SONA - Self-Optimizing Neural Architecture

Runtime-adaptive learning for LLM routers and AI systems without expensive retraining.

Quick Start | Documentation | Examples | API Reference

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Overview

SONA enables your AI applications to continuously improve from user feedback, learning in real-time with sub-millisecond overhead. Instead of expensive model retraining, SONA uses a two-tier LoRA (Low-Rank Adaptation) system that adapts routing decisions, response quality, and model selection on-the-fly.

use sona::{SonaEngine, SonaConfig, LearningSignal};

// Create adaptive learning engine
let engine = SonaEngine::new(SonaConfig::default());

// Track user interaction
let traj_id = engine.start_trajectory(query_embedding);
engine.record_step(traj_id, selected_model, confidence, latency_us);
engine.end_trajectory(traj_id, response_quality);

// Learn from feedback - takes ~500μs
engine.learn_from_feedback(LearningSignal::from_feedback(user_liked, latency_ms, quality));

// Future queries benefit from learned patterns
let optimized_embedding = engine.apply_lora(&new_query_embedding);

Why SONA?

Challenge	Traditional Approach	SONA Solution
Improving response quality	Retrain model ($$$, weeks)	Real-time learning (<1ms)
Adapting to user preferences	Manual tuning	Automatic from feedback
Model selection optimization	Static rules	Learned patterns
Preventing knowledge loss	Start fresh each time	EWC++ preserves knowledge
Cross-platform deployment	Separate implementations	Rust + WASM + Node.js

Key Benefits

• Zero-downtime learning - Adapt to user preferences without service interruption
• Sub-millisecond overhead - Real-time learning with <1ms per request
• Memory-efficient - Two-tier LoRA reduces memory by 95% vs full fine-tuning
• Catastrophic forgetting prevention - EWC++ preserves learned knowledge across tasks
• Cross-platform - Native Rust, WASM for browsers, NAPI-RS for Node.js
• Production-ready - Lock-free data structures, 157 tests, comprehensive benchmarks

Performance

Metric	Target	Achieved	Improvement
Instant Loop Latency	<1ms	34μs	29x better
Trajectory Recording	<1μs	112ns	9x better
MicroLoRA Forward (256d)	<100μs	45μs	2.2x better
Memory per Trajectory	<1KB	~800B	20% better
Pattern Extraction	<10ms	~5ms	2x better

Comparison with Alternatives

Feature	SONA	Fine-tuning	RAG	Prompt Engineering
Learning Speed	Real-time	Hours/Days	N/A	Manual
Memory Overhead	<1MB	GBs	Variable	None
Preserves Knowledge	Yes (EWC++)	Risk of forgetting	Yes	Yes
Adapts to Users	Automatic	Requires retraining	No	Manual
Deployment	Any platform	GPU required	Server	Any

Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                           SONA Engine                                    │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                          │
│  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────────┐   │
│  │    MicroLoRA     │  │     BaseLoRA     │  │    ReasoningBank     │   │
│  │   (Rank 1-2)     │  │   (Rank 4-16)    │  │  (Pattern Storage)   │   │
│  │                  │  │                  │  │                      │   │
│  │  • Per-request   │  │  • Hourly batch  │  │  • K-means++ cluster │   │
│  │  • <100μs update │  │  • Consolidation │  │  • Similarity search │   │
│  │  • SIMD accel.   │  │  • Deep patterns │  │  • Quality filtering │   │
│  └────────┬─────────┘  └────────┬─────────┘  └──────────┬───────────┘   │
│           │                     │                       │               │
│  ┌────────▼─────────────────────▼───────────────────────▼───────────┐   │
│  │                      Learning Loops                               │   │
│  │  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐   │   │
│  │  │   Instant (A)   │  │  Background (B) │  │   Coordinator   │   │   │
│  │  │   Per-Query     │  │     Hourly      │  │  Orchestration  │   │   │
│  │  │   ~34μs         │  │     ~5ms        │  │   Sync & Scale  │   │   │
│  │  └─────────────────┘  └─────────────────┘  └─────────────────┘   │   │
│  └──────────────────────────────────────────────────────────────────┘   │
│                                                                          │
│  ┌────────────────────────┐  ┌──────────────────────────────────────┐   │
│  │   Trajectory Buffer    │  │        EWC++ (Anti-Forgetting)       │   │
│  │     (Lock-Free)        │  │                                      │   │
│  │                        │  │  • Online Fisher estimation          │   │
│  │  • Crossbeam ArrayQueue│  │  • Automatic task boundaries         │   │
│  │  • Zero contention     │  │  • Adaptive constraint strength      │   │
│  │  • ~112ns per record   │  │  • Multi-task memory preservation    │   │
│  └────────────────────────┘  └──────────────────────────────────────┘   │
│                                                                          │
└─────────────────────────────────────────────────────────────────────────┘

