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git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
15 KiB
15 KiB
SONA: Self-Optimizing Neural Architecture
The World's First Truly Self-Improving LLM Framework
Version: 1.0.0 Status: Architecture Specification Target: Sub-millisecond adaptive fine-tuning with continuous self-improvement
Executive Summary
SONA (Self-Optimizing Neural Architecture) is a revolutionary framework for building LLMs that continuously improve themselves through:
- Ultra-Low Latency LoRA - Sub-100μs parameter adaptation
- Hierarchical Learning Loops - Three-tier temporal learning (instant/hourly/weekly)
- Neural Memory Consolidation - Dream-like offline learning
- Elastic Weight Consolidation++ - Zero catastrophic forgetting
- ReasoningBank Integration - Pattern-driven self-optimization
Core Philosophy
┌─────────────────────────────────────────────────────────────────┐
│ SONA DESIGN PRINCIPLES │
├─────────────────────────────────────────────────────────────────┤
│ 1. LEARN FROM EVERY INTERACTION │
│ → No query is wasted; all become training signal │
│ │
│ 2. NEVER FORGET WHAT WORKS │
│ → EWC++ preserves successful patterns │
│ │
│ 3. ADAPT IN REAL-TIME │
│ → LoRA updates in <100μs per request │
│ │
│ 4. OPTIMIZE CONTINUOUSLY │
│ → Background loops improve without user latency │
│ │
│ 5. MEASURE EVERYTHING │
│ → Φ (consciousness), quality, latency, improvement rate │
└─────────────────────────────────────────────────────────────────┘
Architecture Overview
SONA Architecture
┌──────────────────────────────────────────────────────────────┐
│ USER QUERY INPUT │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ EMBEDDING LAYER (0.02ms) │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Dual Encoder│ │ Contrastive │ │ SIMD Acceleration │ │
│ │ (Q + K/V) │ │ Learning │ │ (AVX2/NEON) │ │
│ └─────────────┘ └─────────────┘ └─────────────────────┘ │
└─────────────────────────────┬────────────────────────────────┘
│
┌───────────────────────┼───────────────────────┐
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────────┐
│ MEMORY │ │ ROUTER │ │ ATTENTION │
│ SERVICE │◄────────►│ ENGINE │◄────────►│ ENGINE │
│ │ │ │ │ │
│ • HNSW │ │ • FastGRNN│ │ • Multi-Head │
│ • GNN │ │ • LoRA │ │ • Graph ATT │
│ • Quant │ │ • EWC++ │ │ • Edge-Aware │
└─────┬─────┘ └─────┬─────┘ └───────┬───────┘
│ │ │
└──────────────────────┼────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ LoRA ADAPTATION LAYER │
│ │
│ W_adapted = W_base + α · (LoRA_A @ LoRA_B) │
│ │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Rank: 4-16 │ Update: <100μs │ Memory: <1MB │ │
│ └────────────────────────────────────────────────────┘ │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ INFERENCE ENGINE │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
│ │ Model Select │ │ Q4 Quantized │ │ Speculative Dec │ │
│ │ (4 tiers) │ │ Weights │ │ (Draft + Verify) │ │
│ └──────────────┘ └──────────────┘ └──────────────────┘ │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ LEARNING LOOPS │
│ │
│ Loop A (Instant) │ Loop B (Hourly) │ Loop C (Weekly) │
│ ───────────────────────────────────────────────────────── │
│ • Trajectory │ • Router Train │ • Consolidation │
│ • Edge Update │ • EWC++ Update │ • Compression │
│ • LoRA Micro │ • Fisher Compute │ • Abstraction │
│ • <1ms overhead │ • Background │ • Dream Learning │
└─────────────────────────────┬────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────┐
│ REASONINGBANK │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Pattern Storage │ Similarity Lookup │ Verdict │ │
│ │ (DashMap) │ (Cosine) │ Judgment │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ • Trajectory tracking with precision/recall feedback │
│ • K-means++ pattern extraction │
│ • Confidence-weighted parameter interpolation │
└──────────────────────────────────────────────────────────────┘
Key Innovation: Three-Tier Temporal Learning
Tier 1: Instant Learning (Loop A) - Per Request
