Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastiv

arXiv:2605.19042v1 Announce Type: new Abstract: Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on

arXiv:2603.18767v1 Announce Type: new Abstract: Concept unlearning has emerged as a promising direction for reducing the risks of harmful content generation in text-to-image

arXiv:2604.05634v1 Announce Type: new Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation. While existing MU methods

arXiv:2610.14605v1 Announce Type: new Abstract: Current evaluations of multimodal unlearning typically focus on whether a model stops outputting target information. This

arXiv:2606.27379v1 Announce Type: cross Abstract: Large language models increasingly face demands to "forget" training data, knowledge, or behaviors due to regulatory deletion

arXiv:2609.00003v1 Announce Type: new Abstract: Machine unlearning studies the removal of knowledge from an AI model, making the system forget a concept it previously learned.

arXiv:2604.19089v1 Announce Type: new Abstract: Large language models (LLMs) require frequent knowledge updates to reflect changing facts and mitigate hallucinations. To meet this

arXiv:2604.04982v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have opened new opportunities for recommender systems by enabling rich semantic