PI-DST graph-growth seed
Generates a simple layered graph and records node count, edge count, diameter, max degree, and a toy invariant flag.
Boundary: this simulation does not prove PI-DST or infinite scalability.
fractal-geometry • Mechanization target
PI-DST — Fractal Routing Growth Target
A DFT network family with bounded fractal growth constraints may support structured routing-depth bounds under explicit assumptions.
Assumptions
- Subnetwork sequence is explicitly defined.
- Hausdorff or box-counting dimension constraint is stated.
- Routing tree construction is specified.
- Message-depth metric is defined.
Proof Obligations
- Define subnetwork sequence.
- Prove covering bound.
- Prove routing-depth bound.
- Show assumptions are implementation-realistic.
- Separate mathematical bound from operational latency.
Simulation seeds
First toy models for review
The first Python simulations are deliberately small. They are intended to produce reproducible toy metrics and worked examples, not proof or validation.
Fiber Vector examples
Creates governance, evidence, and contract example fibers using normalized illustrative values and cosine-similarity comparison.
Boundary: these examples are modeling exercises, not validated physical or operational metrics.
Claim Boundary
Formalization target. Scaling claims must remain conditional on stated assumptions and empirical implementation tests.