🧠 Neural & SONA

Feature	Description	Performance
SONA Learning	Self-Optimizing Pattern Learning	sub-millisecond pattern matching
5 Learning Modes	real-time, balanced, research, edge, batch	Mode-specific optimization
9 RL Algorithms	PPO, A2C, DQN, Q-Learning, SARSA, Decision Transformer, etc.	Comprehensive RL
LoRA Integration	Low-Rank Adaptation for efficient fine-tuning	Minimal memory overhead
MicroLoRA	Ultra-lightweight LoRA for edge/real-time modes	<5MB memory footprint
EWC++ Memory	Elastic Weight Consolidation prevents catastrophic forgetting	Zero knowledge loss
Trajectory Tracking	Execution path recording for pattern extraction	Continuous learning

Feature	Description	Improvement
Scalar Quantization	Reduce vector precision for memory savings	4x memory reduction
Product Quantization	Compress vectors into codebooks	8-32x memory reduction
HNSW Indexing	Hierarchical Navigable Small World graphs	HNSW-indexed search
LRU Caching	Intelligent embedding cache with TTL	<1ms cache hits
Batch Processing	Process multiple embeddings in single call	10x throughput
Memory Compression	Pattern distillation and pruning	50-75% reduction