Metacognitive Orchestration in Kwasa-Kwasa

Overview

The Metacognitive Orchestrator is a core component of Kwasa-Kwasa that provides intelligent guidance and control over text processing operations. It maintains awareness of document state, processing goals, and context to ensure optimal results.

Core Features

1. Goal Representation

The orchestrator can represent and track complex writing and processing goals:

// Define a writing goal
item goal = Goal.new("Write a technical tutorial for beginners")
    .with_target_audience("beginners")
    .with_technical_depth("moderate")
    .with_keywords(["tutorial", "beginner", "step-by-step"])
    .with_completion_criteria(function(text) {
        return readability_score(text) > 70 &&
               contains_all_keywords(text) &&
               has_clear_structure(text)
    })

// Track progress
goal.update_progress(0.3)  // 30% complete
print("Goal completion: {}%".format(goal.progress() * 100))

2. Context Awareness

The orchestrator maintains awareness of the current processing context:

// Get current context
item context = orchestrator.current_context()
print("Domain: {}".format(context.domain))
print("Technical level: {}".format(context.technical_level))

// Update context based on content
within text:
    given contains_technical_terms():
        context.increase_technical_level()
    given readability_score() < 50:
        context.mark_as_complex()

3. Intelligent Intervention

The orchestrator can provide suggestions and interventions:

// Set up intervention handlers
orchestrator.on_complex_sentence(function(sentence) {
    return simplify_sentences(sentence, "moderate")
})

orchestrator.on_technical_term(function(term) {
    ensure_explanation_follows(term)
})

// Process text with interventions
item processed = orchestrator.process_with_interventions(text)

4. Progress Evaluation

Continuous evaluation of progress towards goals:

// Evaluate alignment with goals
item alignment = orchestrator.evaluate_alignment(text)
if alignment < 0.3:
    item suggestions = orchestrator.suggest_improvements()
    for each suggestion in suggestions:
        print("Suggestion: {}".format(suggestion))

Processing Architecture

1. Three-Layer Processing Model

The orchestrator implements a streaming-based concurrent processing model:

1. Context Layer

   // Set up context layer
   item context_layer = orchestrator.context_layer()
    .with_domain("scientific")
    .with_audience("academic")
    .with_style_guide("APA")
   

2. Reasoning Layer

   // Configure reasoning layer
   item reasoning_layer = orchestrator.reasoning_layer()
    .with_logic_rules([
        "if contains_technical_term then needs_explanation",
        "if readability_low then needs_simplification"
    ])
   

3. Intuition Layer

   // Set up intuition layer
   item intuition_layer = orchestrator.intuition_layer()
    .with_pattern_recognition()
    .with_heuristic_analysis()
   

2. Streaming Processing

// Set up streaming pipeline
item pipeline = orchestrator.create_pipeline()
    .add_stage(context_analysis)
    .add_stage(content_processing)
    .add_stage(quality_validation)
    .with_continuous_feedback()

// Process stream
pipeline.process_stream(text_stream)

Knowledge Integration

1. Research Integration

// Configure research capabilities
orchestrator.configure_research()
    .with_sources(["academic_papers", "technical_docs"])
    .with_citation_style("IEEE")
    .with_fact_checking(true)

// Perform research
item research = orchestrator.research_context("quantum computing", {
    depth: "medium",
    focus: ["basic principles", "applications"]
})

2. Knowledge Database

// Access knowledge database
item db = orchestrator.knowledge_db()

// Store new knowledge
db.store_fact({
    domain: "physics",
    concept: "quantum entanglement",
    definition: "A quantum phenomenon where particles remain connected...",
    confidence: 0.95
})

// Query knowledge
item results = db.query({
    domain: "physics",
    confidence_threshold: 0.8
})

Best Practices

1. Goal Definition

// Define clear, measurable goals
item goal = Goal.new("Explain quantum computing")
    .with_metrics([
        Metric.readability(min_score: 70),
        Metric.technical_accuracy(min_score: 0.9),
        Metric.explanation_completeness(required_concepts: [
            "superposition",
            "entanglement",
            "quantum bits"
        ])
    ])

2. Context Management

// Maintain consistent context
within document:
    ensure_consistent_technical_level()
    maintain_narrative_flow()
    track_defined_terms()

3. Intervention Configuration

// Configure targeted interventions
orchestrator.configure_interventions({
    readability: {
        threshold: 65,
        action: simplify_sentences
    },
    technical_terms: {
        max_density: 0.1,
        action: ensure_explanation_follows
    },
    coherence: {
        min_score: 0.8,
        action: improve_transitions
    }
})

Performance Optimization

1. Streaming Processing
   • Process text in chunks
   • Enable parallel processing where possible
   • Use incremental updates
2. Memory Management
   • Cache frequently accessed knowledge
   • Implement efficient context switching
   • Use memory-efficient data structures
3. Resource Allocation
   • Prioritize critical interventions
   • Balance processing depth vs. speed
   • Implement adaptive resource allocation