3 papers
cs.CR2026
Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions
Xiaoyu Xu, Minxin Du, Li Bai +8
Machine unlearning aims to remove target influence while preserving other capabilities. This survey compares methods, benchmarks, and evidence across large language models and syst…
cs.LG2026
Machine Unlearning in Low-Dimensional Feature Subspace
Kun Fang, Qinghua Tao, Junxu Liu +4
Machine Unlearning (MU) aims at removing the influence of specific data from a pretrained model while preserving performance on the remaining data. In this work, a novel perspectiv…
cs.CL2026
FIT to Forget: Robust Continual Unlearning for Large Language Models
Xiaoyu Xu, Minxin Du, Kun Fang +5
While large language models (LLMs) exhibit remarkable capabilities, they increasingly face demands to unlearn memorized privacy-sensitive, copyrighted, or harmful content. Existing…