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This work proposes Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level unlearning framework that selects transformer layers using a forget-to-retain significance score and provides an empirical path toward quantization-resilient unlearning.
SafeCommit, a risk controlled layer between agent reasoning and external execution, is introduced, a calibrated set of plausible latent worlds from memory, observations, tool outputs, provenance, and policy constraints that permits a side effectful action only when a conformal action certificate shows that the action i...
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