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Phase 5: Advanced Features Specification

Overview

Phase 5 implements advanced learning features to complete the self-evolution system. This is the final phase and focuses on cutting-edge AI capabilities.

Components

1. Multi-Task Learning

  • Purpose: Learn multiple tasks simultaneously with shared representations
  • Features:
    • Task-specific and shared layers
    • Multi-task optimization
    • Task weighting and balancing
    • Multi-task transfer learning
  • Classes:
    • MultiTaskLearner - Main multi-task learning orchestrator
    • SharedLayer - Shared representation layers
    • TaskSpecificLayer - Task-specific layers
    • MultiTaskOptimizer - Optimizes all tasks simultaneously
    • MultiTaskResult - Multi-task learning results

2. Continual Learning

  • Purpose: Learn sequentially without forgetting previous tasks
  • Features:
    • Catastrophic forgetting prevention
    • Elastic weight consolidation
    • Memory replay strategies
    • Knowledge distillation
  • Classes:
    • ContinualLearner - Main continual learning engine
    • ElasticWeightConsolidation - EWC algorithm
    • MemoryReplay - Experience replay buffer
    • KnowledgeDistillation - Knowledge transfer
    • ContinualResult - Continual learning metrics

3. Self-Supervised Learning

  • Purpose: Learn from unlabeled data using self-generated labels
  • Features:
    • Contrastive learning (SimCLR, MoCo)
    • Masked language modeling (BERT-style)
    • Autoencoder-based learning
    • Self-supervised pretraining
  • Classes:
    • SelfSupervisedLearner - Main SSL orchestrator
    • ContrastiveLearner - Contrastive learning
    • MaskedModelingLearner - Masked modeling
    • AutoencoderLearner - Autoencoder learning
    • SSLResult - SSL learning results

4. Neural Architecture Evolution

  • Purpose: Evolve neural architectures using evolutionary algorithms
  • Features:
    • Architecture representation (graph-based)
    • Evolutionary operations (mutation, crossover, selection)
    • Fitness evaluation
    • Architecture generation and refinement
  • Classes:
    • NeuralArchitectureEvolver - Main evolution engine
    • ArchitectureGenotype - Architecture representation
    • EvolutionaryOperator - Mutation and crossover
    • FitnessEvaluator - Architecture fitness
    • EvolutionResult - Evolution outcomes

5. Distributed Training

  • Purpose: Train models across multiple devices/nodes
  • Features:
    • Data parallelism
    • Model parallelism
    • Gradient synchronization
    • Fault tolerance and recovery
  • Classes:
    • DistributedTrainer - Main distributed orchestrator
    • DataParallel - Data parallelism
    • ModelParallel - Model parallelism
    • GradientSynchronizer - Gradient sync
    • DistributedResult - Training results

Requirements

Functional Requirements

  1. Multi-Task Learning

    • Support at least 3 tasks simultaneously
    • Optimize all tasks jointly
    • Balance task weights dynamically
    • Enable task transfer

  2. Continual Learning

    • Learn at least 5 tasks sequentially
    • Prevent catastrophic forgetting
    • Maintain accuracy on previous tasks
    • Support replay and distillation

  3. Self-Supervised Learning

    • Support at least 2 SSL methods
    • Generate self-supervised tasks
    • Pretrain on unlabeled data
    • Transfer to downstream tasks

  4. Neural Architecture Evolution

    • Evolve architectures for at least 3 generations
    • Support genetic operations
    • Evaluate fitness accurately
    • Generate novel architectures

  5. Distributed Training

    • Support data parallelism
    • Support model parallelism
    • Synchronize gradients efficiently
    • Handle node failures gracefully

Non-Functional Requirements

  1. Performance: Efficient multi-task and distributed training
  2. Scalability: Support many tasks and devices
  3. Reliability: Robust to failures and forgetting
  4. Extensibility: Easy to add new tasks and architectures
  5. Testability: Comprehensive test coverage (100%)

Implementation Order

Week 1

  1. Multi-Task Learning
    • Multi-task architecture
    • Shared and task-specific layers
    • Multi-task optimization
    • Task balancing

Week 2

  1. Continual Learning
    • EWC implementation
    • Memory replay
    • Knowledge distillation
    • Forgetting prevention

Week 3

  1. Self-Supervised Learning
    • Contrastive learning
    • Masked modeling
    • Autoencoder learning
    • SSL pretraining

Week 4

  1. Neural Architecture Evolution
    • Architecture representation
    • Evolutionary operations
    • Fitness evaluation
    • Architecture generation

Week 5

  1. Distributed Training
    • Data parallelism
    • Model parallelism
    • Gradient synchronization
    • Fault tolerance

Week 6

  1. Integration and Testing
    • End-to-end integration
    • Comprehensive testing
    • Documentation
    • Final deployment

Success Criteria

  • All 5 components implemented and tested
  • 100% test coverage (minimum 30 tests total)
  • Integration tests pass
  • Performance benchmarks met
  • Documentation complete
  • Ready for production deployment

Deliverables

  1. Multi-Task Learning (advanced/multi_task_learning.py)
  2. Continual Learning (advanced/continual_learning.py)
  3. Self-Supervised Learning (advanced/self_supervised_learning.py)
  4. Neural Architecture Evolution (advanced/neural_architecture_evolution.py)
  5. Distributed Training (advanced/distributed_training.py)
  6. Tests (5 test files)
  7. Documentation (PHASE5_COMPLETE.md)

Phase 5 Start Date: 2026-03-08 Estimated Duration: 6 weeks Priority: HIGH Status: READY TO START