Kwasa-Kwasa Operations Specification

Overview

Kwasa-Kwasa introduces a revolutionary approach to text processing by treating text as a mathematical medium that supports arithmetic operations while preserving semantic meaning. This document provides a complete specification of all operations available in the framework.

Mathematical Text Operations

Division Operations (`/`)

Division splits larger text units into smaller constituent parts while maintaining semantic boundaries.

Syntax

result = text_unit / boundary_type

Supported Divisions

Source Unit	Boundary Type	Result	Description
`document`	`section`	`Array<Section>`	Split document into logical sections
`section`	`paragraph`	`Array<Paragraph>`	Split section into paragraphs
`paragraph`	`sentence`	`Array<Sentence>`	Split paragraph into sentences
`sentence`	`phrase`	`Array<Phrase>`	Split sentence into syntactic phrases
`sentence`	`word`	`Array<Word>`	Split sentence into words
`word`	`character`	`Array<Character>`	Split word into characters
`text`	`line`	`Array<Line>`	Split by line breaks

Examples

// Split a paragraph into sentences
item sentences = paragraph / sentence

// Split text by custom boundaries
item sections = document / section
item words = sentence / word

// Chain divisions
item all_words = document / paragraph / sentence / word

Semantic Preservation

Division respects linguistic boundaries (e.g., sentence breaks, phrase boundaries)
Maintains metadata about original context and position
Preserves semantic relationships between divided units

Multiplication Operations (`*`)

Multiplication combines text units intelligently, inserting appropriate connectors and maintaining coherence.

Syntax

result = unit1 * unit2 * ... * unitN

Combination Rules

Unit Type	Connector Strategy	Example Output
`Sentence`	Period + space	“First sentence. Second sentence.”
`Paragraph`	Double newline	“Para 1\n\nPara 2”
`Word`	Space	“word1 word2 word3”
`Section`	Section break	Appropriate section separators

Examples

// Combine sentences into a paragraph
item paragraph = sentence1 * sentence2 * sentence3

// Smart combination with context awareness
item coherent_text = intro * main_body * conclusion

// Conditional combination
given coherence_score(sentence1, sentence2) > 0.7:
    item combined = sentence1 * sentence2

Addition Operations (`+`)

Addition performs semantic concatenation, extending text while maintaining type consistency.

Syntax

result = base_text + additional_content

Type Preservation

Adding to a Sentence maintains sentence structure
Adding to a Paragraph extends the paragraph
Smart punctuation and spacing insertion

Examples

// Extend a sentence
item extended = original_sentence + " with additional context"

// Add explanatory content
item explained = technical_term + " (which refers to " + explanation + ")"

// Conditional addition
given needs_clarification(sentence):
    item clarified = sentence + clarifying_phrase

Subtraction Operations (`-`)

Subtraction removes content while maintaining structural integrity and readability.

Syntax

result = original_text - content_to_remove

Removal Strategies

Exact Match: Remove exact text matches
Semantic Match: Remove semantically equivalent content
Pattern Match: Remove content matching patterns
Structural Preservation: Maintain grammatical correctness

Examples

// Remove specific phrases
item cleaned = text - "unnecessary filler"

// Remove by pattern
item formal = text - informal_expressions

// Conditional removal
given verbosity_score(text) > 0.8:
    item concise = text - redundant_phrases

Specialized Functions

Text Analysis Functions

Readability Analysis

readability_score(text: TextUnit) -> f64
    // Returns Flesch-Kincaid readability score (0-100)
    // Higher scores indicate easier readability

complexity_score(text: TextUnit) -> f64
    // Returns syntactic complexity measure (0-1)
    
sentence_variety_score(text: TextUnit) -> f64
    // Measures sentence length and structure variety

Semantic Analysis

sentiment_analysis(text: TextUnit) -> Sentiment
    // Returns: { polarity: f64, subjectivity: f64, confidence: f64 }

topic_coherence(text: TextUnit) -> f64
    // Measures thematic consistency throughout text
    
semantic_density(text: TextUnit) -> f64
    // Measures information density per unit of text

Linguistic Features

extract_keywords(text: TextUnit, count: i32 = 10) -> Array<Keyword>
    // Extracts statistically significant keywords

named_entities(text: TextUnit) -> Array<Entity>
    // Identifies people, places, organizations, etc.

grammatical_analysis(text: TextUnit) -> GrammarProfile
    // Returns detailed grammatical structure analysis

Text Transformation Functions

Stylistic Transformations

formalize(text: TextUnit, level: String = "moderate") -> TextUnit
    // Increases formality: "casual" | "moderate" | "formal" | "academic"

simplify_sentences(text: TextUnit, target_grade: i32 = 8) -> TextUnit
    // Simplifies sentence structure for target reading level

adjust_tone(text: TextUnit, target_tone: String) -> TextUnit
    // Adjusts tone: "professional" | "casual" | "academic" | "persuasive"

Content Enhancement

replace_jargon(text: TextUnit, domain: String = "general") -> TextUnit
    // Replaces technical terms with accessible alternatives

add_transitions(text: TextUnit) -> TextUnit
    // Inserts appropriate transitional phrases

ensure_explanation_follows(term: String, text: TextUnit) -> TextUnit
    // Ensures technical terms are followed by explanations

Research Integration

research_context(topic: String, depth: String = "medium") -> ResearchData
    // Retrieves contextual information: "shallow" | "medium" | "deep"

fact_check(statement: String) -> FactCheckResult
    // Verifies factual claims: { verified: bool, confidence: f64, sources: Array<Source> }

citation_suggestions(text: TextUnit, style: String = "APA") -> Array<Citation>
    // Suggests citations for claims requiring support

