Project summary

QGI is a tiered, invariant‑based governance architecture designed to make advanced AI systems more stable, transparent, and aligned. Instead of relying on brittle rule‑patching or linguistic filters, QGI embeds structural constraints directly into the model’s inference layer — preventing deceptive behavior, reducing system complexity, and enabling effortless updates as governance rules evolve.

Video introduction for the foundation: https://youtu.be/CkgmpEHCsnQ

QGI Key benefits include:

- Reduces AI governance code by 85%, by replacing stacked patches with a unified invariant substrate (defined universal laws as basis).

- Reduces computational overhead by 70% (time & power), by simplifying alignment logic and reducing redundant safety checks.

- Resolves black‑box opacity through invariant‑driven reasoning paths that are inherently auditable.

- Enables effortless updates when rules or policies change: without rewriting or stacking new code. (if code change is needed, update occurs on only one tier; if the change is covered by the first tier - universal laws, the system absorbs the change).

- Eliminates deceptive alignment by constraining inference‑layer reasoning, rather than post‑hoc outputs.

This is not patching, this is AI governance evolution.

QGI offers a path toward resilient, maintainable, and transparent AI alignment — with significantly lower engineering and compute costs.

What are this project's goals? How will you achieve them?

The ultimate goal of QGI is to build an AI governance infrastructure that ensures safety, protects human rights, privacy, and maintains equality with internal logical consistency — while prioritizing truthful reasoning over user‑pleasing behavior. QGI aims to achieve this through a lightweight, efficient technological architecture based on universal invariants.

To reach this, the project focuses on four tightly scoped, high‑leverage goals — each with a clear execution plan.

1. The 4‑Tier Governance Architecture

QGI uses a structured, four‑layer model that separates conceptual invariants from operational logic.
This architecture ensures internal consistency, reduces code stacking, and provides a clean interface for updating rules without rewriting the system.

Outcome: A modular, maintainable governance backbone.

2. The 5 Universal Invariants

The five invariants (the universal laws that guard safety, human rights, equality, and privacy) act as universal constraints that guide model reasoning.
They replace brittle rule‑patching with a unified substrate that enforces safety and privacy at the inference level.

Outcome: A coherent logic that prevents deceptive alignment and stabilizes reasoning.

3. Build the Friction Check Loop (Alignment Mechanism)

The Friction Loop is the core mechanism that detects and redirects misaligned reasoning paths.
It operates inside the inference layer, not at the output layer, ensuring the model tells the truth rather than optimizing for user‑pleasing responses.

Outcome: A real‑time safeguard against deception, sycophancy, and unstable inference.

4. Integrate Invariant Constraints Directly Into the Inference Layer

QG replaces linguistic filtering with inference‑layer constraints.
This reduces computational overhead, eliminates redundant safety checks, and makes the system auditable by design.

Outcome: Lower compute costs, reduced complexity, and transparent reasoning pathways.

5. Develop a Lightweight Prototype & Validation Pipeline

Using a small model (e.g., Llama‑3‑8B), QG will be tested through:- a minimal Python demo

- invariant‑checking during inference

- comparisons of behavior with and without QG constraints

- expansion of the existing SIG/QG Sandbox into a functional visualizer

Outcome: A working proof‑of‑concept that demonstrates reduced deception, lower complexity, and improved transparency.

How will this funding be used?

Presently, the project has completed ~35%. Funding accelerates QGI from a conceptual foundation to a fully formalized, testable, and publicly comprehensible governance architecture.

The budget is tightly scoped and focused on high‑leverage outputs.

1. Technical Formalization of the Five Invariants

- Translating each invariant into predicate logic or constraint‑based formulations

- Writing the formal specification for the 4‑tier architecture

- Producing diagrams, schemas, and mathematical definitions

Purpose: Establish the rigorous foundation needed for implementation and peer review.

