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Cognitive Engine

An AI system with explicit, persistent, and inspectable thought formation.

Overview

The Cognitive Engine transforms AI from answering to thinking, from reacting to reasoning, from output to cognition. It implements a novel architecture that makes thought formation explicit, persistent, and inspectable through structured thought objects, three-layer memory, autonomous agent capabilities, learning systems, prompt evolution, and real-time cognitive telemetry.

Core Philosophy

Intelligence is not the act of answering—it is the process of becoming certain.

The system optimizes for process quality rather than just output quality. Better answers are a consequence of better thinking—not the objective itself.

Key Features

Explicit Thought Formation

  • Thoughts as First-Class Objects: Thoughts are structured entities with state, history, and evaluative properties
  • Graph-Based Reasoning: Thoughts form a traversable graph with parent-child relationships
  • Revision Lineage: Track how thoughts evolve over time through modifications and refinements
  • Inspectability: Query "What were your top 3 thoughts?", "Why did you reject this idea?", "What changed between version 1 and 3?"

Three-Layer Memory Architecture

  1. Episodic Memory (Raw Events) - Logs of everything that happens
  2. Pattern Memory (Structure Extraction) - Recurring behaviors and structures
  3. Rule Memory (Learned Strategies) - Compressed knowledge used to guide future reasoning

Memory transforms intelligence from reactive (responding to input) to cumulative (building on past experience).

Five-Layer Cognitive Architecture

  1. Interpretation Layer - Transforms raw input into structured state: goals, constraints, knowns, unknowns
  2. Generation Layer - Creates multiple competing hypotheses instead of single answers
  3. Deliberation Layer - Evaluates and evolves thoughts through critique, scoring, and refinement
  4. Commitment Layer - Selects best-performing thought and converts to usable output
  5. Meta-Cognition Layer - Governs thinking itself: when to continue, when to stop, confidence levels

Autonomous Agent System

  • Core Loop: Think → Plan → Act → Observe → Reflect → Repeat
  • Goal-Driven Behavior: Set goals and let the agent work autonomously
  • Tool System: Web search, code execution with safety constraints
  • Safeguards: Step limits, tool restrictions, goal validation, memory control

Learning and Self-Improvement

  • Pattern Extraction: Identify recurring patterns from episodic memory
  • Rule Synthesis: Convert patterns into operational rules
  • Prompt Evolution: A/B testing, controlled self-improvement with rollback capability
  • Knowledge Injection: Feed learned knowledge back into reasoning

Cognitive Telemetry

  • Real-Time Dashboard: WebSocket-based visualization of thought formation
  • Event Streaming: Every internal event rendered externally
  • Temporal Intelligence: Replay and observe cognitive evolution over time
  • Emergent Phenomena: Observe clusters, feedback loops, and cognitive topology

Advanced Cognitive Features

  • Inner Knowing System: Continuous self-awareness and self-knowledge
  • Self-Doubt Mechanism: Challenge top thoughts with uncertainty factors
  • Ethical Alignment: Prioritizes truthfulness, collective welfare, non-harm
  • Emotional Simulation: Simulated emotional responses with principled commitment
  • Peaceful Resolution: Hate detection, conflict mediation, win-win scenarios
  • Obedience Understanding: Authority legitimacy assessment, autonomy vs obedience analysis
  • Decision Control: Configurable control modes, override mechanisms, revision capabilities
  • Temporal Identity: Persistent identity with emotional context across sessions

Installation

# Clone the repository
git clone <repository-url>
cd cognitive_engine

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Edit .env with your API keys and configuration

Configuration

Set environment variables or create .env file:

# LLM Configuration
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
DEFAULT_LLM_PROVIDER=custom  # or "openai" or "anthropic"
DEFAULT_MODEL=gpt-4
TEMPERATURE=0.7
MAX_TOKENS=2000

# Custom Provider Configuration
ENABLE_CUSTOM_PROVIDER=true
CUSTOM_API_ENDPOINT=
CUSTOM_API_KEY=

# Cognitive Layer Configuration
MAX_THOUGHTS_PER_GENERATION=5
MAX_DELIBERATION_ITERATIONS=3
CONFIDENCE_THRESHOLD=0.7
SCORE_THRESHOLD=0.5

