The paper introduces Quantized Sufficient Statistics (QSS), a method that separates a frozen global schema from mutable, sum‑decomposable content to enable exact unlearning. QSS offers two variants: QSS‑L, which removes a label while keeping the input, and QSS‑E, which removes both input and label by learning a schema without deletable examples. Experiments on 15 vision, text, and tabular datasets show that QSS‑L achieves deletion latencies up to 483× faster than SISA while maintaining comparable accuracy, and QSS‑E quantifies the accuracy trade‑off when all traces of an input are removed.
By Ami Tavory, Shripad Gade, Tal Sarig, Noam Touitou, Ido Guy
arXiv:2605. 07482v2 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full retraining.
By Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei, Mohammad Rostami, Jesse Thomason, Robin Jia
arXiv:2506.10946v3 Announce Type: replace-cross
Abstract: Unlearning large language models (LLMs) is important due to copyright and privacy concerns. A key challenge is unintended forgetting, where r...
By Peizhi Niu, Evelyn Ma, Boya Hou, Huiting Zhou, Duo Zhou, Huan Zhang, S. Rasoul Etesami, Olgica Milenkovic
The paper introduces MoME, a switchable unlearning framework for re-identification models that allows a single backbone to operate in either Full mode (standard inference) or Core mode (unlearned inference). MoME partitions the backbone into core parameters that retain retrieval capability and zero‑initialized parasitic parameters that store full‑data knowledge, training them with gradient isolation so the core never receives forget‑set gradients. The authors demonstrate that Core mode satisfies deletion requests with a simple flag flip, prove its independence from the forget set, and validate its effectiveness on Market‑1501, VeRi‑776, and RegDB using ResNet and CLIP backbones.
By Jingong Chen, Kyle Gao, Guangyuan Li, Chulwoo Pack, Sijia Liu, Kwanghee Won
arXiv:2605. 20341v2 Announce Type: replace-cross Abstract: Federated learning systems must support data deletion requests to comply with privacy regulations, yet retraining from scratch after each deletion is computationally prohibitive.
By Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi
The paper introduces Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer‑selective unlearning framework for large language models. FOM-UL uses a forget‑to‑retain significance score to identify transformer layers that strongly influence the forget set while being insensitive to the retain set, allowing targeted updates that preserve most of the model. Experiments on TOFU, KnowUnDo, and MUSE-style benchmarks show that FOM-UL reduces residual memorization and maintains utility better than several baselines, even after 8‑bit and 4‑bit post‑training quantization, and it also limits recovery of forgotten content in adversarial prompt tests.
By Ravi Ranjan, Olivera Kotevska, Agoritsa Polyzou
arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.
By Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui, Prashant Trivedi, Yash Sinha, Pratik Narang
arXiv:2610.31786v1 Announce Type: new
Abstract: Federated unlearning (FU) aims to remove a designated target client's influence from a trained model without the prohibitive cost of retraining from sc...
By Thanh Linh Nguyen, Quoc-Viet Pham
Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machine unlearning offers...
The paper introduces auditability as a fourth evaluation axis for machine unlearning, measuring the separation between forgotten and retained data via a statistic computed by the provider. Experiments on eleven unlearning methods across TOFU, MUSE, and RWKU datasets show none satisfy all four axes, prompting the authors to propose a two‑stage approach: GapWiden, which widens the audit statistic gap using a hinge loss, and GapVeil, an inference‑time rejection sampler that refines responses based on audit statistics. The combined method achieves oracle‑level auditability and leakage control for all tested unlearning techniques, while improving retain performance for most methods.
By Piyush Tiwary, Anushka Srivastava, Prathosh A. P
The paper investigates what a service must retain to faithfully reproduce a training pipeline after data deletion requests, focusing on a rule where earlier similar records block later ones. It shows that a limit on future deletions precisely determines which records can still be needed for training, enabling an exact repair of training statistics after repeated deletions. The authors also construct finite‑precision features for ridge regression and demonstrate that the required private information can be minimized across a sequence of deletion requests, validating their approach on 1,001 AskUbuntu records.
By Priyank Jayraj, Devanshu sahoo, Uday Mittal
Machine unlearning aims to remove a user's data from a trained model after a deletion request. Existing methods are majorly evaluated on three axes - how well they forget, how much capability they ret...