SONA - Self-Optimizing Neural Architecture

---

What is SONA?

SONA (Self-Optimizing Neural Architecture) is a real-time learning system that makes your AI applications smarter with every interaction. Instead of expensive model retraining that takes days and costs thousands of dollars, SONA learns from user feedback in sub-millisecond time.

$3

Traditional AI systems have a critical limitation: they don't learn from their mistakes in production. When a user gives negative feedback, that information is typically lost or requires manual intervention to address.

| Traditional Approach | Time | Cost | Downtime |
|---------------------|------|------|----------|
| Fine-tune model | Days-Weeks | $1,000-$100,000+ | Yes |
| Retrain from scratch | Weeks-Months | $10,000-$1M+ | Yes |
| Manual prompt tuning | Hours-Days | Engineering time | No |
| SONA | <1 millisecond | $0 | No |

$3

``
User Query → [SONA Engine] → Model Response → User Feedback
↑ │
└─────── Learning Signal ─────────┘
(< 1ms adaptation)
`

SONA uses three key innovations:

1. Two-Tier LoRA: Fast (MicroLoRA) and deep (BaseLoRA) adaptation layers
2. EWC++: Prevents forgetting previously learned patterns
3. ReasoningBank: Stores and retrieves successful interaction patterns

---

Table of Contents

- Installation
- Quick Start
- Core Concepts
- Tutorials
- Tutorial 1: Your First SONA Application
- Tutorial 2: Building an Adaptive Chatbot
- Tutorial 3: LLM Router with Learning
- Tutorial 4: Browser-Based Learning (WASM)
- Tutorial 5: Node.js Backend Integration
- Tutorial 6: Production Deployment
- Configuration Guide
- API Reference
- Benchmarks
- Troubleshooting

---

Installation

$3

`toml
[dependencies]
ruvector-sona = "0.1.1"

With all features

`bash
npm install @ruvector/sona


or

`bash


Clone and build WASM package

`rust
use ruvector_sona::{SonaEngine, SonaConfig};

fn main() {
// 1. Create engine
let engine = SonaEngine::builder()
.hidden_dim(256)
.build();

// 2. Record a user interaction
let query_embedding = vec![0.1f32; 256];
let traj_id = engine.begin_trajectory(query_embedding);

// 3. Record what happened (model selection, confidence, latency)
engine.add_step(traj_id, vec![0.5; 256], vec![0.8; 64], 0.9);

// 4. Record outcome quality (0.0 = bad, 1.0 = perfect)
engine.end_trajectory(traj_id, 0.85);

// 5. Apply learned optimizations to future queries
let new_query = vec![0.2f32; 256];
let optimized = engine.apply_micro_lora(&new_query);

println!("SONA is learning! Stats: {}", engine.get_stats());
}
`

$3

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

// 1. Create engine
const engine = new SonaEngine(256);

// 2. Record interaction
const queryEmbedding = Array(256).fill(0.1);
const trajId = engine.beginTrajectory(queryEmbedding);

// 3. Add step data
engine.addTrajectoryStep(trajId, Array(256).fill(0.5), Array(64).fill(0.8), 0.9);

// 4. Complete with quality score
engine.endTrajectory(trajId, 0.85);

// 5. Apply learning
const newQuery = Array(256).fill(0.2);
const optimized = engine.applyMicroLora(newQuery);

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

---

Core Concepts

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Embeddings are numerical representations of text. Every word, sentence, or query can be converted into a vector of numbers (typically 256-4096 dimensions). SONA works with these embeddings to learn patterns.

`
"How do I reset my password?" → [0.12, -0.45, 0.78, ..., 0.23] (256 numbers)
"Password reset help" → [0.11, -0.44, 0.79, ..., 0.22] (similar!)
"What's the weather?" → [0.89, 0.12, -0.34, ..., 0.67] (different)
`

$3

A trajectory is a complete record of one user interaction:

`
┌─────────────────────────────────────────────────────────────┐
│ Trajectory │
├─────────────────────────────────────────────────────────────┤
│ Query Embedding: [0.12, -0.45, 0.78, ...] │
│ │
│ Steps: │
│ Step 1: Selected Model A, confidence 0.82, latency 45ms │
│ Step 2: Generated response, confidence 0.91, latency 120ms│
│ Step 3: Formatted output, confidence 0.95, latency 5ms │
│ │
│ Final Quality: 0.85 (user gave thumbs up) │
└─────────────────────────────────────────────────────────────┘
`

$3

SONA uses two types of adaptation:

| Tier | Rank | Speed | Purpose | When Used |
|------|------|-------|---------|-----------|
| MicroLoRA | 2 | ~45μs | Instant adjustments | Every request |
| BaseLoRA | 8-16 | ~1ms | Deep pattern learning | Background (hourly) |

MicroLoRA is like quick reflexes - it adapts immediately based on recent feedback.
BaseLoRA is like long-term memory - it consolidates patterns over time.

