Challenge degradation and modulation withdrawal

For a frozen acute checkpoint, let ${\mathcal{P}}_f$ remove an exact fraction of active positions under a specified ranking. Define

Use the same evaluation law and a declared noise coupling.

A smaller degradation does not necessarily mean better post-challenge performance. For example, accuracies $0.40\to 0.40$ and $0.95\to 0.80$ have losses $0$ and $0.15$, while the second checkpoint remains better. Both absolute post-challenge performance and degradation must therefore be reported.

Removing the source's modulation switches both $a\to 1$ and $\tanh \to \mathrm{ReLU}$, while retaining consolidated weights and biases. That is a specified computational intervention, not an experiment on pharmacological withdrawal. Its possible effect is not necessarily a reversal of the original treatment.