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. Technical Implementation Notes (FAQ)

To ensure transparency regarding the capabilities of this artifact:

• Production vs. Demo: The production CausaNova Engine runs on .NET 8 using the Z3 Theorem Prover via Kubernetes. This artifact demonstrates the identical logic architecture but runs a ported, simulated SMT-Resolver in client-side JavaScript for portability.
• Determinism: The validation logic presented here is mathematically deterministic. Given the same constraints and input, the output is guaranteed to be identical, independent of the LLM's stochastic nature.