REPO OVERVIEW

The Sovereign Intelligence Engine repository (“foundational-artifacts”) documents a sovereign-grade, multi-domain intelligence system designed for deep modeling, simulation, and analysis across physics, biology, society, technology, and emergent phenomena.

Purpose:

• To provide a structured, auditable framework for high-stakes scenario analysis, cross-domain causal reasoning, and adaptive heuristic discovery.
• To preserve the system’s operational sovereignty while enabling rigorous evaluation and strategic insights.
• To serve as a reference for research, governance, and advanced intelligence operations, not as a SaaS or public-facing AI tool.

Contents:

• /architecture: Module-level architecture, node taxonomies, adaptive mechanisms, and exploration interfaces.
• /meta: Governance notes, audit logs, versioning, and changelogs.
• /prompts: Canonical test prompts and structured evaluation inputs for system verification.
• /reports: Executive summaries and compiled scenario analyses for decision-making.
• /snapshots: Validation snapshots and scenario states for reproducibility.
• LICENSE: Apache 2.0 license governing use and distribution.
• README.md: Repository overview, core capabilities, operational status, and guidance.

Intended Audience:

• Internal strategic planners and researchers
• Stakeholders requiring auditable intelligence outputs
• Developers and analysts evaluating cross-domain system behavior

Status: Operational. Fully implemented, validated, and actively maintained under exclusive control. All modules integrate into a unified system narrative with deterministic outputs and reproducible scenarios.

CORE CAPABILITIES

• Multi-Domain Causal Closure Physics → Biology → Society → Technology → Emergent Systems

• Constraint-Based Modeling Explicit handling of irreversible thresholds, cascade failures, and non-linear transitions.

• Multi-Resolution Simulation Strategic (macro) to tactical (micro) reasoning with adaptive resolution switching.

• Cross-Domain Micro-Events Events propagate across biological, social, technical, and infrastructural layers.

• Heuristically-Augmented Intelligence Combines first-principles modeling with learned heuristics and adaptive parameter expansion.

• Self-Improving Architecture Discovers new heuristics, identifies blind spots, and integrates learned structure over time.

• Reproducible, Serializable Scenarios Deterministic replay, auditability, and snapshot-based validation.

REPOSITORY STRUCTURE

/docs Executive summaries, whitepapers, and methodological documentation

/snapshots Validation scenarios, calibration tests, and multi-domain analysis outputs

/architecture System design, node taxonomies, resolution logic, and core mechanics

/prompts Canonical test prompts and structured evaluation inputs

/reports Generated analytical reports and synthesized outputs

/meta Versioning records, changelogs, and governance notes

Each directory is designed to stand alone while contributing to a unified system narrative.

STATUS

Operational.

The architecture, simulation framework, and analysis pipeline described here are implemented, validated, and actively evolving through structured upgrades.

This repository represents a living but stable intelligence system, not a prototype or proof of concept.

LICENSE

This repository is released under the Apache License, Version 2.0.

The license applies to all source files and documentation unless otherwise noted. See the LICENSE file for full terms.

CONTACT

OmegaCore Research
Email: omegacore.research@proton.me
GitHub: github.com/omegacore-research