The Shape of Thought: Exploring Embedding Strategies with Ollama, HF, and H-Net
🔍 Summary
Stephanie, a self-improving system, is built on a powerful belief:
If an AI can evaluate its own understanding, it can reshape itself.
This principle fuels every part of her design from embedding to scoring to tuning.
At the heart of this system is a layered reasoning pipeline:
- MRQ offers directional, reinforcement-style feedback.
- EBT provides uncertainty-aware judgments and convergence guidance.
- SVM delivers fast, efficient evaluations for grounded comparisons.
These models form Stephanie’s subconscious engine the part of her mind that runs beneath explicit thought, constantly shaping her understanding. But like any subconscious, its clarity depends on how raw experience is represented.