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 (), 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 (). 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.
Claudio Bresciano (Thu,) studied this question.