TNA Minimal Implementation v1

The TNA Minimal Implementation v1 provides a functional Python-based skeleton and pseudo-code designed to execute the core cycle of the Theory of Axiomatic Necessity (TNA). Moving beyond classical solvers or machine learning models, this implementation establishes a continuous loop that detects structural failures, interprets them as boundaries (∂Ω), and triggers domain expansions based on computed pressure (FB). The system operates on the core principle of boundary → indeterminacy → pressure → expansion, utilizing mutation and selection mechanisms to evolve its parameter space (Ω). A key property of this implementation is its epistemic autonomy: it does not learn from external data but rather from its own structural failures. The provided skeleton includes extensible modules for domain representation, reliability evaluation ($R(x)$), and boundary detection, offering a foundation for integration with physics simulators, neural networks, or cognitive models. Ultimately, this minimal version serves as a generative system that builds theory by systematically confronting its own limits.