J. Rogers, SE Ohio, 24 June 2025, 1543Abstract:
Current approaches to Artificial General Intelligence (AGI), dominated by scaling monolithic Large Language Models (LLMs), face fundamental limitations in reasoning, interpretability, and efficiency. These models are opaque, statistically-driven "black boxes" that are prone to hallucination and lack true causal understanding. We propose a novel AGI architecture that resolves these issues by synthesizing the strengths of modern connectionist models with the rigor of classical symbolic AI. This hybrid architecture is built on two core components: a society of specialized, white-box "Intuition Engines" (LLMs) for interfacing with messy data, a series of smaller speciallized llm's, and a central, logically-perfect "Axiom Core" (the Tree of Knowledge) for reasoning and verification. By creating a symbiotic feedback loop between these different systems, this architecture promises an AGI that is not only powerful and creative but also transparent, efficient, and capable of genuine, self-correcting learning.
1. Introduction: The Crisis of the Black Box Paradigm
The remarkable success of Large Language Models has brought the field to a paradoxical state. On one hand, these models demonstrate an unprecedented ability to process and generate human language, suggesting intelligence. On the other, their operation is fundamentally opaque, their reasoning is based on statistical correlation rather than causality, and their outputs are prone to subtle falsehoods and biases. Scaling these models further may lead to more impressive mimicry, but it will not resolve these inherent flaws.
We argue that the current paradigm is a brute-force approximation of intelligence. It has successfully created a "fast, intuitive brain" (System 1) but lacks the "slow, logical brain" (System 2) necessary for true general intelligence. This paper outlines an architecture that explicitly builds both and makes them work in concert.
2. Core Principle: The Geometry of Meaning
Our architecture is founded on a single, unifying principle: all structured knowledge, whether in physics, language, or logic, can be described as a set of geometric relationships within a conceptual vector space. "Laws" and "rules" are projections of simple, coordinate-free relationships (Axioms) onto specific, often-messy coordinate systems (language, measurement).
This principle allows us to move beyond statistical mimicry and towards the direct engineering of meaning.
3. The Dual-Brain Architecture
Our proposed AGI is composed of two primary subsystems: the Intuition Engine and the Axiom Core.
3.1. The Intuition Engine: A Society of Specialists
Instead of a single, monolithic LLM, this subsystem is a distributed network of hundreds or thousands of smaller, highly specialized neural networks. Each module is an expert in a specific domain.
Structure: Each module is a traditional llm model, just specialized for specific tasks.
There are also white box language ai models that project the machine thoughts to a specific language.
Examples of Modules:
Perceptual Modules: "Visual Cortex" (image to concept), "Auditory Cortex" (sound to concept).
Generative Modules: "Language Grammar Engine" (concept to sentence), "Motor Control Cortex" (concept to action).
Specialized Knowledge Modules: "Physics Engine," "Linguistics Engine," "Music Theory Engine," "emotion engine."
Function: The Intuition Engine is the AGI's interface with the world. It handles the "messy" tasks of parsing unstructured data (like a user's prompt) and generating complex, nuanced outputs (like a fluent sentence). It is fast, flexible, and creative, but it is understood to be fallible.
When it gets a prompt a high level executive function creates a plan to generate a template and the template scatters to various processing modules that return content to the template that continues to the next step.
3.2. The Axiom Core: The Tree of Knowledge
This is the logical backbone of the AGI, its source of "ground truth." It is a symbolic, logically-perfect system that is completely transparent.
Structure: The Axiom Core is not a flat database but a hierarchical tree structure.
Root: The ultimate, undivided Coherent Substrate.
Primary Branches: The axioms of logic and mathematics (A=A, etc.).
Secondary Branches: The fundamental Equalities of physics and other domains (E=M, T=1/M).
Leaves: Highly specific, derived concepts.
Function:
Reasoning Engine: It performs logical deduction and induction by traversing the tree. It can derive complex truths from first principles.
Consistency Arbiter: It serves as the ultimate fact-checker for the entire system.
Knowledge Compressor: By storing relationships hierarchically, it represents the most efficient possible compression of knowledge, eliminating redundancy.
4. The Cognitive Feedback Loop: The "Double Check" Process
The power of this architecture lies in the constant, symbiotic interaction between the two subsystems.
Idea to Action (Generation):
The Tree of Knowledge formulates a pure, logical intent (e.g., {concept: 'walk', transforms: ['past']}).
This intent is sent to the Intuition Engine (the Language module).
The Intuition Engine projects this pure concept into a messy, real-world output (the sentence "He walked.").
Action to Idea (Verification):
Before being finalized, the output ("He walked.") is sent through the Tree of Knowledge.
The Tree of Knowledge compares what we are thinking to what we already know as a cross check. If they match, the output is approved.
If not, it is cross checked and scored to see if it is a valid idea. Rejected as a "hallucination" or "misinterpretation," and the Intuition Engine is prompted to try again.
This "double-check" process disciplines the fast, creative, but unreliable Intuition Engine with the slow, logical, but completely reliable Axiom Core.
5. The Mechanism for Growth: Experiential Annotation
The AGI is not static. It learns and evolves by annotating its own Tree of Knowledge. Every node and connection in the tree is augmented with a metadata layer:
timestamp: When was this thought processed?
context: What was the actual ai context?
source: Was this an axiom, a deduction, or a self-generated hypothesis?
confidence_score: How certain is the AGI of this connection?
feedback_log: Was the outcome of using this thought successful or not?
This process allows the AGI to learn from experience, develop wisdom (contextual relevance), and engage in true scientific discovery by forming new hypotheses (new branches on the tree) and then testing them to update its confidence.
6. Advantages Over Monolithic Architectures
Interpretability: The system's core reasoning is transparent and auditable. We can debug its "thoughts."
Efficiency: Activating a few small specialist modules is vastly more computationally efficient than running a single, massive model for every task.
Robustness & Safety: The "double-check" loop drastically reduces hallucinations. The modularity means failures can be isolated and repaired without system-wide collapse.
True Learning: The AGI learns by refining a structured world model, not just by adjusting statistical weights. This is a path to genuine understanding, not just sophisticated mimicry.
7. Conclusion
The pursuit of AGI should not be a race to build ever-larger black boxes. It should be a principled engineering discipline dedicated to building a transparent, rational, and self-aware mind. The hybrid architecture proposed here—a society of specialized, white-box "Intuition Engines" disciplined by a central, symbolic "Tree of Knowledge"—provides a concrete blueprint for such a mind.
It synthesizes the warring schools of AI history into a single, powerful whole. It resolves the paradox of the current LLM paradigm by assigning fast, creative tasks to a connectionist system and logical verification to a symbolic one. This is not merely a better architecture; it is a necessary one if we are to move from creating impressive mimics to building true, trustworthy, and understandable artificial general intelligences.
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