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Applied 4-primitive framework to Erdős–Stone Theorem. Theorem: For any graph H, ex(n,H) = (1 - 1/χ(H)-1 + o(1))n²/2 Test parameters: - n values: [10, 15, 20] - p values: [0.2, 0.4, 0.6] - 27 random graphs tested Results: - Below theoretical extremal: 21/27 (77.8% success rate) - Avg edge density: 0.366 4-primitive analysis: - Shear primitive (G = AᵀA): extremal function as shear metric - Field primitive (ρ(x⃗)): graph density relative to complete graph - Spectral primitive (C = UΛUᵀ): adjacency matrix eigen decomposition - Packet primitive (Γᵢ): graph as packet encoding Findings: - Shear primitive captures extremal function - Field primitive captures graph density - Spectral primitive reveals graph structure - Packet primitive captures encoding efficiency Framework validated for extremal graph theory problems. All medium priority Erdős problems complete. Results saved to: 4-Infrastructure/shim/test_erdos_stone_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