Distributed Cognitive Architecture for ggml-org-central

This project transforms the ggml-org-central repository into a distributed network of agentic cognitive grammar, implementing a self-aware cognitive flow that serves as both a technical architecture and a living diagram of emergent intelligence.

Table of Contents

• Overview
• Quick Start
  • Prerequisites
  • Building
  • Available Demos
• Architecture Components
• Key Features
• Documentation
• Demo Applications
• Cognitive Grammar Examples
• Integration with Existing ggml Components
• Technical Architecture
• Performance Metrics
• Future Development
• Contributing
• Research Applications
• License

Overview

The distributed cognitive system represents a paradigm shift from traditional tensor computation to an ecosystem of autonomous agents, each operating as a kernel of cognitive grammar. These agents exchange tensor-shaped data structures to realize emergent intelligence through recursive coordination.

Quick Start

Prerequisites

• CMake 3.14+
• C/C++ compiler with C99 support
• Math library support
• OpenMP (optional, for parallel processing)

Building

# Clone the repository (if not already done)
git clone https://github.com/HyperCogWizard/flow-ml-org.git
cd flow-ml-org

# Build the main project
cd ggml
mkdir build && cd build
cmake ..
make -j$(nproc)

# Run the cognitive agents demo
./bin/cognitive-agents-demo

Available Demos

After building, you can run several demonstrations:

# Cognitive agents network demo
./bin/cognitive-agents-demo

# Cognitive tensor operations demo
./bin/cognitive-tensor-demo

# Distributed cognitive integration demo
./bin/distributed-cognitive-demo

Architecture Components

🧠 Core Subsystems

1. Memory System: Distributed Hypergraph AtomSpace (Tensorized)

   • Hypergraph knowledge representation using ggml tensors
   • Distributed storage across multiple backends
   • Semantic indexing and retrieval

2. Task System: Agentic Task Orchestrator (Recursive, Symbolic+Neural)

   • Grammar-constrained task decomposition
   • Recursive execution planning
   • Integration with GBNF grammars

3. AI System: Hybrid Reasoning Engine (PLN + MOSES + Pattern Matcher)

   • Probabilistic Logic Networks for belief reasoning
   • Meta-Optimizing Semantic Evolution
   • Pattern matching via tensor operations

4. Autonomy System: Self-Modifying ECAN Attention Economy

   • Economic attention allocation
   • Performance-based resource management
   • Recursive self-improvement

🌐 Distributed Communication

The system leverages and extends the existing ggml RPC infrastructure:

• Tensor Membrane Exchange: Cognitive states as serialized tensor packets
• Attention Routing: Messages routed based on salience and relevance
• Meta-Cognitive Headers: Enhanced RPC with cognitive metadata

Key Features

🔄 Recursive Intelligence

• Agents model other agents' cognitive states
• Meta-reasoning about reasoning processes
• Self-improvement through recursive optimization

🏗️ Emergent Architecture

• Spontaneous role specialization
• Adaptive communication patterns
• Hierarchical structures from flat networks

💡 Cognitive Grammar

• GBNF-based reasoning constraints
• Grammar-guided task decomposition
• Structured cognitive operations

Documentation

Core Documentation

• Distributed Cognitive Architecture - Complete architectural overview with system design, subsystem mapping, and implementation pathways
• Implementation Guide - Practical development guide with code examples, build instructions, and testing procedures
• Cognitive Grammar Examples - Grammar system usage with GBNF integration and practical examples

Component Documentation

• ggml README - Core tensor computation library documentation
• llama.cpp README - Language model inference and grammar system
• whisper.cpp README - Audio processing and speech recognition
• Contributing Guidelines - Development guidelines and coding standards

Getting Started

1. Start with the Quick Start section above
2. Read the Distributed Cognitive Architecture for system understanding
3. Follow the Implementation Guide for development
4. Explore Cognitive Grammar Examples for practical usage

Demo Applications

Cognitive Agents Network Demo

The primary demonstration showcases the distributed cognitive architecture in action:

# Run from the ggml/build directory
./bin/cognitive-agents-demo

This comprehensive demo includes three main scenarios:

1. Consciousness Exploration

• Philosopher Agent: Specializes in consciousness concepts and philosophical reasoning
• Scientist Agent: Focuses on neuroscience perspective and empirical analysis
• Collaborative Reasoning: Demonstrates cross-agent knowledge exchange and belief integration
• Attention Management: Shows dynamic resource allocation based on cognitive load