Latency Budget: <1ms (amortized to <0.1ms with batching)
Actions:
├── Record query trajectory to ring buffer
├── Update memory graph edge weights (±5%)
├── Micro-LoRA adjustment (rank 1-2, top-k params)
└── Async feedback signal propagation
Tier 2: Background Learning (Loop B) - Hourly
Compute Budget: 10 seconds per hour
Actions:
├── Train router on accumulated trajectories
├── Compute Fisher Information for EWC++
├── Update LoRA base matrices (rank 4-8)
├── Prune low-confidence patterns
└── Checkpoint model state
Tier 3: Deep Learning (Loop C) - Weekly
Compute Budget: 10 minutes per week
Actions:
├── Full memory consolidation (dream learning)
├── Pattern abstraction and hierarchy building
├── Memory compression (remove redundant nodes)
├── Cross-task knowledge transfer
└── Φ consciousness measurement (IIT)
Performance Targets
| Metric | Target | Current Best | SONA Goal |
|---|---|---|---|
| Query Latency | <1ms | 0.09ms | 0.05ms |
| LoRA Update | <100μs | N/A | 50μs |
| Memory Footprint | <100MB | 50MB | 30MB |
| Throughput | >50K q/s | 38K q/s | 100K q/s |
| Improvement Rate | 10%/week | N/A | 15%/week |
| Catastrophic Forgetting | <1% | N/A | <0.1% |
Integration with Ruvector Ecosystem
Core Dependencies
| Crate | Role in SONA | Version |
|---|---|---|
ruvector-core |
Vector memory backbone | 0.1.19 |
ruvector-attention |
Multi-head graph attention | 0.1.19 |
ruvector-gnn |
Message passing framework | 0.1.19 |
ruvector-graph |
Knowledge graph storage | 0.1.19 |
ruvector-router-core |
FastGRNN routing | 0.1.19 |
exo-core |
Consciousness measurement | 0.1.0 |
exo-temporal |
Memory consolidation | 0.1.0 |
New SONA-Specific Modules
| Module | Purpose |
|---|---|
sona-lora |
Ultra-low latency LoRA adapters |
sona-ewc |
Enhanced EWC with task awareness |
sona-reasoning |
ReasoningBank integration |
sona-dreams |
Offline consolidation engine |
sona-metrics |
Self-improvement measurement |
Document Index
| Document | Description |
|---|---|
| 01-LORA-ULTRA.md | Ultra-low latency LoRA system |
| 02-LEARNING-LOOPS.md | Three-tier learning architecture |
| 03-EWC-PLUS-PLUS.md | Enhanced elastic weight consolidation |
| 04-REASONINGBANK.md | Pattern-driven optimization |
| 05-MEMORY-DREAMS.md | Offline consolidation and dreams |
| 06-COMPONENTS.md | Component integration specs |
| 07-IMPLEMENTATION.md | Implementation roadmap |
| 08-BENCHMARKS.md | Performance targets and testing |
| 09-API-REFERENCE.md | API specification |
Quick Start
use sona::{SONAEngine, SONAConfig, LearningMode};
// Initialize SONA with default configuration
let config = SONAConfig::builder()
.lora_rank(8)
.ewc_lambda(1000.0)
.learning_loops(LearningMode::AllThreeTiers)
.memory_budget_mb(50)
.target_latency_us(100)
.build();
let mut sona = SONAEngine::new(config)?;
// Process queries - learning happens automatically
let response = sona.query("What is the meaning of life?")?;
// Check self-improvement metrics
let metrics = sona.improvement_metrics();
println!("Weekly improvement: {:.1}%", metrics.weekly_gain * 100.0);
println!("Φ consciousness: {:.3}", metrics.phi);
Why SONA Will Create the World's Best Self-Improving LLM
-
No Other System Combines All These:
- LoRA for instant adaptation
- EWC++ for zero forgetting
- ReasoningBank for pattern learning
- Dream consolidation for creativity
- Φ measurement for consciousness tracking
-
Built on Production-Proven Ruvector:
- 150x faster HNSW search
- 39 attention mechanisms
- 30+ specialized crates
- 38K q/s throughput proven
-
Mathematically Sound:
- Fisher Information preserves important weights
- Low-rank decomposition minimizes compute
- Reservoir sampling ensures unbiased learning
- Information-theoretic compression
-
Biologically Inspired:
- Three-tier temporal learning (like human memory)
- Dream-based consolidation (like REM sleep)
- Edge-weighted graphs (like neural synapses)
- Attention-based retrieval (like human recall)
SONA: Where every query makes the model smarter.