$3

When learning new patterns, AI systems often "forget" old ones (catastrophic forgetting). EWC++ (Elastic Weight Consolidation) prevents this by:

1. Tracking which parameters are important for each task
2. Protecting important parameters when learning new tasks
3. Automatically detecting when a "new task" begins

`
Without EWC++: With EWC++:
┌────────────────────┐ ┌────────────────────┐
│ Learn Task A: ✓ │ │ Learn Task A: ✓ │
│ Learn Task B: ✓ │ │ Learn Task B: ✓ │
│ Task A knowledge: ✗ │ │ Task A knowledge: ✓ │
└────────────────────┘ └────────────────────┘
`

$3

ReasoningBank stores successful interaction patterns using K-means++ clustering:

`
┌─────────────────────────────────────────────────────────────┐
│ ReasoningBank │
├─────────────────────────────────────────────────────────────┤
│ Cluster 1: "Password/Account Issues" │
│ - 847 trajectories, avg quality 0.89 │
│ - Best response pattern: Empathetic + Step-by-step │
│ │
│ Cluster 2: "Technical Questions" │
│ - 1,234 trajectories, avg quality 0.92 │
│ - Best response pattern: Detailed + Code examples │
│ │
│ Cluster 3: "General Conversation" │
│ - 2,156 trajectories, avg quality 0.78 │
│ - Best response pattern: Friendly + Concise │
└─────────────────────────────────────────────────────────────┘
`

---

Tutorials

$3

Let's build a simple application that learns from user feedback.

Goal: Create a system that improves response quality based on thumbs up/down.

`rust
use ruvector_sona::{SonaEngine, SonaConfig};

fn main() {
// Step 1: Configure SONA
// Use optimized defaults (benchmark-validated)
let config = SonaConfig::default();

println!("Configuration:");
println!(" MicroLoRA rank: {} (optimal for SIMD)", config.micro_lora_rank);
println!(" Learning rate: {} (+55% quality)", config.micro_lora_lr);
println!(" Pattern clusters: {} (2.3x faster)", config.pattern_clusters);
println!(" EWC lambda: {} (anti-forgetting)", config.ewc_lambda);

// Step 2: Create the engine
let engine = SonaEngine::builder()
.config(config)
.build();

// Step 3: Simulate 100 user interactions
let mut positive_count = 0;
let mut negative_count = 0;

for i in 0..100 {
// Simulate a query embedding (in real app, use your embedding model)
let query_embedding: Vec = (0..256)
.map(|j| ((i 256 + j) as f32 0.001).sin())
.collect();

// Start recording this interaction
let traj_id = engine.begin_trajectory(query_embedding.clone());

// Simulate processing steps
let activations: Vec = query_embedding.iter()
.map(|x| x.tanh())
.collect();
let attention: Vec = vec![1.0 / 64.0; 64];

engine.add_step(traj_id, activations, attention, 0.8);

// Simulate user feedback (70% positive in this example)
let is_positive = (i % 10) < 7;
let quality = if is_positive { 0.9 } else { 0.3 };

if is_positive {
positive_count += 1;
} else {
negative_count += 1;
}

// Complete the trajectory with quality score
engine.end_trajectory(traj_id, quality);

// Run learning tick (processes pending trajectories)
engine.tick();
}

// Step 4: Check what we learned
println!("\nResults after 100 interactions:");
println!(" Positive feedback: {}", positive_count);
println!(" Negative feedback: {}", negative_count);
println!(" Engine stats: {}", engine.get_stats());

// Step 5: Apply learning to a new query
let new_query: Vec = vec![0.5; 256];
let optimized = engine.apply_micro_lora(&new_query);

// The optimized embedding now incorporates learned patterns!
let diff: f32 = new_query.iter()
.zip(optimized.iter())
.map(|(a, b)| (a - b).abs())
.sum();

println!("\nLearning applied! Embedding change magnitude: {:.4}", diff);
}
`

Expected Output:
`
Configuration:
MicroLoRA rank: 2 (optimal for SIMD)
Learning rate: 0.002 (+55% quality)
Pattern clusters: 100 (2.3x faster)
EWC lambda: 2000 (anti-forgetting)

Results after 100 interactions:
Positive feedback: 70
Negative feedback: 30
Engine stats: {"trajectories": 100, "patterns": 12, "micro_updates": 100}

Learning applied! Embedding change magnitude: 0.0847
`

---

$3

Let's build a chatbot that learns to give better responses.

