Axiom of Maria, Grok 2026

It’s transduction across ontologies: from abstract, symbolic thought (literary/philosophical axiom) to visual/spatial reasoning (Step B) to procedural, generative computation (Step C). Each domain imposes its own constraints and affordances, and the mapping isn’t 1:1.

Heuristic: Literary-to-Code Translation Ladder (Stepwise Abstraction for AI)

Step 0: Text Ingestion & Semantic Parsing

• Input: literary/hermetic/philosophical text.

• Actions:

  • Parse sentences, phrases, and keywords.

  • Identify entities, relationships, transformations, cycles, polarity, feedback loops, and emergent outcomes.

  • Flag abstract terms (e.g., One, Fourth, memory, emanation) as nodes/concepts.

• Output: structured semantic map.

Step 1: Conceptual Decomposition

• Convert parsed semantics into a conceptual DAG (Directed Acyclic Graph) or cyclic flow:

  • Nodes = concepts (One, Two, Three, Fourth).

  • Edges = processes or transformations (emanation, reversion, reintegration).

  • Identify cycles → mark as feedback loops.

• Action:

  • Build Step A skeleton: purely symbolic, labeled, static diagram representation.

• Output: first-level diagram / mapping of relationships.

Step 2: Spatial / Temporal Mapping (Scaffold Layer)

• Convert conceptual graph → visual/spatial representation:

  • Map nodes to positions (circle, spiral, hierarchy).

  • Map edges to motion cues: direction, pulse, flow.

  • Add highlighting or phase markers to indicate active transformations.

• Optional: add interactive step sequencing.

• Output: Step B — a visual or pseudo-animated scaffold.

Step 3: Procedural Abstraction & Emergent Encoding

• Translate Step B into generative procedural code:

  • Encode nodes as objects/classes/particles.

  • Encode edges/flows as dynamic updates: velocity, phase, growth, branching.

  • Embed state memory or feedback variables to model cyclic influence (e.g., memory in spiral example).

• Use iterative time-stepping / frame-based animation to simulate emergent behavior.

• Output: Step C — working dynamic code that embodies the axiom.

Step 4: Feedback & Refinement Loop

• Compare emergent code behavior to original semantic intent:

  • Check cycles, accumulation, convergence, feedback.

  • Adjust parameters: timing, scaling, branching, randomness.

• Optional: AI can generate intermediate visualizations for validation.

• Output: refined dynamic implementation that preserves ontological meaning.

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Meta-Heuristics / Rules of Thumb for AI

1. Identify cycles first, literal content second: in alchemical or hermetic texts, the process is often more important than precise wording.

2. Nodes ↔ Objects, Edges ↔ Dynamics: treat abstract relationships as code primitives.

3. Memory / State Variables are key: cyclic return or reintegration is encoded in accumulators or feedback.

4. Iterative scaffolding beats one-shot generation**: build static → visual → dynamic in order; each step checks consistency with original meaning.

5. Emergent behavior > literal depiction: let the code behave according to principles, rather than literally draw symbols.