# OptiRS Learned TODO (v0.3.0)
## Module Status: Production Ready
**Release Date**: 2026-03-17
**Tests**: 143 tests passing (2 ignored)
**Features**: LSTM optimizers, Transformer optimizers, Meta-learning
**SciRS2 Compliance**: 100%
---
## Completed: SciRS2 Integration
- [x] **Full SciRS2-Core Integration** - 100% complete
- [x] **ML Pipeline Foundation** - Built on scirs2_core::ml_pipeline
- [x] **Neural Architecture Search** - Using scirs2_core::neural_architecture_search
- [x] **Memory Efficient Operations** - scirs2_core::memory_efficient::LazyArray for history
- [x] **JIT Compilation** - scirs2_core::jit for optimized transformer kernels
- [x] **Array Operations** - All neural operations use scirs2_core::ndarray
- [x] **Random Generation** - scirs2_core::random for all stochastic operations
---
## Completed: Core Learned Optimizer Infrastructure
### Neural Optimizer Framework
- [x] Generic neural optimizer trait and interface
- [x] Parameter update rule neural networks
- [x] Gradient preprocessing and feature extraction
- [x] State management for recurrent optimizers
- [x] Memory systems for optimization history
- [x] Multi-step prediction and rollout
### Transformer-Based Optimizers
- [x] Multi-head attention for parameter importance
- [x] Positional encoding for parameter sequences
- [x] Layer normalization and residual connections
- [x] Causal masking for autoregressive optimization
- [x] Attention pattern analysis
- [x] Memory-efficient attention implementation
### LSTM Optimizers
- [x] Vanilla LSTM for parameter update rules
- [x] GRU alternative for computational efficiency
- [x] Bidirectional variants for global context
- [x] Forget gate analysis and tuning
- [x] Hidden state initialization strategies
- [x] Gradient clipping for stability
### Meta-Learning Framework
- [x] MAML implementation (inner/outer loop)
- [x] Second-order gradient computation
- [x] Task sampling and distribution management
- [x] Evaluation on held-out tasks
- [x] Meta-validation and early stopping
---
## Completed: Advanced Features
### Hyperparameter Learning
- [x] Learning rate prediction networks
- [x] Adaptive scheduling based on loss landscape
- [x] Multi-parameter learning rate optimization
- [x] Warmup and cooldown strategy learning
### Training Infrastructure
- [x] Distributed meta-training foundation
- [x] Efficient task sampling and batching
- [x] Gradient accumulation for large meta-batches
- [x] Mixed precision training support
- [x] Checkpointing and resumption
### Evaluation Framework
- [x] Convergence speed metrics
- [x] Final performance comparison
- [x] Stability and robustness analysis
- [x] Generalization to unseen tasks
- [x] Computational efficiency measurement
---
## Future Work (v0.2.0+)
### Domain-Specific Optimization
- [x] Computer vision specific optimizers (CVOptimizer)
- [x] NLP-specific token-aware updates (NLPOptimizer)
- [x] Attention pattern optimization (AttentionOptimizer)
### Online Learning and Adaptation
- [x] Continual learning (EWC, Progressive Networks)
- [x] Online MAML for continuous task streams (staleness decay, buffer management, adaptation efficiency)
- [ ] Real-time adaptation mechanisms
### Advanced Architectures
- [ ] Graph Neural Network optimizers
- [ ] Memory-augmented optimizers (NTM)
- [x] Episodic memory systems (EpisodicMemoryBank, SupportSetManager)
### Multi-Task and Transfer
- [x] Cross-domain knowledge transfer (domain registration, similarity, transferability matrix)
- [x] Shared representation learning (shared representation updates)
- [ ] Zero-shot optimization
- [x] Few-shot adaptation strategies (PrototypicalNetwork, FastAdaptationEngine, TaskSimilarityCalculator)
### Research Features
- [ ] NAS for optimizer architectures (DARTS)
- [ ] Quantum-inspired optimizers
- [ ] Variational quantum optimizer
---
## Testing Status
### Coverage
- [x] Neural optimizer architecture tests
- [x] Meta-learning algorithm tests
- [x] Memory system tests
- [x] Attention mechanism tests
- [x] State management tests
### Test Count
```
143 tests passing
2 intentionally ignored (hardware-specific)
```
---
## Performance Achievements
- Learned optimizer framework operational
- Meta-learning pipeline complete
- Transformer and LSTM optimizers working
- Production-ready evaluation metrics
- Wave 2: Few-shot learning, episodic memory, online MAML, cross-domain transfer
---
**Status**: ✅ Production Ready
**Version**: v0.3.0
**Release Date**: 2026-03-17