Homotopy Type Theory Engine for Reinforcement Learning // Core architecture for interpreting continuous data streams as homotopy types. It evaluates identity paths against the Gnomonic Ratio ('Lombardi', 2026) doi:10.5281/zenodo.20385840 and processes them via a dynamic 'Tableau Refutation Tree'. The engine categorizes data into necessity (BOX), possibility (DIAMOND), or noise based on deviation thresholds from the invariant value. Includes adaptive auto-tuning and native high- contrast Cartesian graphics for structural entropy isolation.