DecisionUnlearn-Bench: Unlearning Probabilistic Decisions under Changing Schemas

Reported by arXiv cs.CR (Tiantong Wu, Wei Yang Bryan Lim)

Summary by Cyber Security News30 Oct 2026, 05:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.38431

arXiv:2610.38431v1 Announce Type: new Abstract: Machine unlearning aims to reproduce the behavior of a model trained without designated data. For probabilistic decision models, this target also depends on the candidate options supplied at inference. We introduce DecisionUnlearn-Bench, an empirical framework that compares full, unlearned, and retain-only models across deletion granularities, label renderings, and candidate sets.

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