1. The Stochasticity Gap

The integration of Generative AI into deterministic environments faces a fundamental contradiction. Industrial control systems require reliability of ≈99.999%, whereas State-of-the-Art LLMs are probabilistic engines. We cannot build critical infrastructure on "maybe".

Current approaches (RAG, Chain-of-Thought) reduce error rates but do not eliminate the possibility of structural violation. CausaNova solves this by ensuring that the Neural Network never touches the execution layer directly.

2. Architecture: The Mathematical Firewall

The system enforces a strict separation of concerns:

• Neural Layer (The Architect): Translates vague user intent into a structured Intermediate Representation (DSL).
• Symbolic Layer (The Guard): A deterministic SMT-Solver validates this DSL against hard constraints (Logical, Legal, Physical).
• Execution Layer (The Atomizer): Only validated DSL graphs are compiled into target code (HTML, SQL, C#).

3. The Self-Extending DSL

Unlike static code generators, CausaNova uses a recursive JSON schema. The definition of a "Form Field" or a "Database Table" is itself data, not code. This allows the system to transport logic securely across boundaries (Server to Client) without executing arbitrary code.

4. Live Proof

This HTML file is not just a document. It is the engine itself. By clicking the "Run Interactive Demo" tab above, you can access the actual Compiler and Resolver running entirely in your browser via JavaScript. No server required.