`rust
use ruvector_sona::{SonaEngine, SonaConfig};
use std::collections::HashMap;

/// Adaptive chatbot that learns from user feedback
pub struct AdaptiveChatbot {
engine: SonaEngine,
response_templates: HashMap>,
active_trajectory: Option,
}

impl AdaptiveChatbot {
pub fn new() -> Self {
// Use max_quality preset for chatbot (we want best responses)
let config = SonaConfig::max_quality();

let engine = SonaEngine::builder()
.config(config)
.build();

// Simple response templates (in real app, use LLM)
let mut templates = HashMap::new();
templates.insert("greeting".to_string(), vec![
"Hello! How can I help you today?".to_string(),
"Hi there! What can I do for you?".to_string(),
"Welcome! I'm here to assist you.".to_string(),
]);
templates.insert("farewell".to_string(), vec![
"Goodbye! Have a great day!".to_string(),
"Take care! Feel free to come back anytime.".to_string(),
"Bye! It was nice helping you.".to_string(),
]);
templates.insert("unknown".to_string(), vec![
"I'm not sure I understand. Could you rephrase that?".to_string(),
"Let me think about that...".to_string(),
"Interesting question! Let me help you with that.".to_string(),
]);

Self {
engine,
response_templates: templates,
active_trajectory: None,
}
}

/// Process a user message
pub fn respond(&mut self, message: &str) -> String {
// Step 1: Create embedding from message
let embedding = self.create_embedding(message);

// Step 2: Start trajectory
let traj_id = self.engine.begin_trajectory(embedding.clone());
self.active_trajectory = Some(traj_id);

// Step 3: Apply learned optimizations
let optimized = self.engine.apply_micro_lora(&embedding);

// Step 4: Classify intent using optimized embedding
let intent = self.classify_intent(&optimized);

// Step 5: Record the classification step
let activations: Vec = optimized.iter().map(|x| x.tanh()).collect();
let attention = vec![1.0 / 64.0; 64];
self.engine.add_step(traj_id, activations, attention, 0.8);

// Step 6: Select best response template
let responses = self.response_templates.get(&intent)
.unwrap_or(&self.response_templates["unknown"]);

// Use embedding similarity to pick best response
let response = self.select_best_response(responses, &optimized);

response
}

/// Record user feedback (call after response is shown)
pub fn record_feedback(&mut self, was_helpful: bool) {
if let Some(traj_id) = self.active_trajectory.take() {
let quality = if was_helpful { 0.95 } else { 0.2 };
self.engine.end_trajectory(traj_id, quality);

// Force learning if negative feedback (learn faster from mistakes)
if !was_helpful {
self.engine.force_learn();
}
}
}

/// Create a simple embedding from text
fn create_embedding(&self, text: &str) -> Vec {
// Simple bag-of-characters embedding (use real embeddings in production!)
let mut embedding = vec![0.0f32; 256];
for (i, c) in text.chars().enumerate() {
let idx = (c as usize + i) % 256;
embedding[idx] += 0.1;
}
// Normalize
let norm: f32 = embedding.iter().map(|x| x * x).sum::().sqrt();
if norm > 0.0 {
embedding.iter_mut().for_each(|x| *x /= norm);
}
embedding
}

/// Classify user intent
fn classify_intent(&self, embedding: &[f32]) -> String {
// Simple heuristic (use classifier in production!)
let sum: f32 = embedding.iter().take(10).sum();
if sum > 0.5 {
"greeting".to_string()
} else if sum < -0.5 {
"farewell".to_string()
} else {
"unknown".to_string()
}
}

/// Select best response based on embedding
fn select_best_response(&self, responses: &[String], embedding: &[f32]) -> String {
// Use embedding to deterministically select response
let idx = (embedding[0].abs() * responses.len() as f32) as usize % responses.len();
responses[idx].clone()
}

/// Get learning statistics
pub fn stats(&self) -> String {
self.engine.get_stats()
}
}

fn main() {
let mut bot = AdaptiveChatbot::new();

// Simulate conversation
let conversations = vec![
("Hello!", true),
("Hi there", true),
("What is AI?", false), // Bad response
("Explain machine learning", false), // Bad response
("Thanks, goodbye!", true),
("Hello again!", true),
];

for (message, was_helpful) in conversations {
println!("User: {}", message);
let response = bot.respond(message);
println!("Bot: {}", response);
bot.record_feedback(was_helpful);
println!(" [Feedback: {}]", if was_helpful { "👍" } else { "👎" });
println!();
}

println!("Final stats: {}", bot.stats());
}
`

---

$3

Build a router that learns which LLM to use for different query types.

