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.

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

Building

# Clone and build the main project
cd ggml
mkdir build && cd build
cmake ..
make -j8

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

# Run recursive self-improvement demo (NEW!)
./bin/recursive-self-improvement-demo
# Run meta-meta-reasoning capabilities demo (NEW!)
./bin/meta-meta-reasoning-demo

# Run comprehensive recursive self-improvement tests
./bin/test-recursive-self-improvement

# Test emergent behavior analysis (NEW!)
./test_emergent_behavior_analysis
./test_distributed_emergent_integration

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

5. Foundation Layer: CogUtil Utility Library

   • Type system for OpenCog atoms with inheritance
   • Configuration management and structured logging
   • Memory pool allocation and string utilities
   • Comprehensive error handling and validation

6. Component System: Formal Dependency Management

   • YAML-based component specifications
   • Automatic dependency validation at build time
   • Circular dependency detection and prevention
   • Build order enforcement and error handling

🌐 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 & Self-Improvement

• Agents model other agents' cognitive states
• Meta-reasoning about reasoning processes
• Recursive self-improvement - System optimizes its own optimization algorithms
• Intelligence bootstrapping - Creates meta-meta optimization loops
• Self-modifying parameters - Dynamic learning rate and attention adaptation
• Meta-optimization - Optimization algorithms that optimize other optimization algorithms
• Performance-based architecture adaptation - System modifies its own structure
• Automated architecture evolution - Dynamic creation/removal of cognitive modules, topology modification, and learning from successful adaptations

🏗️ Emergent Architecture

• Spontaneous role specialization
• Adaptive communication patterns
• Hierarchical structures from flat networks
• Real-time emergent behavior analysis - Detection and classification of spontaneous behaviors

🔍 Emergent Behavior Analysis

• Spontaneous Optimization Detection - Identifies improvement without explicit commands
• Emergent Cooperation Analysis - Detects superlinear performance gains from module interactions
• Phase Transition Recognition - Monitors major operational mode changes
• Novel Adaptation Patterns - Discovers new attention allocation strategies
• Performance Correlation Analysis - Statistical analysis of behavioral relationships

💡 Cognitive Grammar

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

Documentation

• Distributed Cognitive Architecture - Complete architectural overview
• Implementation Guide - Practical development guide
• Recursive Self-Improvement - NEW! Complete guide to recursive self-improvement capabilities
• Emergent Behavior Analysis - NEW! Real-time detection and analysis of emergent behaviors
• Cognitive Grammar Examples - Grammar system usage
• CogUtil Library - OpenCog utility library for foundation services
• Component Dependencies - Component dependency system and specifications

Demo Applications

Consciousness Exploration

# Demonstrates philosophical reasoning between agents
./bin/cognitive-agents-demo

The demo includes:

• Philosopher Agent: Specializes in consciousness concepts
• Scientist Agent: Focuses on neuroscience perspective
• Collaborative Reasoning: Cross-agent knowledge exchange
• Attention Management: Dynamic resource allocation
• Emergent Behavior Analysis: Real-time detection of emergent behaviors

MOSES Evolutionary Optimization

# Demonstrates program synthesis and symbolic regression
../bin/moses-demo

The MOSES demo includes:

• Evolutionary Program Synthesis: Automatic generation of programs through evolution
• Symbolic Regression: Learning mathematical functions from data
• Cognitive Integration: Programs represented in AtomSpace and cognitive tensors
• Prime-based Encoding: Uses Matula-Goebel encoding for program structures
• Meta-Optimization: Self-improving optimization parameters

Example Output

=== Consciousness Exploration Demo ===
Created cognitive agent 1751328539001 at localhost:8001
Created cognitive agent 1751328539002 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 1751328539001 sent cognitive tensor (type 1, attention 0.80, salience 0.56)

PLN Reasoning Engine

The integrated PLN (Probabilistic Logic Networks) reasoning engine provides sophisticated inference capabilities:

# Test PLN reasoning functionality
../bin/test-pln-reasoning

# Run enhanced cognitive agents with PLN
../bin/cognitive-agents-pln-demo

PLN Features:

• Advanced Inference Rules: Modus ponens, deduction, induction, abduction
• Forward/Backward Chaining: Automatic and goal-directed reasoning
• Truth Value Propagation: Uncertainty handling with strength and confidence
• Pattern Matching: Rule discovery and application
• Cross-Agent Reasoning: Collaborative inference between cognitive agents

Example PLN Operations:

// Modus Ponens: (A, A→B) ⊢ B
opencog_truth_value_t conclusion = opencog_pln_modus_ponens(premise_a, implication_ab);

// Deduction: (A→B, B→C) ⊢ (A→C)
opencog_truth_value_t chained = opencog_pln_deduction(ab_implication, bc_implication);

// Reasoning session with forward chaining
opencog_pln_session_t* session = opencog_pln_session_init(atomspace, 0.7f);
bool made_inference = opencog_pln_inference_cycle(session);

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

Real Distributed Communication

The system now supports true network-based cognitive agent communication:

Key Features

• Agent Discovery: Automatic registration and discovery of cognitive agents across the network
• State Synchronization: Real-time sharing of cognitive states, attention levels, and performance metrics
• Distributed Attention: Coordinated attention allocation across multiple agents
• Tensor Exchange: Direct sharing of neural tensor data between agents
• Workflow Coordination: Multi-agent collaborative cognitive workflows
• Health Monitoring: Heartbeat system with automatic failover

Quick Start

# Build with distributed communication support
cd ggml
mkdir build && cd build
cmake .. -DGGML_RPC=ON
make -j8

# Run basic distributed communication test
./bin/basic-test

# Run multi-agent distributed demo (requires separate terminals)
./bin/real-distributed-demo

Network Architecture

Agent A (localhost:8001) ←→ Agent B (localhost:8002)
     ↓                              ↓
 Cognitive State              Cognitive State  
 Attention Updates           Attention Updates
 Tensor Exchange             Tensor Exchange
 Workflow Requests          Workflow Responses

Example Usage

// Initialize distributed cognitive agent
distributed_cognitive_architecture_t* agent = 
    distributed_cognitive_init(ctx, "localhost:8001");

// Enable real distributed communication
distributed_cognitive_enable_real_communication(agent);

// Connect to remote agent
distributed_cognitive_connect_to_agent(agent, "localhost:8002", "RemoteAgent");

// Broadcast cognitive state to network
distributed_cognitive_broadcast_state(agent);

// Synchronize with network
distributed_cognitive_sync_with_network(agent);

Phase 3: Self-Modification

• Recursive self-improvement - System optimizes its own optimization algorithms
• Meta-optimization capabilities - Optimization loops that optimize other optimization loops
• Intelligence bootstrapping - Uses current intelligence to create better intelligence
• Self-modifying learning parameters - Dynamic adaptation of learning rates and attention allocation
• Automated architecture evolution - System modifies its own cognitive structure
• Emergent behavior analysis - Real-time detection and analysis of emergent behaviors
• Meta-meta-reasoning capabilities - Reasoning about reasoning processes with self-reflective pattern evolution

Contributing

This project represents a synthesis of:

• OpenCog cognitive architecture principles
• ggml tensor computation infrastructure
• GBNF grammar-constrained generation
• Economic attention allocation theories

Contributions are welcome in areas of:

• Cognitive reasoning algorithms
• Distributed systems optimization
• Grammar system enhancements
• Performance benchmarking

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. See individual component licenses for details.

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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.