Steeg, Qiang Qiu and 1 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026UT AustinMassachusetts Institute of TechnRIKENHKBU / RIKENCarnegie MellonMachine unlearning

On the Fragility of Latent Knowledge: Layer-wise Influence under Unlearning in Large Language Model

Jianing Zhu, Zongze Li, Chandler Squires, Qizhou Wang and 2 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026National TaiwanNational Yang Ming Chiao TungU Illinois, Urbana ChampaignMachine unlearning

Robust Concept Unlearning in Diffusion Models via Directional Stability Regularization

Bo-Han Lai, Hsuan-Tien (Tien) Lin, Chia-Mu Yu, Han Zhao and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KoreaMachine unlearning

Spectral Unlearning: Transformer Structure-Preserving Updates for Language Model

Sung Il Choi, Junhao Cai, Dohun Kim, Changhee Joo

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PurdueMachine unlearning

Contrastive Representation Shaping for LLM Unlearning

CLReg uses contrastive regularization to separate forget and retain representations, reducing entanglement and improving LLM unlearning without extra privacy risks.

Haoran Tang, Rajiv Khanna

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026SpotlightUniversität TübingenUniverstity of TübingenEberhard-Karls-Universität TübinU TübingenMachine unlearning

Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs

JensUn uses Jensen-Shannon divergence to achieve stable, robust LLM unlearning with preserved utility and strong resistance to relearning.

Naman Deep Singh, Maximilian Mueller, Amit Peleg, Francesco Croce and 1 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026The University of QueenslandMonashU QueenslandMachine unlearning

Open Vocabulary Domain Unlearning

Existing domain unlearning overfits to seen classes; this paper proposes open-vocabulary domain unlearning via Fisher-masked parameter editing and targeted manifold scattering to erase domains across unseen classes with few shots.

Sumanth V Udupa, Mehrtash Harandi, Yadan Luo, Mahsa Baktashmotlagh

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026CISPA Helmholtz Center for InforCISPA Helmholtz CenterMachine unlearning

Forgetting Has Neighbors: Localized Collateral Forgetting in Machine Unlearning

Unlearning causes localized collateral forgetting that grows near deleted examples due to inconsistent surrogate targets, and local teacher distillation mitigates it.

Polina Dolgova, Sebastian Stich

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026EPFLMachine unlearning

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

ATWU learns token-level forget-specificity via retain-conflict scoring to improve LLM unlearning, achieving state-of-the-art forget-retain trade-offs without external supervision.

Gizem Yüce, Giorgos Nikolaou, Nicolas Flammarion

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Illinois at Urbana-ChampaignCS @ UIUCU IllinoisU Illinois Urbana-ChampaignMachine unlearning

You Can’t Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning

Concept entanglement in diffusion models forces a trade-off where robust unlearning of a target necessarily damages overlapping concepts proportionally to their overlap.

Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026YonseiKAISTMachine unlearning

Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning

SAEParate clusters diffusion latent features by concept via contrastive learning to improve precise concept unlearning with reduced cross-concept interference.

Hyeonjin Kim, Hangyeol Jung, Heechan Yun, Sungjun Yun and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KoreaNew YorkThe University of Texas at AustiBostonMachine unlearning

Consistency-Preserving Concept Erasure via Unsafe–Safe Pairing and Directional Fisher-weighted Adaptation

PAIR reframes diffusion model concept erasure via unsafe-safe pairs, using paired semantic realignment and directional Fisher-weighted adaptation to remove targeted concepts while preserving structural and semantic consistency.

Yongwoo Kim, Sungmin Cha, Hyunsoo Kim, Jaewon Lee and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Walmart TechNortheasternWalmartISTAUMASS LowellMachine unlearning

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation

TRACER reassigns semantic item tokens to erase target concepts in generative recommendation while better preserving recommendation utility than baseline unlearning methods.

Ziheng Chen, Jiali Cheng, Zezhong Fan, Diyuan Wu and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026KoreaMachine unlearning

Retain-Neutral Surrogates for Min-Max Unlearning

ROSU constrains min-max unlearning to retain-orthogonal surrogate perturbations for closed-form updates and reduced retain damage in high-coupling regimes.

Junhao Cai, Dohun Kim, Dowon Kim, Sung Il Choi and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
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