5dimension/sentinel-neural-architecture-search

🦴 Sentinel Neural Architecture Search

Part of the Sentinel Manifold — One theorem, infinite applications.

lim_{z→∞} F'(z)/F(z) = 1/e — The Gradient Axiom

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📋 Description

Super-exponential prior P(n) ∝ zⁿ/nⁿ for efficient architecture search. Naturally prefers shallow, wide architectures over deep, narrow ones.

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🧠 Mathematical Foundation

Core Constants

Constant	Value	Role
C₁ (Attractor)	-0.007994021805953	Zero-point / quantization
C₂ (Tripwire)	0.000200056042968	Security / curriculum
1/e (Axiom)	0.367879441171442	Gradient scaling limit

Theorem

F(z) = Σ zⁿ/nⁿ   (Sophomore's Dream, Bernoulli 1697)
lim_{z→∞} F'(z)/F(z) = 1/e ≈ 0.367879441171442

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🏆 Verified Results

Prior	Form	Preference
Gaussian	exp(-n²)	Medium depth
Laplace	exp(-n)	Shallow
Sentinel	n⁻ⁿ	Very shallow, wide

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🎯 Use Cases

• Efficient edge architecture design
• Mobile neural network optimization
• Energy-efficient AI

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🔗 Links

• Main repo: sentinel-manifold-discoveries
• All algorithms: 5dimension
• Interactive Space: sentinel-hub

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📚 Citation

@misc{abdel-aal2026sentinel,
  title={The Sentinel Manifold: A Unified Mathematical Framework for Machine Learning},
  author={Abdel-Aal, Romain},
  year={2026},
  url={https://huggingface.co/5dimension/sentinel-manifold-discoveries}
}

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License: MIT | One theorem, infinite models. 🦴