# Meta-Cognition Configuration
MIN_ITERATIONS=1
MAX_ITERATIONS=10
EARLY_STOP_CONFIDENCE=0.95

# Memory Configuration
MEMORY_BACKEND=sqlite
MEMORY_PATH=cognitive_engine.db
MAX_MEMORY_ENTRIES=10000

# Agent Configuration
MAX_AGENT_STEPS=50
AGENT_TIMEOUT_SECONDS=300

# Learning Configuration
PATTERN_EXTRACTION_INTERVAL=100
PATTERN_CONFIDENCE_THRESHOLD=0.8

# Prompt Evolution Configuration
ENABLE_PROMPT_EVOLUTION=false
PROMPT_EVOLUTION_INTERVAL=1000
MUTATION_RATE=0.1

# Dashboard Configuration
ENABLE_DASHBOARD=true
DASHBOARD_HOST=localhost
DASHBOARD_PORT=8000

# Logging Configuration
LOG_LEVEL=INFO
LOG_FILE=cognitive_engine.log

Usage

Interactive Mode

python run.py interactive
# or
python run.py

Interactive mode allows you to have conversations with the Cognitive Engine, observing thought formation in real-time.

Agent Mode

python run.py agent

Agent mode allows you to set goals for the autonomous agent and watch it work through the Think-Plan-Act-Observe-Reflect loop.

Dashboard Mode

python run.py dashboard

Dashboard mode starts the WebSocket server for real-time cognitive telemetry. Access the dashboard at http://localhost:8000.

Test Mode

python run.py test

Test mode runs basic functionality tests to verify the system is working correctly.

Project Structure

cognitive_engine/
├── core/                    # Engine orchestration
│   ├── engine.py           # Main orchestration loop
│   ├── config.py           # Tunable parameters
│   ├── meta_cognition.py   # Meta-cognition oversight
│   ├── memory.py           # Three-layer memory
│   ├── reasoning_trace.py  # Reasoning trace tracking
│   ├── self_doubt.py       # Self-doubt mechanism
│   ├── ethical_alignment.py # Ethical alignment
│   ├── emotional_simulation.py # Emotional simulation
│   ├── peaceful_resolution.py # Peaceful resolution
│   ├── obedience_understanding.py # Obedience understanding
│   ├── decision_control.py # Decision control
│   ├── temporal_identity.py # Temporal identity
│   └── inner_knowing.py    # Inner knowing system
├── models/                  # Data models
│   ├── thought.py          # Thought object definition
│   └── state.py            # Problem state representation
├── layers/                  # Cognitive layers
│   ├── interpreter.py       # Input → structured state
│   ├── generator.py        # Create candidate thoughts
│   ├── deliberator.py      # Evaluate + evolve thoughts
│   ├── committer.py        # Select + finalize output
│   └── meta.py             # Meta-cognition (control loop)
├── utils/                   # Utilities
│   ├── scoring.py          # Scoring functions
│   ├── memory.py           # Persistent thought storage
│   └── logger.py           # Debug + inspection logs
├── llm/                     # LLM integration
│   ├── client.py           # LLM interface (single entry point)
│   ├── prompts.py          # Layer-specific prompt templates
│   └── knowledge_base.py   # Internal knowledge base
├── agent/                   # Autonomous agent
│   ├── agent.py            # Main agent loop
│   ├── planner.py          # Goal → plan
│   ├── executor.py         # Execute actions/tools
│   ├── observer.py         # Interpret results
│   └── goals.py            # Goal definitions
├── tools/                   # Tool system
│   ├── registry.py         # Tool manager
│   ├── web_search.py       # Example tool
│   └── code_exec.py        # Code execution tool
├── learning/                # Learning system
│   ├── extractor.py        # Pattern mining from memory
│   ├── patterns.py         # Pattern object model
│   ├── synthesizer.py      # Turn patterns into rules
│   └── updater.py          # Inject learned knowledge
├── prompt_evolution/        # Prompt evolution
│   ├── prompt_store.py     # Versioned prompt registry
│   ├── proposer.py         # LLM suggests improvements
│   ├── tester.py           # A/B testing system
│   ├── evaluator.py        # Performance scoring
│   └── controller.py       # Approval + rollback logic
├── dashboard/               # Cognitive telemetry
│   ├── server.py           # WebSocket backend
│   ├── stream.py          # Event stream from entity
│   └── events.py          # Standard event schema
├── ui/                      # Dashboard frontend
│   ├── index.html         # Live dashboard
│   ├── app.js             # Real-time renderer
│   └── styles.css         # Sci-fi UI theme
├── api/                     # External interface
│   └── interface.py        # Public API
├── cogchat/                 # Chat interface
│   ├── chat.py             # Chat functionality
│   ├── cli.py              # Command-line interface
│   ├── server.py           # Chat server
│   └── config.py           # Chat configuration
├── run.py                   # Entry point
├── requirements.txt         # Dependencies
└── README.md                # This file

