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This work introduces Event Signature Transfer (EST), a training-free, model-agnostic operator that turns a completed past event into an explicit forecast scenario, preserving the forecast's linked structure and reducing to it exactly at zero strength.
A previously undocumented confound in how unlearning is evaluated on BatchNorm-based architectures is identified: a single forward pass over retain data, an operation that modifies no weight, can deterministically rewrite the model's normalization state and reverse the apparent surface-metric forgetting.
It is concluded that, on this benchmark and within this family of frozen-encoder architectures, text content is not the operative signal behind the reported gains and the perturbation protocol and evaluation harness are released as a reusable diagnostic toolkit.
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