arXiv Machine Learning July 31, 2026 By Anat Kleiman, Robert Fisher, Ben Deaner, Udi Wieder

When unlearning is free: leveraging low influence points to reduce computational costs

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arXiv:2512. 05254v2 Announce Type: replace Abstract: As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important.

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Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs

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Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget

arXiv:2606. 16110v1 Announce Type: new Abstract: Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements.

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On the Recoverability of Private Information Unlearning in Large Language Models

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Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

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arXiv:2506. 01396v2 Announce Type: replace Abstract: Differential privacy (DP) has become an essential framework for privacy-preserving machine learning.

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