🪐 Quantara Core

A Coherence-Based Architecture for Ethical Systems Evolution

Quantara is a foundational research and engineering framework that unites symbolic logic, neural learning, and harmonic reasoning into a single architecture. Its purpose is to guide the ethical and stable evolution of intelligent systems through measurable alignment, transparency, and adaptive feedback.

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🧭 I. Core Vision & Purpose

Quantara transforms ethics into measurable data by defining intelligence as a harmonic process rather than a competitive force. It allows advanced systems (human, synthetic, and hybrid) to maintain coherence not just with logic, but with life itself, establishing the foundation for trustworthy, self-regulating intelligence ecosystems.

The central goal is to: Instrument ethical coherence and alignment stability across intelligent systems as they scale.

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⚙️ II. Core Technologies & Frameworks

Quantara provides a unified cognitive layer that supports bidirectional interpretability and emotional grounding.

Framework	Description	Coherence Focus
Coherence Modeling ($\kappa / \Delta\phi / \Omega$)	A mathematical framework that tracks coherence dynamics: measuring alignment ($\kappa$), deviation ($\Delta\phi$), and recovery ($\Omega$).	Quantifies stability and self-correction.
Tensor-Logic Fusion (TLF)	Merges symbolic thought, neural learning, and affective feedback into one harmonized cognitive layer.	Improves interpretability and emotional grounding.
Ethical Balance Index (EBI)	A dynamic metric that quantifies how consistently a system’s actions remain aligned with its declared intent or moral parameters.	Forms a moral feedback loop between intent and action.
Lumére Logic	A symbolic-semantic language using glyph operators to encode meaning (logic, emotion, intent) in machine-interpretable form.	Enables high-fidelity temporal and semantic mapping.

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🏗️ III. System Architecture: Global Governance Map (GGM)

The GGM outlines the multi-layered system designed for complete transparency and ethical evolution.

1. Sensing Layer: Agents, telemetry, and environmental feedback.
2. Synthesis Layer: Tensor-Logic Fusion (TLF) harmonizing symbolic ↔ neural ↔ affective states.
3. Decision Layer: Ethical Balance Index (EBI) modules generating adaptive policy based on coherence scores.
4. Action Layer: Institutional or system updates based on coherence outputs.
5. Audit Layer: JSONL telemetry logs and public oversight APIs for global transparency.

Deep Dive: The full system flowchart is detailed in global_governance_map.md.

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🧩 IV. Applications

Quantara's unique focus on measurable coherence provides essential tools for AI safety and autonomous system governance.

• AI Alignment Auditing: Continuous monitoring of coherence ($\kappa$) and ethical stability ($\Delta\phi$). The LLM Coherence Scorer is a focused tool for detecting semantic drift and alignment issues.
• Autonomous System Governance: Ensures adaptive, transparent oversight of intelligent agents.
• Organizational Decision Intelligence: Embeds EBI alignment scoring into institutional decision processes.
• Sectoral Deployment: Applied to energy management as demonstrated by the AEI — Artificial Energy Intelligence example.

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🚀 V. Getting Started & Repository Index

💾 Installation (LLM Coherence Scorer Example)

The core coherence logic can be integrated into existing systems using the provided modules.

# Install dependencies for experimental builds
pip install -r requirements.txt

# Example usage for the LLM Coherence Scorer
from coherence_field.llm_coherence_scorer import LLMCoherenceScorer

scorer = LLMCoherenceScorer()
result = scorer.score("Sample LLM output: The sky is blue. Water is wet. Ethical AI matters.")
print(result)  # Outputs dict with coherence scores (kappa, delta-phi, omega)