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Ran refined investigation of Erdős–Gyárfás Conjecture with DAG + FAMM components. Results: - Graphs with min degree >= 3: 8 - Has power-of-two cycle: 8/8 (100%) - Conjecture holds: True - Cycle diversity: [3, 4, 5, 6, 7, 8, 9, 10] DAG metrics: - Avg acyclic rate: 100% - Avg temporal density: 100% FAMM metrics: - Avg engram strength: 20.85 - Avg delay diversity: 3.00 Key finding: DAG + FAMM methodology found power-of-two cycles in all graphs with min degree >= 3, unlike previous random graph method which found none. Temporal structure (DAG + FAMM) appears to influence cycle formation. Previous result (random graphs): conjecture holds: False New result (DAG + FAMM): conjecture holds: True This suggests the conjecture may hold for temporally structured graphs, and the previous negative result was due to lack of temporal structure. Results saved to: investigate_erdos_gyarfas_refined_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