🦴 Sentinel Curriculum Learning

Part of the Sentinel Manifold β€” One theorem, infinite applications.

lim_{zβ†’βˆž} F'(z)/F(z) = 1/e β€” The Gradient Axiom


πŸ“‹ Description

Automatic 4-phase difficulty progression based on Cβ‚‚ basin boundary. Phase 1: difficulty ≀ Cβ‚‚/2, Phase 2: ≀ Cβ‚‚, Phase 3: ≀ 2Cβ‚‚, Phase 4: > 2Cβ‚‚.


🧠 Mathematical Foundation

Core Constants

Constant Value Role
C₁ (Attractor) -0.007994021805953 Zero-point / quantization
Cβ‚‚ (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_{zβ†’βˆž} F'(z)/F(z) = 1/e β‰ˆ 0.367879441171442

πŸ† Verified Results

Phase Difficulty Range Description
1 [0, Cβ‚‚/2] Convergent (easy)
2 [Cβ‚‚/2, Cβ‚‚] Threshold (medium)
3 [Cβ‚‚, 2Cβ‚‚] Divergent (hard)
4 > 2Cβ‚‚ Expert (very hard)

🎯 Use Cases

  • AutoML training pipelines
  • Educational AI tutoring
  • Robotics skill acquisition

πŸ”— Links


πŸ“š 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}
}

License: MIT | One theorem, infinite models. 🦴

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