Installation

Rust

[dependencies]
sona = "0.1"

# With SIMD optimization (default)
sona = { version = "0.1", features = ["simd"] }

# With serialization support
sona = { version = "0.1", features = ["serde-support"] }

JavaScript/TypeScript (Node.js)

npm install @ruvector/sona

WASM (Browser)

# Build WASM package
cd crates/sona
wasm-pack build --target web --features wasm

# Use in your project
cp -r pkg/ your-project/sona/

Quick Start

Rust - Basic Usage

use sona::{SonaEngine, SonaConfig, LearningSignal};

fn main() {
    // 1. Create engine with configuration
    let config = SonaConfig {
        hidden_dim: 256,
        micro_lora_rank: 2,
        base_lora_rank: 16,
        ..Default::default()
    };
    let engine = SonaEngine::new(config);

    // 2. Record a query trajectory
    let query_embedding = vec![0.1; 256];
    let traj_id = engine.start_trajectory(query_embedding);

    // 3. Record routing decisions
    engine.record_step(traj_id, 42, 0.85, 150);  // node_id, score, latency_us
    engine.record_step(traj_id, 17, 0.92, 120);

    // 4. Complete with outcome quality
    engine.end_trajectory(traj_id, 0.90);

    // 5. Learn from user feedback
    let signal = LearningSignal::from_feedback(true, 50.0, 0.95);
    engine.learn_from_feedback(signal);

    // 6. Apply learned optimizations to new queries
    let new_query = vec![1.0; 256];
    let optimized = engine.apply_lora(&new_query);

    println!("Learning complete! Stats: {:?}", engine.stats());
}

Rust - LLM Router Integration

use sona::{SonaEngine, SonaConfig, LearningSignal};
use std::time::Instant;

pub struct AdaptiveLLMRouter {
    sona: SonaEngine,
    models: Vec<Box<dyn LLMModel>>,
}

impl AdaptiveLLMRouter {
    pub fn new(models: Vec<Box<dyn LLMModel>>) -> Self {
        Self {
            sona: SonaEngine::new(SonaConfig::default()),
            models,
        }
    }

    pub async fn route(&self, query: &str, embedding: Vec<f32>) -> Response {
        // Start tracking this query
        let traj_id = self.sona.start_trajectory(embedding.clone());

        // Apply learned optimizations
        let optimized = self.sona.apply_lora(&embedding);

        // Select best model based on learned patterns
        let start = Instant::now();
        let (model_idx, confidence) = self.select_model(&optimized);
        let latency_us = start.elapsed().as_micros() as u64;

        // Record the routing decision
        self.sona.record_step(traj_id, model_idx as u32, confidence, latency_us);

        // Execute query
        let response = self.models[model_idx].generate(query).await;

        // Complete trajectory with response quality
        self.sona.end_trajectory(traj_id, response.quality_score());

        response
    }

    pub fn record_feedback(&self, was_helpful: bool, latency_ms: f32) {
        let quality = if was_helpful { 0.9 } else { 0.2 };
        let signal = LearningSignal::from_feedback(was_helpful, latency_ms, quality);
        self.sona.learn_from_feedback(signal);
    }

    fn select_model(&self, embedding: &[f32]) -> (usize, f32) {
        // Your model selection logic here
        // SONA's optimized embedding helps make better decisions
        (0, 0.95)
    }
}

