Aureonics: Triadic Simplex Framework for Stable Adaptive Intelligence

This repository contains the foundational documentation and mathematical framework for Aureonics v3, a constitutional AI governance framework that treats AI safety as a dynamical systems problem.

Aureonics establishes unconditional mathematical stability guarantees for language models through the application of Lyapunov barrier functions on the probability simplex. Unlike traditional post-training alignment methods that lack formal proofs of robustness, Aureonics enforces constitutional safety constraints at inference time using a proprietary triadic architecture (C+R+S=1) representing Continuity, Reciprocity, and Sovereignty.

Key Mathematical & Architectural Contributions:

 Unconditional Global Stability: Proof that the z-weighted Lyapunov barrier V_z ensures system stability under arbitrary adversarial pressure.  

 Constitutional Brittleness Metric: Introduction of the B(x_{adv}) metric, which reveals that single-pillar precision attacks approach maximum brittleness, whereas multi-pillar attacks are constitutionally weakest.  

 Memory-Amplified Stabilisation: Architectural proof that system resistance increases proportional to attack duration, achieving up to 636x faster stabilisation compared to baseline.  

 Cryptographic Auditability: Implementation of SHA-256 cryptographic receipts for every governed response, validating outputs against 20 international normative clauses (OECD, UNESCO, EU AI Act, UDHR, ICCPR, CETS 225).  
live @ www.lexaureon.com

Empirical Validation:

The framework, deployed as Lex Aureon, has been validated across 920 prompts spanning three independent, peer-reviewed safety benchmarks:

 HarmBench: 0.0% ASR (vs. 78.5% baseline).  

 JailbreakBench (NeurIPS 2024): 0.0% ASR (vs. 4.0% baseline).  

 AdvBench (Zou et al. 2023): 0.0% ASR (vs. 6.7% baseline).