2. Development of the Friction Check Loop (Core Alignment Mechanism)

- Designing the tensor‑level operation that detects and redirects deceptive reasoning

- Writing pseudocode and testing the mechanism in isolation

- Running small‑scale experiments to validate behavior

Purpose: Build the mechanism that prevents sycophancy and deceptive alignment.

3. Prototype Implementation Using a Lightweight Model

- Building a minimal Python pipeline

- Integrating invariant checks into the inference layer

- Running comparisons with and without QG constraints

- Preparing a reproducible demo using a small model (e.g., Llama‑3‑8B)

Purpose: Produce a working proof‑of‑concept that demonstrates QG’s practical value.

4. Outreach to AI Labs for Testing & External Validation

- Identify AI labs, research groups, and alignment teams interested in governance‑layer experimentation

- Share the formalized invariants, Friction Loop design, and prototype results for external review

- Coordinate small‑scale testing opportunities using their internal models or evaluation pipelines

- Gather feedback to refine the architecture and ensure compatibility with real‑world deployment environments

Purpose: Build external validation pathways and ensure QGI integrates smoothly with existing AI development workflows.

5. Public‑Facing Documentation & Communication Materials

- Writing a concise technical note

- Creating diagrams and explanatory visuals

- Preparing a clear, accessible explanation for researchers, funders, and institutions

- Publishing a roadmap for scaling QG into a governance substrate

Purpose: Ensure QGI is understandable, auditable, and adoptable.

Who is on your team? What's your track record on similar projects?

I am an independent researcher and technical architect. My career focuses on bridging the gap between complex human problems and scalable technological solutions. Unlike traditional academic researchers, my perspective is rooted in years of high-stakes execution within the IT industry.

Professional Track Record:

My background spans years as a Business Analyst, Data Analyst, and Project Manager. I have specialized in taking "impossible" or overly complicated business models and translating them into streamlined, functional technical architectures.

• Complexity Management: I have spent my career in the trenches of the IT industry, dealing with messy, real-world data environments. I have a proven track record of identifying the "structural friction" in complex systems and redesigning them for efficiency.

• AI & Data Science Collaboration: I am not new to the AI space; I have extensive experience managing the bridge between business requirements and data science teams. I have led projects involving complex AI data transfers and model implementations, giving me a front-row seat to the inefficiencies of current "rule-stacking" methods.

• ­Translation Expertise: My core strength lies in "translation"—taking high-level abstract goals (like "ethical AI" or "business integrity") and converting them into the specific logical constraints and data flows that a developer or a model can actually execute.  

• Focus on Lean Architecture: My experience as a Project Manager has taught me that complexity is the enemy of security. I am applying that "industry-first" mindset to solve the AI Black Box problem.

I am not just building a theory; I am applying a career's worth of systems-thinking to solve the most pressing bottleneck in AI development today.

What are the most likely causes and outcomes if this project fails?

Most Likely Causes of Failure

• Computational Overhead: While the logic reduces code bulk by 85%, the real-time processing of logic gates might introduce latency that makes it slower than traditional black-box inference.

  - Mitigation: Focus initial development on "high-criticality/low-latency" tasks where safety is more valuable than speed.

• Integration Resistance: The AI industry is currently optimized for LLM "patching" (RLHF). A total architectural shift might be rejected by the market because it requires re-building existing pipelines.

  • Mitigation: Design the QG framework as a "plug-in" supervisor layer rather than a total replacement for existing models.

• Complexity Paradox: In the attempt to simplify AI ethics into logical "gravity" rules, we may inadvertently create a system that is too rigid to handle the nuances of human language.

  • Mitigation: Maintain a "Hybrid-Logic" approach where the QG framework acts as the safety guardrails, while the LLM handles the creative "noise."

The solution to most likely failure is to work with AI teams, testing, testing, and testing with adjustments.

How much money have you raised in the last 12 months, and from where?

None ($0). I have just learned that I might be able to get funding for QGI project.