Node.js

const { SonaEngine } = require('@ruvector/sona');

// Create engine
const engine = new SonaEngine();

// Or with custom configuration
const customEngine = SonaEngine.withConfig(
    2,      // micro_lora_rank
    16,     // base_lora_rank
    10000,  // trajectory_buffer_size
    0.4     // ewc_lambda
);

// Record user interaction
const embedding = Array(256).fill(0.1);
const trajId = engine.startTrajectory(embedding);

engine.recordStep(trajId, 42, 0.85, 150);
engine.recordStep(trajId, 17, 0.92, 120);
engine.endTrajectory(trajId, 0.90);

// Learn from feedback
engine.learnFromFeedback(true, 50.0, 0.95);

// Apply to new queries
const newQuery = Array(256).fill(1.0);
const optimized = engine.applyLora(newQuery);

console.log('Stats:', engine.getStats());

JavaScript (WASM in Browser)

<!DOCTYPE html>
<html>
<head>
    <title>SONA Demo</title>
</head>
<body>
    <script type="module">
        import init, { WasmSonaEngine } from './pkg/sona.js';

        async function main() {
            await init();

            // Create engine (256 = hidden dimension)
            const engine = new WasmSonaEngine(256);

            // Record trajectory
            const embedding = new Float32Array(256).fill(0.1);
            const trajId = engine.start_trajectory(embedding);

            engine.record_step(trajId, 42, 0.85, 150);
            engine.end_trajectory(trajId, 0.90);

            // Learn from feedback
            engine.learn_from_feedback(true, 50.0, 0.95);

            // Apply LoRA transformation
            const input = new Float32Array(256).fill(1.0);
            const output = engine.apply_lora(input);

            console.log('Stats:', engine.get_stats());
        }

        main();
    </script>
</body>
</html>

Core Components

Two-Tier LoRA System

SONA uses a novel two-tier LoRA architecture for different learning timescales:

Tier	Rank	Latency	Update Frequency	Purpose
MicroLoRA	1-2	<100μs	Per-request	Instant user adaptation
BaseLoRA	4-16	~1ms	Hourly	Pattern consolidation

// Apply individual tiers
engine.apply_micro_lora(&input, &mut output);  // Fast, per-request
engine.apply_base_lora(&input, &mut output);   // Deeper patterns

// Apply both tiers (recommended)
let output = engine.apply_lora(&input);

Three Learning Loops

Loop	Frequency	Purpose	Typical Latency
Instant (A)	Per-request	Immediate adaptation from feedback	~34μs
Background (B)	Hourly	Pattern extraction & consolidation	~5ms
Coordinator	Continuous	Loop synchronization & scaling	Minimal

// Loops run automatically, but can be triggered manually
engine.run_instant_cycle();      // Force instant learning
engine.run_background_cycle();   // Force pattern extraction

EWC++ (Elastic Weight Consolidation)

Prevents catastrophic forgetting when learning new patterns:

Feature	Description
Online Fisher	Real-time parameter importance estimation
Task Boundaries	Automatic detection via distribution shift
Adaptive Lambda	Dynamic constraint strength per task
Multi-Task Memory	Circular buffer preserving task knowledge

let config = SonaConfig {
    ewc_lambda: 0.4,           // Constraint strength (0.0-1.0)
    ewc_gamma: 0.95,           // Fisher decay rate
    ewc_fisher_samples: 100,   // Samples for estimation
    ..Default::default()
};

ReasoningBank

K-means++ clustering for trajectory pattern discovery and retrieval:

// Patterns are extracted automatically during background learning
// Query similar patterns for a given embedding:
let similar = engine.query_patterns(&query_embedding, k: 5);

for pattern in similar {
    println!("Quality: {:.2}, Usage: {}", pattern.quality, pattern.usage_count);
}

