Expand description
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.
Re-exports§
pub use anomaly::Anomaly;pub use anomaly::AnomalyDetector;pub use anomaly::Surprise;pub use predict::evaluate;pub use predict::CellScore;pub use predict::PredictionReport;pub use predictor::Prediction;pub use predictor::SelfModel;pub use predictor::StatePrediction;pub use uncertainty::Confidence;pub use uncertainty::Interval;
Modules§
- anomaly
- Noticing that something is unusual.
- predict
- Predicting the next reflection.
- predictor
- An online self-model: predict the next reflection, then find out.
- uncertainty
- How sure the model is, and why that has to be reported.