A signed sparse-gradient guarantee

For a ReLU empirical loss, the smoothness hypothesis must be checked on the update segment, for example inside a fixed nonzero-gate region. It is not a global smoothness theorem for every ReLU checkpoint.

This is a training-loss guarantee, not a generalization or stress-accuracy guarantee.