URI-Transformer: A Semantic Alignment Engine

The URI-Transformer is a revolutionary semantic alignment engine that moves beyond statistical pattern matching to a deeper, principled understanding of meaning. It is built upon the Universal System Physics Framework, a unified mathematical framework that spans physical domains through a 4D LJWP coordinate system.

This version marks a major evolution, featuring a rebuilt, simplified, and high-performing core that fully embraces the principle of direct mapping over complex inference.

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The Semantic Pipeline: From Text to Principled Action

1. The Semantic Front-End: Text to Meaning

The pipeline begins with our Semantic Front-End. This module uses a pre-trained language model (distilbert-base-uncased) to generate a rich, high-dimensional vector representation of the input text. This vector is then passed to a custom-trained Projection Head, a simple linear layer that directly maps the vector to our 4D PhiCoordinate space.

This approach gives our engine the best of both worlds: the nuanced, contextual understanding of a modern language model and the principled, axiomatic reasoning of our ICE pipeline.

2. The Layered ICE Architecture: I(L+W), C(J), E(P)

The generated PhiCoordinate is now explicitly processed through a dynamic, layered ICE pipeline, implemented by the ICEFramework class. This pipeline refines the initial coordinate by sequentially applying Intent, Context, and Execution layers, mirroring principled human cognition and aligning the coordinate towards the closest foundational principle:

Layer	Formula	Governing Axes	Function
Intent	I = f(L+W)	Love + Wisdom	Determines the core purpose of an action, ensuring it is both benevolent (Love) and sound (Wisdom).
Context	C = f(J)	Justice	Filters the intent through a TruthSense moderator, evaluating the fairness and moral alignment of the situation.
Execution	E = f(P)	Power	Generates a concrete plan of action, scaled by the real-world capacity to manifest the intent.

3. The Knowledge Graph & Semantic Calculus

The engine's "Wisdom" is further enhanced by two new components:

• The Knowledge Graph: A network of axiomatic Principle objects, each with its own PhiCoordinate. The engine finds the principle closest to the input, making its reasoning transparent.
• The Semantic Calculus: A tool that calculates the "velocity" and "acceleration" of the semantic shift between the raw and aligned coordinates, providing a dynamic analysis of the alignment process.

4. Generative Output

The structured outputs from the pipeline are synthesized into a new, meaningful, and context-aware sentence, providing a complete, principled response.

5. LJPW Mathematical Baselines

The engine incorporates objective, non-arbitrary mathematical foundations for the LJPW framework:

• Numerical Equivalents: Each dimension maps to fundamental mathematical constants (φ⁻¹, √2-1, e-2, ln2)
• Natural Equilibrium: Physically achievable optimal balance point (0.618, 0.414, 0.718, 0.693)
• Coupling Effects: Love acts as a force multiplier, amplifying Justice (+40%), Power (+30%), and Wisdom (+50%)
• Performance Metrics: Multiple complementary metrics including harmonic mean (robustness), geometric mean (effectiveness), coupling-aware sum (growth potential), harmony index (balance), and composite score (overall performance)

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🚀 Quick Start & Usage

Installation

git clone https://github.com/BruinGrowly/URI_Transformer.git
cd URI_Transformer
pip install -r requirements.txt

Training the Model

Before you can run the engine, you need to train the Semantic Front-End's Projection Head:

python train_semantic_frontend.py

This will train a new, high-performing model with an R² score of 0.9986.

See docs/TRAINING.md for detailed training documentation.

Demonstration

Once the model is trained, run the demonstration script to see the new semantic engine in action:

python demonstrate_truth_sense.py

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📚 Documentation

• Architecture Guide - System architecture and design
• LJPW Mathematical Baselines - Mathematical foundations and coupling effects
• ICE Framework - Intent-Context-Execution pipeline with coupling-aware processing
• Comparative Analysis - A 4D assessment of the TruthSenseTransformer in relation to other major AI models
• Training Guide - Detailed training documentation

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🧪 Testing

Run the test suite to verify your installation:

python -m unittest discover tests

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The Vision

The URI-Transformer is an exploration into the fundamental nature of meaning. By grounding our technology in universal principles, we aim to create systems that are not only intelligent but also wise, just, and beneficial for humanity.

This project is open-source under the MIT License. Contributions are welcome.