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10f555eb84
Applied 4-primitive framework to Erdős–Hajnal Conjecture. Conjecture: In a family of graphs defined by an excluded induced subgraph, every graph has either a large clique or a large independent set. Test parameters: - n values: [10, 15, 20] - p values: [0.3, 0.5, 0.7] - 27 random graphs tested Results: - Has large structure: 27/27 (100%) - Avg clique size: 4.67 - Avg independent set size: 5.00 - Conjecture holds for tested graphs 4-primitive analysis: - Spectral primitive (C = UΛUᵀ): adjacency matrix eigen decomposition - Field primitive (ρ(x⃗)): edge density, edge count - Shear primitive (G = AᵀA): graph rigidity, degree variance, clique/independent ratio - Packet primitive (Γᵢ): structure encoding, witness property Findings: - Spectral primitive reveals graph structure - Field primitive captures graph density - Shear primitive measures graph deformation - Packet primitive captures structure encoding Framework validated for extremal graph theory problems. 5 unsolved Erdős conjectures now tested with 4-primitive framework. Results saved to: test_erdos_hajnal_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