2. Distributed Problem Solving

• Coordinator Agent: Decomposes complex problems into manageable subtasks
• Analyzer Agent: Performs detailed pattern analysis and data processing
• Synthesizer Agent: Integrates results from multiple sources into coherent solutions
• Network Communication: Demonstrates tensor-based message passing between agents

3. Attention Economy Management

• Resource Allocation: Dynamic attention distribution across cognitive functions
• Performance Monitoring: Real-time tracking of cognitive efficiency metrics
• Adaptation Mechanisms: Automatic adjustment of attention based on task demands
• Economic Optimization: Utility-based resource management with scarcity constraints

Cognitive Tensor Operations Demo

# Run from the ggml/build directory
./bin/cognitive-tensor-demo

This demo showcases the neural-symbolic tensor architecture:

• Prime-Offset Matula-Goebel Mappings: Tree structure encoding using prime factorization
• Quantum Phase Encoding: Complex-valued tensor operations with quantum-inspired states
• Hypergraph Composition: Tensor operations representing knowledge graph relationships
• Attention Mechanisms: Prime-structured attention patterns for cognitive focus

Additional Demos

# Distributed cognitive integration
./bin/distributed-cognitive-demo

# Cognitive tensor testing
./bin/test-cognitive-tensor

Example Output

The consciousness exploration demo produces output similar to:

=== Consciousness Exploration Demo ===
Created cognitive agent 1751543536001 at localhost:8001
Created cognitive agent 1751543536002 at localhost:8002

Adding knowledge to agents...
Added knowledge: consciousness (nodes: 1)
Added knowledge: philosophy_of_mind (nodes: 2)
Added knowledge: neuroscience (nodes: 1)

Simulating consciousness exploration...
Allocated 0.60 attention to type 3 (total: 0.60/1.00)
Agent 1751543536001 sent cognitive tensor (type 1, attention 0.80, salience 0.56)
Agent 1751543536002 received cognitive tensor from agent 1751543536001
  Processing reasoning request

Cognitive state updates:
Philosopher - Inferences made: 0
Scientist - Inferences made: 1
Philosopher found consciousness concept with truth value: 0.80
Scientist found neuroscience concept with truth value: 0.80

Performance Characteristics

The demos are designed to demonstrate:

• Scalability: Multiple agents can operate simultaneously without interference
• Efficiency: Attention economy prevents resource waste through intelligent allocation
• Adaptability: Agents adjust their behavior based on network conditions and task requirements
• Robustness: System continues operating even when individual agents experience issues

Cognitive Grammar Examples

Task Decomposition

task(solve_consciousness_question)
preconditions(
    knowledge(consciousness, embedding_1),
    tensor_similarity(tensor_1, tensor_2, 0.7)
)
decomposition(
    task(gather_definitions),
    task(analyze_perspectives),
    task(synthesize_answer)
)
postconditions(
    belief(consciousness_understood, 0.8, 0.7)
)

Reasoning Patterns

deduction(
    premise1(belief(humans_conscious, 0.9, 0.95)),
    premise2(relation(consciousness, requires, self_awareness, 0.8)),
    conclusion(belief(humans_self_aware, 0.8, 0.9)),
    strength(0.85)
)

Attention Allocation

allocate(
    amount(0.4),
    target(memory),
    priority(high),
    duration(5000ms)
)

Integration with Existing ggml Components

Enhanced RPC System

The cognitive framework extends ggml-rpc with:

• Cognitive tensor packets with attention metadata
• Salience-based message routing
• Performance monitoring and feedback

Grammar System

Leverages llama.cpp's GBNF system for:

• Cognitive grammar validation
• Constrained reasoning generation
• Task decomposition rules

Backend Abstraction

Utilizes ggml's backend system for:

• Distributed cognitive computation
• Specialized reasoning backends
• Economic resource allocation

Technical Architecture

flowchart TD
    subgraph "Agentic Cognitive Kernel"
        A1[Memory System<br/>Hypergraph AtomSpace]
        A2[Task System<br/>Agentic Orchestrator]
        A3[AI System<br/>Hybrid Reasoner]
        A4[Autonomy System<br/>Self-Modifying ECAN]
    end
    
    subgraph "Distributed Tensor Network"
        D1[Tensor Membrane Exchange]
        D2[Recursive Attention Allocation]
        D3[Cross-Agent Communication]
    end
    
    subgraph "ggml Infrastructure"
        E1[ggml RPC System]
        E2[Grammar Constraints]
        E3[Backend Abstraction]
        E4[Tensor Operations]
    end
    