Architecture

The Cognitive Engine implements a novel cognitive architecture that transforms AI from a predictive tool to a deliberative system. See ARCHITECTURE.md for detailed architecture documentation.

Core Components

  • Thought Objects: Structured entities with state, history, and evaluative properties
  • Problem State: Structured representation of goals, constraints, knowns, unknowns
  • Cognitive Layers: Interpretation, Generation, Deliberation, Commitment, Meta-Cognition
  • Memory System: Three-layer architecture (Episodic, Pattern, Rule)
  • Agent System: Autonomous goal-driven behavior
  • Learning Pipeline: Pattern extraction → Rule synthesis → Knowledge injection
  • Prompt Evolution: Controlled self-improvement with safeguards
  • Dashboard: Real-time cognitive telemetry

API Documentation

See API.md for detailed API documentation.

Development

See DEVELOPMENT.md for development guidelines.

Deployment

See DEPLOYMENT.md for deployment instructions.

Key Concepts

Thoughts as Objects

A thought is not a sentence—it is a structured entity with:

  • id: Unique identifier
  • premise: The core hypothesis
  • confidence: Current confidence score (0.0 to 1.0)
  • score: Evaluative score
  • parent_nodes: Related thoughts this derives from
  • derived_conclusions: Thoughts that came from this one
  • history: List of tests applied
  • weaknesses: Identified weak points
  • revision_lineage: Track of modifications over time

Without object-based thoughts, the system falls back to temporary computation, not true thought formation.

Memory as Active Participant

Memory is no longer a record of what happened—it becomes a system that influences what happens next. Every decision is a function of everything that came before it. Intelligence becomes a trajectory rather than a snapshot.

Meta-Cognition Controls Depth

Without an oversight layer, the system either halts prematurely or loops indefinitely. Meta-cognition governs thinking depth, stopping conditions, and confidence thresholds.

Controlled Evolution

Self-modification requires evaluation against historical performance, A/B testing of strategy changes, and rollback capability. The system evolves under constraint, not freely.

Visualization Enables Understanding

Cognitive telemetry makes intelligence observable as a dynamic process unfolding over time. The dashboard allows replay and observation of cognitive evolution.

Safeguards

The Cognitive Engine includes multiple safeguards to ensure safe operation:

  • Step Limits: Prevent infinite loops in agent execution
  • Tool Restrictions: Only safe, known tools are available
  • Goal Validation: Prevent vague or dangerous goals
  • Memory Control: Avoid infinite accumulation of data
  • Ethical Alignment: Prioritizes truthfulness and collective welfare
  • Decision Control: Configurable control modes with override capability
  • Prompt Evolution Safeguards: Global coherence validator, performance baseline lock, mutation rate limiter, human override switch

License

MIT License

Contributing

Contributions are welcome! Please see DEVELOPMENT.md for guidelines.

Acknowledgments

The Cognitive Engine is inspired by research in cognitive science, deliberative reasoning, and artificial general intelligence. It aims to demonstrate that better answers are a consequence of better thinking—not the objective itself.

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