A complete 8-layer implementation of PrimeLM - a self-referential, standards-based language model that uses mathematical prime factorization as a universal translation layer between neural networks, conversation memory, and semantic understanding.
This is a complete functional implementation of PrimeLM's core architecture. All 8 layers are implemented and working together to create conversational AI through mathematical prime operations rather than traditional transformer architectures.
Key Capabilities:
- ✅ Name & Attribute Queries: "What is my name?" → "Your name is Alex"
- ✅ Entity Relationships: "My dog's name is Max" → Remembers and recalls relationships
- ✅ Episodic Memory: Builds personality profiles and learns from conversation history
- ✅ Emotional Intelligence: Detects emotions and adapts response style
- ✅ Mathematical Coherence: Uses prime factorization for consistent reasoning
- ✅ Standards Integration: Schema.org vocabulary and semantic understanding
PrimeLM demonstrates how mathematical prime factorization can serve as a universal translation layer for conversational AI:
Neural Embeddings → Prime Factorization → Mathematical Coherence → Natural Language
Instead of relying solely on large language models, PrimeLM uses:
- Prime factorization of neural embeddings for mathematical reasoning
- Resonance patterns between prime factors for coherence
- Standards-based vocabulary (Schema.org) for semantic understanding
- Memory consolidation through mathematical operations
- Self-referential analysis of conversation patterns
┌─────────────────────────────────────────────────────────┐
│ Conversational Layers │
├─────────────────────────────────────────────────────────┤
│ 8. Emotional Intelligence Layer ❤️ ✅ IMPLEMENTED │
│ 7. Episodic Memory Layer 🧠 ✅ IMPLEMENTED │
│ 6. Generative Layer 🎨 ✅ IMPLEMENTED │
│ 5. Discourse Layer 💬 ✅ IMPLEMENTED │
│ 4. Pragmatic Layer 🎯 ✅ IMPLEMENTED │
│ 3. Semantic Layer 🧠 ✅ IMPLEMENTED │
├─────────────────────────────────────────────────────────┤
│ Mathematical Foundation │
├─────────────────────────────────────────────────────────┤
│ 2. Prime Resonance Layer 🔢 ✅ IMPLEMENTED │
│ 1. Prime Core Layer ⚡ ✅ IMPLEMENTED │
└─────────────────────────────────────────────────────────┘
Mathematical Foundation:
- Prime Core: Neural embedding → prime factorization conversion
- Prime Resonance: Harmonic analysis and mathematical coherence between prime patterns
Semantic Understanding:
- Semantic Layer: Schema.org vocabulary, entity-relationship understanding, intent recognition
- Pragmatic Layer: Conversation context, entity memory, relationship tracking
Conversation Management:
- Discourse Layer: Topic management, conversation flow, reference resolution
- Generative Layer: Dynamic response generation, personality-aware language production
Advanced Intelligence:
- Episodic Memory: Long-term memory consolidation, personality profiling, learning patterns
- Emotional Intelligence: Emotion detection, empathy modeling, social awareness
src/lib/
├── config.ts # System configuration and adaptive thresholds
├── primelm-models.ts # Main PrimeLM core and orchestration
├── prime-math.ts # Prime number utilities and factorization
├── prime-resonance.ts # Mathematical resonance and harmonic analysis
├── semantic-layer.ts # Natural language understanding and Schema.org
├── pragmatic-layer.ts # Conversation context and entity memory
├── discourse-layer.ts # Topic management and conversation flow
├── generative-layer.ts # Dynamic response generation
├── episodic-memory.ts # Long-term memory and personality profiling
├── emotional-intelligence.ts # Emotion detection and empathy
├── schema-vocabulary.ts # Schema.org vocabulary integration
├── knowledge-bootstrap.ts # Tokenizer vocabulary extraction
├── entity-memory.ts # Semantic entity relationship storage
├── conversation-state.ts # Unified conversation state management
└── error-handling.ts # Graceful error handling and fallbacks
src/app/
└── page.tsx # Chat interface with mathematical debug display
// Neural embeddings converted to prime factors
const primes = embeddingsToPrimes(embeddings);
// Mathematical coherence between conversation turns
const coherence = calculateCoherence(inputPrimes, contextPrimes);
// Response generation through prime operations
const response = generateFromPrimes(resonantPrimes);- Schema.org: Entity types, properties, and relationships
- Transformers.js: Real neural network embeddings
- JSON-LD: Structured data representation
- Mathematical Standards: Prime factorization, harmonic analysis
User: "My dog's name is Max"
PrimeLM: "It's lovely to know that your dog is named Max! What kind of dog is Max?"
User: "What is my dog's name?"
PrimeLM: "Your dog is named Max! I remember you telling me that."
User: "I have a white truck"
PrimeLM: "I understand. Tell me more about your truck."
User: "What color is my truck?"
PrimeLM: "Your truck is white! I remember you telling me that."
User: "My name is Alex"
PrimeLM: "Nice to meet you, Alex! I'm here to help."
User: "What is my name?"
PrimeLM: "Your name is Alex."
npm install@xenova/transformers- Real neural network embeddingsnext- React framework for UItypescript- Type-safe implementation
npm run devOpen http://localhost:3000 for the interactive chat demo.
npm run build- Traditional: Text → Transformer → Text
- PrimeLM: Text → Embeddings → Primes → Mathematical Operations → Text
- Mathematical Traceability: Every response has mathematical justification
- Memory Coherence: Prime factorization ensures consistent memory
- Standards-Based: Uses established vocabularies (Schema.org) not proprietary training
- Self-Referential: Can analyze its own mathematical reasoning patterns
- Efficient: Small models + mathematics vs. massive parameter counts
- Conversation Memory: Remembers names, relationships, attributes across turns
- Entity Recognition: Understands people, animals, objects, and their properties
- Query Resolution: Answers questions about stored information
- Emotional Awareness: Adapts responses based on emotional context
- Mathematical Coherence: Maintains consistency through prime factorization
- Standards Compliance: Uses Schema.org vocabulary for semantic understanding
- Expanded Schema.org: Full vocabulary integration
- RDF/JSON-LD: Complete semantic web compliance
- Temporal Models: Time-based conversation analysis
- Self-Reference: Mathematical introspection capabilities
interface PrimeFactorization {
primes: Record<number, number>; // prime → weight mapping
magnitude: number; // mathematical magnitude
coherence: number; // coherence with context
}
interface ConversationState {
entityMemory: Map<string, EntityInfo>;
episodicMemory: EpisodicMemory[];
emotionalState: EmotionalContext;
primeContext: Record<number, number>;
}- Schema.org Types: Person, Organization, Thing, Animal, Vehicle
- Relationship Properties: hasName, hasProperty, relatedTo
- Semantic Enhancement: Entity type inference and validation
npm testnpm run type-checknpm run lintMIT License - see LICENSE file for details.
- PrimeLM Concept: See
./PrimeLM.mdfor the complete theoretical framework - Mathematical Foundation: Prime factorization as universal translation layer
- Standards Integration: Schema.org, RDF, and semantic web compliance
- Self-Referential AI: Mathematical consciousness through spectral analysis