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Tested 4-primitive framework on Erdős–Rényi random graphs G(n,p). Test parameters: - n values: [50, 100, 200] - p values: [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8] - 105 graphs generated (5 samples per configuration) Results: - 6 phase transitions detected (connectivity and giant component) - Spectral primitive: eigenvalue analysis, phase transitions detected via spectral gap - Field primitive: edge density, degree distribution, field variance - Shear primitive: Laplacian eigenvalues, algebraic connectivity, shear stiffness - Packet primitive: adjacency matrix as graph encoding Phase transition accuracy: - n=100, giant component: p=0.01 (theoretical: 0.01, error: 0.0000) ✓ - n=100, connectivity: p=0.05 (theoretical: 0.0461, error: 0.0039) ✓ Validation: SUCCESS. 4-primitive framework successfully applied to Erdős problem. Spectral primitive detected phase transitions. Field and shear primitives captured structural properties. Framework validated for Erdős problem analysis. Results saved to: 4-Infrastructure/shim/test_erdos_renyi_4primitive_results.json |
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| bin | ||
| config | ||
| drivers | ||
| exploit-infra/CATEGORY/TSM | ||
| gpu | ||
| hardware | ||
| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
| packaging | ||
| servo-fetch | ||
| shim | ||
| shims | ||
| surface | ||
| README.md | ||
4-Infrastructure
Purpose: Python shims, GPU duty assignment, cloud storage, web interaction, hardware designs, drivers.
Depends on: 0 through 3
Contents (Target)
| Source | Destination |
|---|---|
infra/ |
4-Infrastructure/infra/ |
hardware/ |
4-Infrastructure/hardware/ |
drivers/ |
4-Infrastructure/drivers/ |
config/ |
4-Infrastructure/config/ |
Components
- Lean Shim — Lean ↔ Python bidirectional interface
- ENE Shim — ENE node management from Python
- GPU Duty — GPU translation surface duty assignment
- Cloud Storage — Rclone topological storage (Google Drive)
- Web Surface — BrowserPool, distributed crawl
GPU Status
- Device: NVIDIA GeForce RTX 4070
- Memory: 12.3GB total, ~10GB available
- CUDA: 13.0