Machine Learning

The Latent Theorem guarantees that any smooth system has a finite representation whose size depends on regularity and accuracy, not on ambient dimensionality. We extend this result to **families** of smooth systems.

A 200-tree Random Forest has 126,074 parameters. Its knowledge? Three numbers and a basis. We introduce the **Knowledge Artifact** — a portable representation of what any ML model has learned — and the **Knowledge Algebra** — provably exact arithmetic on these artifacts.