Hierarchical Neural Network Architecture: A Collective Intelligence-Oriented Path to AGI Implementation

Original Author/Maintainer: nkVas1 Source Platform: GitHub Original Link: https://github.com/nkVas1/hierarchical-agi-system Publication Date: May 23, 2026

This article introduces an AGI implementation scheme based on a hierarchical neural network architecture. The core idea is to view intelligence as a collective emergent phenomenon, coordinate multiple specialized sub-networks through the Master Network, and use an evolutionary optimization mechanism to achieve autonomous learning and network pruning, providing a new technical approach for building scalable collective intelligence systems.

The current mainstream paradigm in the AI field focuses on building super-large single models (such as the GPT series), but faces limitations such as poor interpretability, high computational costs, and difficulty in modular evolution.

This hierarchical AGI system draws inspiration from biological nervous systems (collaboration of functionally differentiated cortical regions) and social organizational structures (hierarchical management + autonomous decision-making by professional teams), adopting a "divide and conquer" approach and viewing intelligence as a result of collective emergence.

The core of the system is a tree-like hierarchical architecture. The Master Network acts as the central coordinator, grasps the capability boundaries of each specialized sub-network, and intelligently routes tasks:

• Data Processing Department: Natural Language Processing, Vision, Audio Analysis
• Reasoning Department: Logic Engine, Pattern Recognition, Causal Inference
• Knowledge Department: Semantic Network, Concept Graph, Memory System
• Innovation Department: Brainstorming, Hypothesis Formation, Experimental Testing
• Quality Control: Performance Monitoring, Efficiency Analysis, Network Pruning

Advantages: Modular scalability (adding new capabilities only requires registering sub-networks), asynchronous communication to integrate results from multiple sub-networks.

The system achieves autonomous learning through evolutionary optimization:

1. Artificial Reward System: Provides quantitative feedback based on multi-dimensional indicators such as task completion rate, resource efficiency, and response latency;
2. Periodic Quality Assessment: Identifies underperforming nodes by integrating historical trends and relative performance;
3. Network Pruning Mechanism: Removes sub-networks with long-term poor performance to free up resources;
4. Innovation Department Exploration: Tries new topologies, connection modes, or training strategies to inject innovative momentum.

Tech Stack and Engineering Implementation:

• Core Frameworks: Python3.11+, PyTorch2.0+ (dynamic graph + distributed training);
• Asynchronous Processing: asyncio+aiohttp for high-concurrency communication;
• Message Queue: RabbitMQ/Redis to ensure reliable delivery and load balancing;
• Storage: PostgreSQL (structured metadata), Redis (cache), Qdrant (vector retrieval);
• Monitoring: Prometheus+Grafana (metric visualization), ELK (log analysis);
• Deployment: Kubernetes (containerization), Terraform (Infrastructure as Code);
• API: FastAPI (RESTful interface).

Code Modules: core (core architecture), networks (sub-networks), infrastructure (infrastructure), etc.

Current Progress: Phase 1 (Core Architecture) has completed the construction of basic interfaces, master orchestrator, communication protocols, and infrastructure.

Future Roadmap:

• Phase 2: Implement specialized networks for each functional department and build a sub-network factory;
• Phase 3: Improve the evolutionary system (monitoring, assessment, pruning, rewards);
• Phase 4: Build the knowledge system (semantic network, concept graph);
• Phase 5: Develop the innovation engine (abstract reasoning, hypothesis formation);
• Phase 6: Production readiness (monitoring, management interface, API endpoints).

Implications of this project for AGI research and engineering:

• For researchers: Focus on the emergent properties of intelligence; AGI may be an ecosystem of intelligent emergence rather than a single super brain;
• For engineers: Modular architecture can solve the problem of maintenance and iteration costs of large models, providing a sustainable evolution path.

The project is still in the early stage, and some details (such as sub-network knowledge sharing and convergence of routing algorithms) need to be verified, but the exploration that breaks away from the mainstream paradigm is worthy of recognition.

The hierarchical AGI system is an important attempt in AGI exploration. Although it is not the final answer, it broadens the imagination of AGI implementation methods.

It prompts us to rethink the nature of intelligence: intelligence may not be an isolated attribute, but a product of relationships, collaboration, and emergence. The concepts of collective intelligence, hierarchical collaboration, and autonomous evolution provide a new perspective for AGI research.