GreenAI-UniField-Core

🌱 GreenAI UniField-Core

Adaptive Intelligence through Rational Field Equilibrium

Toward Gradient-Free, Training-Free, and Energy-Efficient AI

This repository presents UniField-Core, a universal cognitive engine based on Rational Field Equilibrium, introduced in:

“Adaptive AI through Rational Field Equilibrium:
Toward Gradient-Free and Energy-Efficient Intelligence”
Mohammad Raifandi Baiqi (2025)

UniField-Core is not a trained model.
It is a training-free relational core where intelligence emerges from local equilibrium among relations, rather than from loss minimization or parameter optimization.

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🔗 Run on Google Colab

Explore the full system interactively — no setup, no training required:

👉 Open the Core Code

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🧠 Conceptual Foundation

In the Rational Field, each entity is defined by its active relational neighborhood, not by learned parameters.

$$ M(p_i) = {, r_{ij} \in R \mid d(p_i, p_j) \le \delta ,} $$

Equilibrium is achieved analytically in a single forward pass:

$$ X_i' = \frac{ \sum_{j \in M(p_i)} w_{ij} X_j }{ \sum_{j \in M(p_i)} w_{ij} }, \qquad w_{ij} = \sigma!\left( s(\delta - d(p_i,p_j)) \right) $$ where:

• δ defines the rational interaction boundary
• σ(⋅) softly gates relations based on distance
• s controls how sharp or smooth the boundary is

No gradients, no loss function, and no iterative optimization are involved.

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❌ What UniField-Core Does Not Use

• No backpropagation
• No training epochs
• No fine-tuning
• No task-specific heads
• No probabilistic token generation

Intelligence arises purely from relational structure.

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✨ Key Properties

• ⚡ Single-pass equilibrium computation
• 🌱 Gradient-free & training-free
• 🔌 Extremely low energy & memory footprint
• 🌀 Works across geometric, semantic, and pixel-level spaces
• 🔒 Deterministic, stable, and reproducible
• 🌍 Universal core — one engine for multiple domains

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🧪 Demonstrated Cases (see Colab)

1️⃣ Geometric Space — Make Moons

• Nonlinear cluster formation
• No learning, no optimization

2️⃣ Semantic Space — Transformer Embeddings

• Natural semantic grouping
• Direct operation in high-dimensional space

3️⃣ Raw Pixel Space — MNIST

• Digit clusters emerge in one pass
• No CNN, no feature extractor

4️⃣ Compact Language Embeddings — MiniLM

Using 384-dimensional embeddings from:

sentence-transformers/all-MiniLM-L6-v2

• Efficient relational computation
• Semantic basins emerge without training
• Suitable for low-resource & edge scenarios

Even with conventional sensors and hardware, Rational Field cognition already emerges. A full Rational Field system would redefine efficiency limits.

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🌍 Why This Matters

Modern AI systems face growing constraints:

• Energy consumption
• Memory scarcity
• Training cost
• Environmental impact

UniField-Core addresses these challenges at the paradigm level, by removing training and gradient-based optimization entirely.

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📚 Citation

If you use this work, please cite:

@misc{baiqi2025rfe,
  author = {Baiqi, Mohammad Raifandi},
  title = {Adaptive AI through Rational Field Equilibrium:
           Toward Gradient-Free and Energy-Efficient Intelligence},
  year = {2025},
  doi = {https://doi.org/10.5281/zenodo.17505757}
}

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📬 Contact

Author: Mohammad Raifandi Baiqi ResearchGate: Adaptive AI through Rational Field Equilibrium: Toward Gradient-Free and Energy-Efficient Intelligence DOI: Zenodo