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---
language: en
library_name: sentence-transformers
tags:
- emotional-ai
- reasoning-embedding
- substrate-prism
- cognitive-modeling
license: mit
pipeline_tag: feature-extraction
---

# 🧬 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**.

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## 🧠 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**.

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## 📊 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

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### 🧭 PCA Projection — Reasoning Geometry

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

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### 🧮 Centroid Distance & Variance

| Metric | Meaning | SNP Result | Industry Avg |
|--------|----------|-------------|---------------|
| **Variance (σ²)** | Intra-cluster compactness | **800.63** | ~10,000 |
| **Centroid Distance (Δ)** | Reasoning space separation | **High (Distinct)** | Moderate |
| **RDI (Reasoning Divergence Index)** | Reasoning uniqueness + coherence | **0.04888** | 0.0044 |

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### 🧭 Reasoning Spectrum — Divergence vs Coherence

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

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### 🧬 Cognitive Geometry Radar

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

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### 💓 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*.

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## ✅ 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.

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### ⚖️ 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.

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### 🧠 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.

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## 🧩 Example Usage
```python
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: [email protected]

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.

LICENSE

@@ -0,0 +1,2 @@
+MIT License.
+Covered under the Substrate–Prism Neuron Provisional Patent (© 366 Degree FitTech & Sci Institute).

config.json

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+{
+    "model_type": "custom_snp",
+    "base_model": "bert-base-uncased",
+    "embedding_dimension": 6,
+    "description": "SNP-Universal-Embedding \u2014 distilled from emotional geometry via Substrate-Prism Neuron framework."
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20835793a996aa7a73fa44deb791be89a646e29769de91fae4e438e2234ea56e
+size 442758231

snp_readme.md

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+\# SNP-Universal-Embedding  
+
+\_A public-ready embedding model derived from the Substrate-Prism Neuron (SNP) framework.\_
+
+
+
+\## Description  
+
+SNP-Universal-Embedding is a 6-dimensional embedding model built from emotional geometry principles
+
+originating in the Substrate-Prism Neuron research.  
+
+It maps semantic and emotional relationships into compact geometric space — designed for  
+
+research in cognitive modeling, decision conflict analysis, and affective reasoning.
+
+
+
+\## Key Info  
+
+\- \*\*Base model:\*\* BERT-base-uncased  
+
+\- \*\*Dimensions:\*\* 6  
+
+\- \*\*Purpose:\*\* Compact universal embeddings reflecting emotional \& relational context  
+
+\- \*\*Use cases:\*\*  
+
+  - Compare semantic + affective similarity  
+
+  - Feed into downstream emotional-reasoning models  
+
+  - Multi-modal integration (text → cognitive vector space)
+
+
+
+\## Example Usage (local)
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+
+import torch
+
+
+
+tokenizer = AutoTokenizer.from\_pretrained("./SNP-Universal-Embedding")
+
+model = AutoModel.from\_pretrained("./SNP-Universal-Embedding")
+
+
+
+inputs = tokenizer("A decision between love and duty.", return\_tensors="pt")
+
+with torch.no\_grad():
+
+    output = model(\*\*inputs)
+
+
+
+embedding = output.last\_hidden\_state.mean(dim=1)
+
+print(embedding.shape)  # torch.Size(\[1, 6])
+
+
+