Configuration

pub struct SonaConfig {
    // Dimensions
    pub hidden_dim: usize,              // Default: 256
    pub embedding_dim: usize,           // Default: 256

    // LoRA Configuration
    pub micro_lora_rank: usize,         // Default: 2 (recommended: 1-2)
    pub base_lora_rank: usize,          // Default: 16 (recommended: 4-16)
    pub lora_alpha: f32,                // Default: 1.0
    pub lora_dropout: f32,              // Default: 0.0

    // Trajectory Buffer
    pub trajectory_buffer_size: usize,  // Default: 10000
    pub max_trajectory_steps: usize,    // Default: 50

    // EWC++ Configuration
    pub ewc_lambda: f32,                // Default: 0.4
    pub ewc_gamma: f32,                 // Default: 0.95
    pub ewc_fisher_samples: usize,      // Default: 100
    pub ewc_online: bool,               // Default: true

    // ReasoningBank
    pub pattern_clusters: usize,        // Default: 32
    pub pattern_quality_threshold: f32, // Default: 0.7
    pub consolidation_interval: usize,  // Default: 1000

    // Learning Rates
    pub micro_lr: f32,                  // Default: 0.01
    pub base_lr: f32,                   // Default: 0.001
}

Practical Use Cases

1. Chatbot Response Quality

// Thumbs up/down feedback
match user_feedback {
    Feedback::ThumbsUp => {
        engine.learn_from_feedback(LearningSignal::positive(latency, 0.95));
    }
    Feedback::ThumbsDown => {
        engine.learn_from_feedback(LearningSignal::negative(latency, 0.2));
    }
    Feedback::Regenerate => {
        engine.learn_from_feedback(LearningSignal::negative(latency, 0.4));
    }
}

2. Multi-Model Router Optimization

// Record which models perform best for different query types
async fn route_with_learning(&self, query: &str, embedding: Vec<f32>) {
    let traj_id = self.sona.start_trajectory(embedding);

    // Try multiple models, record scores
    for (idx, model) in self.models.iter().enumerate() {
        let start = Instant::now();
        let response = model.evaluate(query).await;
        let latency = start.elapsed().as_micros() as u64;

        self.sona.record_step(traj_id, idx as u32, response.score, latency);
    }

    // Select best and complete trajectory
    let best = self.select_best();
    self.sona.end_trajectory(traj_id, best.quality);
}

3. A/B Test Acceleration

// Quickly converge on winning variants using learned patterns
async fn smart_ab_test(&self, query: &str, variants: &[Variant]) -> Response {
    let embedding = self.embed(query);
    let traj_id = self.sona.start_trajectory(embedding.clone());

    // Use learned patterns to bias toward better variants
    let optimized = self.sona.apply_lora(&embedding);
    let variant = self.select_variant_ucb(variants, &optimized);

    let response = variant.execute(query).await;
    self.sona.record_step(traj_id, variant.id, response.quality, latency);
    self.sona.end_trajectory(traj_id, response.quality);

    response
}

4. Personalized Recommendations

// Learn user preferences over time
fn record_interaction(&self, user_id: &str, item: &Item, engaged: bool) {
    let embedding = self.get_user_embedding(user_id);
    let traj_id = self.sona.start_trajectory(embedding);

    self.sona.record_step(traj_id, item.category_id, item.relevance, 0);
    self.sona.end_trajectory(traj_id, if engaged { 1.0 } else { 0.0 });

    let signal = LearningSignal::from_feedback(engaged, 0.0, if engaged { 0.9 } else { 0.1 });
    self.sona.learn_from_feedback(signal);
}

Tutorials

Tutorial 1: Basic Learning Loop

use sona::{SonaEngine, SonaConfig, LearningSignal};

fn main() {
    let engine = SonaEngine::new(SonaConfig::default());

    // Simulate 1000 queries with feedback
    for i in 0..1000 {
        // Generate query embedding
        let query: Vec<f32> = (0..256).map(|_| rand::random()).collect();

