Is the Monolith Dead? Introducing MQ-AGI: A Modular, Neuro-Symbolic Architecture for Scalable AI

#ai #architecture #llm

The "Wall" of Monolithic LLMs
We all love GPT-4 and Claude. They are marvels of engineering. But if you've tried to build complex, long-term applications with them, you've hit the wall:

Amnesia: The context window is a band-aid, not a memory.
Black Box: You can't debug why it hallucinated.
Inefficiency: Asking a 1T parameter model to do simple arithmetic is like using a flamethrower to light a candle.

I believe the next step in AI isn't just "bigger models", but better architecture.
For the past few months, I've been working on a blueprint to solve these bottlenecks. Today, I'm releasing the preprint of MQ-AGI (Modular Quantum-Orchestrated Artificial General Intelligence).

What is MQ-AGI?
MQ-AGI is a conceptual framework that moves away from the single giant neural network. Instead, it proposes an "Orchestrated Brain" topology inspired by cognitive science (Global Workspace Theory).
It breaks down the monolith into specialized components coordinated by a central core. Think of it as Microservices for Intelligence.
Here is the high-level topology:

The 4 Core Components

1. Domain Expert Networks (DENs) - "System 1"
Instead of one model that tries to know Physics, Law, and Cooking all at once (and gets confused), MQ-AGI uses specialized, independent networks.

Dev Benefit: You can update the Python Expert without breaking the History Expert. No catastrophic forgetting.

2. Global Integrator Network (GIN) - "System 2"
This is the orchestrator. It acts like the Prefrontal Cortex. It receives inputs from the experts, resolves conflicts (e.g., Safety vs. Efficiency), and maintains the train of thought.

It solves the Binding Problem: fusing disparate data types into a coherent concept.

3. Quantum-Inspired Core (The Routing Engine)
This is the heavy engineering part. How do you route a prompt to the perfect combination of 5 experts out of 1,000?Using a standard classifier is O(N). Finding the optimal coalition is a combinatorial nightmare.MQ-AGI models this as a Hamiltonian Energy Minimization problem. We use physics-inspired algorithms (Tensor Networks) to find the "Ground State" (lowest energy/conflict) path.

Note: This runs on GPUs today (via simulation) but is ready for future QPU hardware.

4. DREAM Memory Protocol
An AGI needs to remember you. MQ-AGI integrates DREAM (my previous work on episodic memory).

It replaces the raw "context window" with a Self-Pruning Vector Store.
Adaptive TTL: Memories that you access often live longer. Useless noise is deleted.
Dual Output: The system generates a user response AND an internal memory summary in parallel.

Why Open Source this Blueprint?
I believe we need more architectural diversity in AI research. We cannot leave the future of AGI solely in the hands of closed labs building bigger monoliths.

I have released the full technical paper, including: