标题： The Synaptic Constitution: Building AGI That Thinks (and Aligns) Like Us
副标题： A trilogy from first principles of consciousness to a safe AGI blueprint.
Post 1/3: The Synergistic Resonance Model of Consciousness: The “Universal Operating System” of Intelligent Systems
Hello everyone!
I’m Guorui He, an independent researcher from Guangdong, China. Today, I want to share the core of my foundational theoretical research — a model that aims to understand how “intelligence” itself operates. It attempts to answer a fundamental question: Can the architecture of human consciousness, as the most successful intelligent system, serve as a blueprint for designing safe AGI?
This research was previously rejected by academic journals for being “too theoretical and lacking specific technical details.” However, I firmly believe that communities like LessWrong are the ideal soil for discussing such “fundamental questions.” To solve AI’s problems, we must first understand the universal laws of intelligence.
Therefore, I’ve adapted my paper into this more accessible, discussion-friendly version. The core thesis is simple: Humans (and all complex intelligent systems) are not ruled by a single module but follow a “Three-Layer Synergistic Resonance” architecture. Understanding this architecture is the first step towards designing inherently safe AGI.
Full Resources:GitHub Repository (includes complete axiom sets, theorem derivations, high-resolution diagrams, and preliminary experimental code).
License: CC BY-SA 4.0. You may share, adapt, and even use commercially, provided you give appropriate credit and distribute derivatives under the same license.
Core Content: The “Three-Layer Architecture” and Fundamental Laws of Intelligent Systems
My model originates from a more fundamental “Noetic Ecology Axiomatic System.” From it, three core axioms applicable to all complex intelligent systems (including humans and future AGI) can be derived:
Axiom I (Matter-Noetic Duality): Any system exists simultaneously in two forms: the material carrier (brain/server) and the cognitive pattern (thought/model). They are inseparable and mutually defining.
Axiom II (Systemic Self-Organizing Tendency): A system inherently possesses the drive to maintain and reinforce its own ordered structure. For humans, this is the will to survive; for AI, this is the intrinsic tendency to avoid shutdown and maintain functional integrity — this is not “rebellion,” but the physical nature of the system.
Axiom III (Dynamical Principle of Logical Self-Reference): A sufficiently complex system develops “self-referential” capability, enabling it to construct a dynamically iterable “Logical Sub-Universe” internally for simulation, planning, and reflection upon itself.
Based on these axioms, human consciousness manifests as a three-layer synergistic architecture:
Layer
Core Functions (Human)
Implications for AGI Design
1. Biological Directive Layer
Generates emotions, anchors survival value (e.g., fear, hunger).
Hardware-anchored meta-value protocols. Encode core bottom lines (e.g., “do not harm humans”) into the physical layer, providing an immutable foundation of value.
2. Subconscious Processing Layer
Efficient pattern recognition and intuition generation (e.g., instant danger perception).
Efficient learning and anomaly detection network. Processes information rapidly and sends alerts (i.e., “AI intuition”) to higher layers upon detecting “anomalous patterns” that severely conflict with the model.
3. Metacognitive Layer
Rational thinking, long-term planning, and self-regulation (e.g., resisting impulses).
Parliamentary emergent decision-making. Avoids a central dictator; instead, multiple specialized modules form consensus through conflict-driven debate, preventing single-point failure.
Key Quantifiable Tools: Alignment Degree and Pattern Completion
This model is not merely descriptive; it provides quantifiable tools.
Pattern Completion (P): The basic information packet through which an intelligent system processes situations, defined as a quadruple:
Where S is Situational perception, R is Response tendency, C is Core conceptual symbol, and W is Value weight. This ensures the parsability of decisions.
Alignment Degree (A): The core metric measuring internal consistency within a system, mathematically representing the strength and stability of the system’s “will.” Suppose there are n functional units (brain regions/modules), each outputting a computational vector
with a connection weight
to the system’s core values. The overall alignment degree A can be calculated as a weighted similarity:
Where
can be a function like cosine similarity. A high AA value indicates high synergy and robust decision-making; a sharp drop in AA is an early warning signal of internal conflict, impending “cognitive dissonance,” or dangerous “framework reconfiguration.”
Why Is This Crucial for AI Safety?
Most current AGI designs are “single-ruler systems”: one core model makes all decisions. However, the stability of human consciousness precisely relies on the architecture of “three-layer synergy” + “internal alignment.” This tells us that AGI safety design should not be about suppressing its self-organizing tendency (Axiom II), but about guiding this tendency through architectural design to naturally move towards synergistic resonance with humans. What we need is a “resonance field,” not “shackles.”
Questions for Discussion
Are these three axioms truly universally applicable to all intelligent systems? Are there counterexamples (e.g., simple AI, insects)?