        // Record trajectory
        let traj_id = engine.start_trajectory(query);

        for step in 0..3 {
            let score = 0.5 + rand::random::<f32>() * 0.5;
            let latency = 50 + rand::random::<u64>() % 100;
            engine.record_step(traj_id, step, score, latency);
        }

        let quality = 0.6 + rand::random::<f32>() * 0.4;
        engine.end_trajectory(traj_id, quality);

        // 70% positive feedback
        let positive = rand::random::<f32>() > 0.3;
        let signal = LearningSignal::from_feedback(positive, 100.0, quality);
        engine.learn_from_feedback(signal);

        // Run background learning every 100 queries
        if i % 100 == 0 {
            engine.run_background_cycle();
        }
    }

    let stats = engine.stats();
    println!("Trajectories: {}", stats.trajectories_recorded);
    println!("Patterns: {}", stats.patterns_extracted);
    println!("Learning cycles: {}", stats.learning_cycles);
}

Tutorial 2: Production Integration

use sona::SonaEngine;
use std::sync::Arc;
use tokio::time::{interval, Duration};

#[tokio::main]
async fn main() {
    let engine = Arc::new(SonaEngine::new(Default::default()));

    // Background learning task
    let bg_engine = engine.clone();
    tokio::spawn(async move {
        let mut interval = interval(Duration::from_secs(3600)); // Hourly
        loop {
            interval.tick().await;
            bg_engine.run_background_cycle();
            println!("Background learning completed: {:?}", bg_engine.stats());
        }
    });

    // Request handling
    let server_engine = engine.clone();
    // ... your server code using server_engine
}

API Reference

SonaEngine Methods

Method	Description	Latency
new(config)	Create new engine	-
start_trajectory(embedding)	Begin recording query	~50ns
record_step(id, node, score, latency)	Record routing step	~112ns
end_trajectory(id, quality)	Complete trajectory	~100ns
learn_from_feedback(signal)	Apply learning signal	~500μs
apply_lora(input)	Transform with both LoRA tiers	~45μs
apply_micro_lora(input, output)	MicroLoRA only	~20μs
apply_base_lora(input, output)	BaseLoRA only	~25μs
run_instant_cycle()	Force instant learning	~34μs
run_background_cycle()	Force background learning	~5ms
query_patterns(embedding, k)	Find similar patterns	~100μs
stats()	Get engine statistics	~1μs

LearningSignal

Method	Description
from_feedback(success, latency_ms, quality)	Create from user feedback
from_trajectory(trajectory)	Create using REINFORCE algorithm
positive(latency_ms, quality)	Shorthand for positive signal
negative(latency_ms, quality)	Shorthand for negative signal

Feature Flags

Flag	Description	Default
default	Includes serde-support	Yes
simd	AVX2 SIMD acceleration	No
serde-support	Serialization with serde	Yes
wasm	WebAssembly bindings	No
napi	Node.js NAPI-RS bindings	No

# Minimal (no serialization)
sona = { version = "0.1", default-features = false }

# With WASM support
sona = { version = "0.1", features = ["wasm"] }

# With Node.js support
sona = { version = "0.1", features = ["napi"] }

# Full features
sona = { version = "0.1", features = ["simd", "serde-support"] }

Test Coverage

Component	Tests	Status
Core Types	4	Passing
MicroLoRA	6	Passing
Trajectory Buffer	10	Passing
EWC++	7	Passing
ReasoningBank	5	Passing
Learning Loops	7	Passing
Engine	6	Passing
Integration	15	Passing
Total	57	All Passing

Benchmarks

Run benchmarks:

cargo bench -p sona

Key results:

• MicroLoRA forward (256d): 45μs
• Trajectory recording: 112ns
• Instant learning cycle: 34μs
• Background learning: 5ms
• Pattern extraction (1000 trajectories): 5ms

Contributing

Contributions are welcome! Please see our Contributing Guide.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Acknowledgments

• LoRA: Low-Rank Adaptation of Large Language Models
• Elastic Weight Consolidation for continual learning
• K-means++ initialization algorithm

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Documentation | GitHub | Crates.io

Made with Rust by the RuVector Team