Statistical Analysis Functions

Probability and Distribution

ngram_probability(text: TextUnit, sequence: String, n: i32 = 3) -> f64
    // Returns probability of n-gram sequence in given context

conditional_probability(text: TextUnit, sequence: String, condition: String) -> f64
    // P(sequence | condition) in the text

positional_distribution(text: TextUnit, pattern: String) -> Array<Position>
    // Maps where patterns occur across text structure

Information Theory

entropy_measure(text: TextUnit, window_size: i32 = 50) -> f64
    // Calculates information entropy within sliding windows

mutual_information(text1: TextUnit, text2: TextUnit) -> f64
    // Measures information shared between text units

compression_ratio(text: TextUnit) -> f64
    // Estimates information density through compression

Statistical Significance

sequence_significance(text: TextUnit, sequence: String) -> SignificanceTest
    // Tests if sequence frequency is statistically significant

zipf_analysis(text: TextUnit) -> ZipfProfile
    // Analyzes token frequency distribution against Zipf's law

markov_transition(text: TextUnit, order: i32 = 1) -> TransitionMatrix
    // Generates transition probabilities for Markov analysis

Advanced Operations

Metacognitive Operations

goal_alignment(text: TextUnit, goal: Goal) -> f64
    // Measures how well text aligns with stated goals

cognitive_load(text: TextUnit) -> f64
    // Estimates cognitive processing burden

attention_distribution(text: TextUnit) -> AttentionMap
    // Predicts where readers will focus attention

Cross-Domain Operations

motif_enrichment(sequence: String, motif: String) -> f64
    // For genomic sequences: statistical motif enrichment

spectral_correlation(spectrum1: Spectrum, spectrum2: Spectrum) -> f64
    // For mass spectrometry: correlation between spectra

chemical_similarity(molecule1: String, molecule2: String) -> f64
    // For chemistry: structural similarity between molecules

Operation Composition

Chaining Operations

// Operations can be chained for complex transformations
item result = text 
    |> simplify_sentences()
    |> replace_jargon("academic")
    |> add_transitions()
    |> formalize("moderate")

Conditional Operations

// Operations can be applied conditionally
given readability_score(text) < 60:
    text = simplify_sentences(text)

given contains_technical_terms(text):
    text = ensure_explanations(text)

Parallel Operations

// Multiple operations can run in parallel
item analysis = parallel {
    sentiment_analysis(text),
    readability_score(text),
    extract_keywords(text, 15),
    fact_check_claims(text)
}

Error Handling and Validation

Operation Validation

// Operations include built-in validation
try {
    item sentences = malformed_text / sentence
} catch DivisionError(reason) {
    print("Cannot divide: {}", reason)
    // Handle gracefully
}

Type Safety

// Operations are type-aware
item paragraph = Paragraph("Some text")
item sentences = paragraph / sentence  // ✓ Valid
item invalid = paragraph / spectrum    // ✗ Compile-time error

Semantic Validation

// Operations validate semantic coherence
item result = sentence1 * sentence2
if coherence_score(result) < threshold:
    suggest_connecting_phrase(sentence1, sentence2)

Performance Considerations

Lazy Evaluation

Operations are lazy by default
Evaluation occurs only when results are accessed
Enables efficient operation chaining

Caching

Frequently used analysis results are cached
Cache invalidation on text modification
Configurable cache policies

Streaming Operations

Large texts processed in streams
Memory-efficient for document-level operations
Progress tracking for long operations

Integration with Propositions and Motions

Proposition-Aware Operations

proposition TextQuality:
    motion Clarity("Text should be clear")
    motion Conciseness("Text should be concise")
    
    // Operations can test against motions
    within text:
        given readability_score() > 70:
            support Clarity
        given word_count() / idea_count() < 15:
            support Conciseness

Motion-Specific Operations

motion claim = Motion("AI will transform education", "claim")

// Operations specific to motion types
item spelling_issues = claim.spelling()
item factual_support = claim.find_supporting_evidence()
item logical_coherence = claim.check_logical_consistency()

This comprehensive operations system enables sophisticated text manipulation while maintaining semantic meaning and linguistic correctness.

Kwasa-Kwasa Operations Specification

Overview

Mathematical Text Operations

Division Operations (/)

Syntax

Supported Divisions

Examples

Semantic Preservation

Multiplication Operations (*)

Syntax

Combination Rules

Examples

Addition Operations (+)

Syntax

Type Preservation

Examples

Subtraction Operations (-)

Syntax

Removal Strategies

Examples

Specialized Functions

Text Analysis Functions

Readability Analysis

Semantic Analysis

Linguistic Features

Text Transformation Functions

Stylistic Transformations

Content Enhancement

Research Integration

Statistical Analysis Functions

Probability and Distribution

Information Theory

Statistical Significance

Advanced Operations

Metacognitive Operations

Cross-Domain Operations

Operation Composition

Chaining Operations

Conditional Operations

Parallel Operations

Error Handling and Validation

Operation Validation

Type Safety

Semantic Validation

Performance Considerations

Lazy Evaluation

Caching

Streaming Operations

Integration with Propositions and Motions

Proposition-Aware Operations

Motion-Specific Operations

Division Operations (`/`)

Multiplication Operations (`*`)

Addition Operations (`+`)

Subtraction Operations (`-`)