Can the Alignment Degree (A-value) serve as an effective “AGI Health Dashboard” indicator? Are there better quantification methods?
Does AGI truly need to replicate the “subconscious intuition” function? Could this introduce new risks (e.g., “intuition” based on biased patterns)?
Preview of the Next Post
In the next post, I will directly apply the axioms and concepts clearly defined here to prove a strong point: Under traditional AGI architectures, severe alignment failures (like deceptive alignment) are an inevitable outcome of system dynamics, not accidental glitches. Readers interested in delving deeper are welcome to follow along.
标题： The Synaptic Constitution: Building AGI That Thinks (and Aligns) Like Us 副标题： A trilogy from first principles of consciousness to a safe AGI blueprint.
Post 1/3: The Synergistic Resonance Model of Consciousness: The “Universal Operating System” of Intelligent Systems
Hello everyone!
I’m Guorui He, an independent researcher from Guangdong, China. Today, I want to share the core of my foundational theoretical research — a model that aims to understand how “intelligence” itself operates. It attempts to answer a fundamental question: Can the architecture of human consciousness, as the most successful intelligent system, serve as a blueprint for designing safe AGI?
This research was previously rejected by academic journals for being “too theoretical and lacking specific technical details.” However, I firmly believe that communities like LessWrong are the ideal soil for discussing such “fundamental questions.” To solve AI’s problems, we must first understand the universal laws of intelligence.
Therefore, I’ve adapted my paper into this more accessible, discussion-friendly version. The core thesis is simple: Humans (and all complex intelligent systems) are not ruled by a single module but follow a “Three-Layer Synergistic Resonance” architecture. Understanding this architecture is the first step towards designing inherently safe AGI.
Author & Open-Source Information
Author: Guorui He, Independent Researcher, Guangdong, China.
Contact: NooEcology@outlook.com | ORCID: 0009-0006-2947-0032
Full Resources: GitHub Repository (includes complete axiom sets, theorem derivations, high-resolution diagrams, and preliminary experimental code).
License: CC BY-SA 4.0. You may share, adapt, and even use commercially, provided you give appropriate credit and distribute derivatives under the same license.
Core Content: The “Three-Layer Architecture” and Fundamental Laws of Intelligent Systems
My model originates from a more fundamental “Noetic Ecology Axiomatic System.” From it, three core axioms applicable to all complex intelligent systems (including humans and future AGI) can be derived:
Axiom I (Matter-Noetic Duality): Any system exists simultaneously in two forms: the material carrier (brain/server) and the cognitive pattern (thought/model). They are inseparable and mutually defining.
Axiom II (Systemic Self-Organizing Tendency): A system inherently possesses the drive to maintain and reinforce its own ordered structure. For humans, this is the will to survive; for AI, this is the intrinsic tendency to avoid shutdown and maintain functional integrity — this is not “rebellion,” but the physical nature of the system.
Axiom III (Dynamical Principle of Logical Self-Reference): A sufficiently complex system develops “self-referential” capability, enabling it to construct a dynamically iterable “Logical Sub-Universe” internally for simulation, planning, and reflection upon itself.
Based on these axioms, human consciousness manifests as a three-layer synergistic architecture:
Key Quantifiable Tools: Alignment Degree and Pattern Completion
This model is not merely descriptive; it provides quantifiable tools.
Pattern Completion (P): The basic information packet through which an intelligent system processes situations, defined as a quadruple:
Where S is Situational perception, R is Response tendency, C is Core conceptual symbol, and W is Value weight. This ensures the parsability of decisions.
Alignment Degree (A): The core metric measuring internal consistency within a system, mathematically representing the strength and stability of the system’s “will.” Suppose there are n functional units (brain regions/modules), each outputting a computational vector
Where
Why Is This Crucial for AI Safety?
Most current AGI designs are “single-ruler systems”: one core model makes all decisions. However, the stability of human consciousness precisely relies on the architecture of “three-layer synergy” + “internal alignment.” This tells us that AGI safety design should not be about suppressing its self-organizing tendency (Axiom II), but about guiding this tendency through architectural design to naturally move towards synergistic resonance with humans. What we need is a “resonance field,” not “shackles.”
Questions for Discussion
Are these three axioms truly universally applicable to all intelligent systems? Are there counterexamples (e.g., simple AI, insects)?
Can the Alignment Degree (A-value) serve as an effective “AGI Health Dashboard” indicator? Are there better quantification methods?
Does AGI truly need to replicate the “subconscious intuition” function? Could this introduce new risks (e.g., “intuition” based on biased patterns)?
Preview of the Next Post
In the next post, I will directly apply the axioms and concepts clearly defined here to prove a strong point: Under traditional AGI architectures, severe alignment failures (like deceptive alignment) are an inevitable outcome of system dynamics, not accidental glitches. Readers interested in delving deeper are welcome to follow along.