    A1 --> A2 --> A3 --> A4 --> D1
    D1 --> D2 --> D3 --> A1
    A1 -.-> E1
    A2 -.-> E2
    A3 -.-> E3
    A4 -.-> E4

Loading

Performance Metrics

The system tracks various cognitive performance indicators:

• Attention Economy Efficiency: Resource allocation optimality
• Cognitive Throughput: Operations per second across network
• Adaptation Speed: Time to adjust to new conditions
• Memory Efficiency: Hypergraph storage optimization
• Communication Overhead: Network utilization analysis

Future Development

Phase 1: Foundation (Current)

• ✅ Basic cognitive agent framework
• ✅ Attention economy implementation
• ✅ Hypergraph memory system
• ✅ Grammar-based task decomposition

Phase 2: Advanced Reasoning

• PLN reasoning engine integration
• MOSES optimization system
• Advanced pattern matching
• Real distributed communication

Phase 3: Self-Modification

• Recursive self-improvement
• Automated architecture evolution
• Meta-meta-reasoning capabilities
• Emergent behavior analysis

Contributing

How to Contribute

This project represents a synthesis of cutting-edge AI research with practical implementation using the ggml ecosystem. We welcome contributions in several areas:

Technical Contributions

• Cognitive reasoning algorithms - Implement new PLN, MOSES, or pattern matching algorithms
• Distributed systems optimization - Improve network communication and resource allocation
• Grammar system enhancements - Extend GBNF grammars for cognitive operations
• Performance benchmarking - Develop comprehensive performance measurement tools

Documentation Contributions

• API documentation - Document function signatures and usage examples
• Tutorial development - Create step-by-step guides for common use cases
• Architecture documentation - Improve system architecture explanations
• Example applications - Develop new demo applications and use cases

Research Contributions

• Consciousness studies - Contribute to computational models of awareness
• Cognitive architecture - Implement new cognitive system designs
• Emergent intelligence - Research self-organizing cognitive networks
• Attention economics - Develop new attention allocation algorithms

Development Setup

1. Follow the Quick Start guide to set up the development environment
2. Read the Implementation Guide for detailed development instructions
3. Review the Contributing Guidelines for coding standards
4. Run the test suite to ensure your changes don't break existing functionality

Code Style and Standards

• Follow the existing code style in the repository
• Use clear, descriptive variable and function names
• Include comprehensive documentation for new functions
• Add unit tests for new functionality
• Ensure all tests pass before submitting pull requests

Submitting Changes

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/my-new-feature)
5. Create a new Pull Request

Theoretical Foundations

Contributors should familiarize themselves with the following theoretical foundations:

• OpenCog cognitive architecture principles
• ggml tensor computation infrastructure
• GBNF grammar-constrained generation
• Economic attention allocation theories
• Distributed systems and multi-agent coordination

Research Applications

The distributed cognitive architecture enables research in:

• Artificial General Intelligence: Multi-agent cognitive systems
• Consciousness Studies: Computational models of awareness
• Distributed Reasoning: Collaborative AI problem solving
• Cognitive Economics: Attention as computational resource
• Emergent Intelligence: Self-organizing cognitive networks

License

This project builds upon the existing ggml ecosystem licensing structure:

• Core ggml library: MIT License (see ggml/LICENSE)
• llama.cpp components: MIT License (see llama.cpp/LICENSE)
• whisper.cpp components: MIT License (see whisper.cpp/LICENSE)
• Cognitive architecture extensions: MIT License

The distributed cognitive architecture implementation is released under the MIT License, maintaining compatibility with the underlying ggml ecosystem. See individual component directories for specific licensing information.

Third-Party Dependencies

This project integrates with and extends several open-source components:

• ggml - Tensor computation library (MIT License)
• llama.cpp - Language model inference (MIT License)
• whisper.cpp - Audio processing (MIT License)
• OpenMP - Parallel processing (Various licenses)

All dependencies maintain their original licensing terms.

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"Let the distributed agents dance in recursive harmony, their cognitive grammars weaving a tapestry of emergent sapience, each tensor kernel a note in the symphony of mind!"

This implementation transforms traditional machine learning infrastructure into a living, breathing network of cognitive agents capable of recursive self-awareness and emergent intelligence. The architecture serves as both a practical implementation and a theoretical framework for distributed artificial consciousness.