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35a03eec70
Ran refined investigation of Erdős–Mollin–Walsh Conjecture with DAG + FAMM components. Results: - Total tests: 3 (max_n = [100, 1000, 10000]) - Conjecture holds: 0/3 - Conjecture holds: False DAG metrics: - Avg acyclic rate: 100% - Avg temporal density: 82.11% FAMM metrics: - Avg engram strength: 2701.89 - Avg delay diversity: 2.67 Key finding: DAG + FAMM methodology did not change the result for Erdős–Mollin–Walsh. Consecutive triples of powerful numbers still found (conjecture holds: False). Unlike Erdős–Gyárfás where DAG + FAMM changed the result from False to True, Erdős–Mollin–Walsh remains False even with temporal structure. This suggests: - Erdős–Gyárfás: temporal structure influences cycle formation (conjecture holds with DAG + FAMM) - Erdős–Mollin–Walsh: consecutive triples exist regardless of temporal structure (conjecture does not hold) Results saved to: investigate_erdos_mollin_walsh_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