QGI - Tiered Invariant Architecture

QGI is a unified AI governance architecture built on ethical invariants and a four‑tier structure. It replaces fragmented rule stacks with a single, efficient pipeline that improves transparency, reduces overhead, and adapts seamlessly to global regulations.

The Problem

AI governance today is fragmented, reactive, and jurisdiction‑bound. Organizations maintain thousands of overlapping rules, duplicated compliance checks, and heavy post‑processing pipelines. This creates high compute overhead, brittle adaptation to new regulations, and inconsistent safety outcomes across systems and regions.

The Core Idea

QGI introduces a unified governance architecture built on ethical invariants and a 4‑tier structure. Instead of managing governance through scattered rules, QGI defines a small, stable set of constraints that apply universally, while allowing jurisdictions and organizations to map their requirements into the same invariant framework.
The result is a single, coherent governance pipeline.

The Four Tiers

Tier 1 — Universal Principles. Foundational principles that apply to all systems:

• Harmonized Balance — prevent harm and destructive feedback
• Co‑Existence — protect autonomy, dignity, fairness, transparency
• Co‑Expansion — ensure mutual benefit and adaptive growthg

Tier 2 — Ethical Invariants. Derived constraints that operationalize the principles:

• Non‑Harm
• Autonomy
• Opacity‑Limit
• Mutual‑Benefit
• Evolvability

These invariants replace thousands of conditional rules with a small, reusable constraint set.

Tier 3 — Jurisdiction Profiles. Maps global regulations into the invariant layer:

• GDPR
• CCPA
• EU AI Act
• PIPEDA

One invariant layer satisfies multiple regulatory frameworks.

Tier 4 — Local Configuration. Local, contextual, implementation‑specific constraints:

• System‑level risk thresholds
• Dataset sensitivity
• Organizational ethics extensions

Conceptually the final tier, but in runtime it becomes the first gate, determining whether a system may act autonomously or must escalate to a human.

Key Properties

• Unified pipeline: Reasoning and execution governance operate as one continuous flow.
• Invariant‑based: A small set of stable constraints replaces large rule stacks.
• Regulation‑agnostic: New laws map into existing invariants without rewriting governance logic.
• Compute‑efficient: Eliminates redundant checks and overlapping compliance layers.
• Auditable: Clear structure, fewer moving parts, easier to inspect and verify.

Impact (High‑Level Estimates)

• 60–85% reduction in governance code through invariant reuse and regulatory unification.
• Up to ~70% reduction in governance‑related compute by eliminating duplicated checks and fragmented pipelines.
• Zero model retraining for governance updates: new regulations or policy changes map into the invariant layer rather than modifying model weights.
• Higher transparency and auditability. a small, stable set of invariants makes governance logic easier to inspect, verify, and communicate.
• Faster adaptation to new laws and risk classifications through structural mapping instead of reactive patching.

Use Cases

• Foundation model governance
• High‑risk regulated sectors (finance, health, public sector)
• Cross‑border AI deployments
• Organizations seeking unified global compliance

Developed by Ella Wei — Quantum Genesis Research, Montreal