Z(t): how the machine behaves.

prediction    M(t-k:t)        ->  M_hat(t + dt)     with uncertainty
anomaly       M(t)            ->  how surprising is this

Prediction before optimisation

This crate improves nothing and chooses nothing. It watches, forms claims, and is scored on how wrong it was. A system that cannot predict its own next state has no business acting on itself, and prediction error is the only honest measure of whether the mirror carries enough signal to be worth having.

The central metric is deliberately skill against a hard baseline, not absolute error. Most cells of a mirror barely move between consecutive reflections, so copying the last value scores extremely well; any model that does not clearly beat that has learned nothing, however small its error looks.

Uncertainty is part of the output

A prediction without a confidence is unusable by a controller: it cannot tell the difference between "move the thread, I am sure" and "move the thread, I am guessing". Every [predictor::Prediction] carries an interval and a confidence derived from how well the model has been doing lately on that specific cell, not from a global average.

What this crate cannot see

It depends on corescout-mirror, corescout-memory and corescout-represent, and on nothing that touches hardware. Everything it knows came through the reflection.