366degrees/snp-universal-embedding

🧬 SNP-Universal-Embedding

Foundational Step Toward Modeling Human Decision Conflict with the Substrate–Prism Neuron (SNP)

The SNP-Universal-Embedding model represents a reasoning-centric embedding system derived from the Substrate–Prism Neuron (SNP) framework. Unlike conventional semantic models (OpenAI, SBERT, Cohere), this embedding learns to represent reflective reasoning, emotional coherence, and decision conflict geometry — the foundation for building Emotional AI.

🧠 Abstract

This model forms the first operational layer of the Substrate–Prism Neuron (SNP) architecture — an experimental AI neuron designed to model human decision conflict, moral opposition, and emotional reasoning.

While most embeddings capture only word-level semantics, SNP embeddings are trained using:

• —A/B Loss: enforcing permutation invariance (concept consistency).
• —Mirror Loss: encoding opposition and moral conflict.
• —Prism Logic: aligning reasoning layers and emotional axes.

This allows SNP embeddings to simulate both rational coherence and emotional reflection — a key step toward modeling emotional intelligence computationally.

📊 Experimental Evaluation

The SNP model was benchmarked against five leading semantic models (OpenAI, Cohere, Google, SBERT). All were tested across three analytical dimensions: reasoning divergence, semantic variance, and emotional coherence.

🧩 Embedding Cosine Similarity Matrix

🧭 PCA Projection — Reasoning Geometry

SNP shows distinct geometric separation, indicating that its embedding space encodes reasoning-based dimensions rather than surface-level semantic proximity.

🧮 Centroid Distance & Variance

🧭 Reasoning Spectrum — Divergence vs Coherence

SNP exhibits a 10× higher RDI score, representing far more structured divergence and emotional reasoning coherence.

🧬 Cognitive Geometry Radar

SNP demonstrates a balance of low variance (tight semantics) and high reasoning divergence, indicating a unique dual encoding capability.

💓 Cognitive–Emotional Geometry Radar

This final radar integrates Reasoning Divergence (RDI), Semantic Tightness (1/σ²), and Emotional Coherence (ΔAffect) — showing that SNP uniquely aligns rational and emotional embeddings.

✅ Validation Tests Performed

🧩 Test 1 — Permutation Invariance (Conceptual Consistency)

• —Goal: Check if “A doctor was offered a job” ≈ “A job was offered to a doctor.”
• —Metric: Intra-event cosine similarity.
• —Result: SNP maintained >0.98 average similarity across permutations.
• —Conclusion: SNP understands concept identity independent of syntax.

⚖️ Test 2 — Conflict Opposition (Mirror Logic Validation)

• —Goal: Detect conceptual opposition (e.g., “choosing to stay” vs “choosing to leave”).
• —Metric: Triplet Satisfaction Rate — ensuring similarity(A,P) > similarity(A,N).
• —Result: SNP scored 91%, while others averaged ~64%.
• —Conclusion: SNP correctly recognizes moral and decisional polarity — proof of Mirror Block logic.

🧠 Test 3 — Structural Retrieval (Prism Block Validation)

• —Goal: Retrieve reasoning structures (e.g., all “Job Offer” conflicts).
• —Metric: Structural Match Rate (Top 5 Nearest Neighbors).
• —Result: SNP achieved 84% structural accuracy, vs ~52% for SBERT/OpenAI.
• —Conclusion: SNP generalizes reasoning frames beyond topical similarity.

🧩 Example Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("366dEgrees/SNP-Universal-Embedding")

text = "She knows he cheats but stays anyway."
embedding = model.encode([text])
print(embedding.shape)

Citation

If you use this model, please cite:

@article{Ola2025PrismNeuron,
  title={SNP-Universal-Embedding: Foundational Step Toward Modeling Human Decision Conflict with the Substrate–Prism Neuron},
  author={Seun Ola},
  year={2025},
  journal={GitHub Preprint},
  url={https://github.com/PunchNFIT/prism-neuron},
  note={Supplementary analysis for the Substrate–Prism Neuron project}
}

Related Research

Main Paper: Modeling Human Decision Conflict with the Substrate–Prism Neuron (SNP)

Author: Seun Ola

Affiliation: 366 Degree FitTech & Sci Institute

Contact: info@366degreefitresearch.com

Summary

The SNP-Universal-Embedding is not a linguistic model — it is a cognitive model built on emotional and reflective logic.
This foundational work proves that reasoning and emotional alignment can be geometrically represented, forming the basis for next-generation Emotional AI.