PRISM: Breaking the Reactive Bottleneck in Retain-Free Unlearning with Proactive Data-Free Structural Priors

The practical deployment of Machine Unlearning (MU) is hindered by two fundamental, often intertwined, challenges: (1) a widespread dependency on original retain data, which is often legally and practically untenable, and (2) a severe operational bottleneck in existing methods. This bottleneck renders critical operational functions, such as efficiently calibrating forgetting intensity and handling concurrent requests, a practical challenge largely unaddressed in the field. We introduce PRISM (PRoactive Intrinsic Structural Modeling for Unlearning), a retain-free framework that solves this trade-off with a proactive and model-centric philosophy. At its core is our novel 'Data-Free Structural Prior', a data-agnostic foundation learned offline by modeling the model's intrinsic parameter structure. This proactive design replaces costly iterative optimization with a single, analytical update at request time. This decoupled structure enables amortized handling of concurrent requests and low-cost calibration to meet a predefined forgetting target. Extensive experiments confirm PRISM's state-of-the-art performance and operational efficiency, unifying the erasure of data, classes, and concepts across discriminative and generative models.