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) 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.