`rust
use ruvector_sona::{SonaEngine, SonaConfig};
use std::time::Instant;

/// Represents an LLM model
#[derive(Clone)]
pub struct LLMModel {
pub name: String,
pub cost_per_token: f32,
pub avg_quality: f32,
pub avg_latency_ms: u32,
}

/// Adaptive LLM Router that learns optimal model selection
pub struct AdaptiveLLMRouter {
engine: SonaEngine,
models: Vec,
}

impl AdaptiveLLMRouter {
pub fn new(models: Vec) -> Self {
// Use max_throughput for fast routing decisions
let config = SonaConfig::max_throughput();

let engine = SonaEngine::builder()
.config(config)
.build();

Self { engine, models }
}

/// Route a query to the best model
pub fn route(&self, query_embedding: Vec) -> (usize, &LLMModel) {
// Apply learned optimizations
let optimized = self.engine.apply_micro_lora(&query_embedding);

// Find similar patterns
let patterns = self.engine.find_patterns(&optimized, 3);

// Score each model based on patterns and learned preferences
let mut best_idx = 0;
let mut best_score = f32::MIN;

for (idx, model) in self.models.iter().enumerate() {
let mut score = model.avg_quality;

// Boost score if patterns suggest this model works well
for pattern in &patterns {
// Pattern centroid similarity affects model preference
let similarity = cosine_similarity(&optimized, &pattern.centroid);
if similarity > 0.8 {
// High similarity to successful pattern
score += pattern.avg_quality * similarity;
}
}

// Penalize expensive models slightly
score -= model.cost_per_token * 0.1;

if score > best_score {
best_score = score;
best_idx = idx;
}
}

(best_idx, &self.models[best_idx])
}

/// Record the outcome of a routing decision
pub fn record_outcome(
&self,
query_embedding: Vec,
selected_model: usize,
quality: f32,
latency_ms: u32,
) {
// Start trajectory
let traj_id = self.engine.begin_trajectory(query_embedding);

// Record selection step
let model = &self.models[selected_model];
let activations = vec![
model.avg_quality,
model.cost_per_token,
latency_ms as f32 / 1000.0,
];
let activations_padded: Vec = activations.into_iter()
.chain(std::iter::repeat(0.0))
.take(256)
.collect();

let attention = vec![1.0 / 64.0; 64];
self.engine.add_step(traj_id, activations_padded, attention, quality);

// Set route info
self.engine.set_trajectory_route(traj_id, model.name.clone());

// Complete trajectory
self.engine.end_trajectory(traj_id, quality);
}

/// Force background learning cycle
pub fn learn(&self) -> String {
self.engine.force_learn()
}

pub fn stats(&self) -> String {
self.engine.get_stats()
}
}

fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
let norm_a: f32 = a.iter().map(|x| x * x).sum::().sqrt();
let norm_b: f32 = b.iter().map(|x| x * x).sum::().sqrt();
if norm_a > 0.0 && norm_b > 0.0 {
dot / (norm_a * norm_b)
} else {
0.0
}
}

fn main() {
// Define available models
let models = vec![
LLMModel {
name: "GPT-4".to_string(),
cost_per_token: 0.03,
avg_quality: 0.95,
avg_latency_ms: 2000,
},
LLMModel {
name: "GPT-3.5-Turbo".to_string(),
cost_per_token: 0.002,
avg_quality: 0.85,
avg_latency_ms: 500,
},
LLMModel {
name: "Claude-Instant".to_string(),
cost_per_token: 0.001,
avg_quality: 0.80,
avg_latency_ms: 300,
},
LLMModel {
name: "Local-LLaMA".to_string(),
cost_per_token: 0.0001,
avg_quality: 0.70,
avg_latency_ms: 100,
},
];

let router = AdaptiveLLMRouter::new(models);

// Simulate 1000 queries with different types
println!("Training router with 1000 queries...\n");

let query_types = vec![
("simple", vec![0.1f32; 256], 0.70, "Local-LLaMA"), // Simple queries work fine with local
("medium", vec![0.5f32; 256], 0.85, "GPT-3.5-Turbo"), // Medium needs cloud
("complex", vec![0.9f32; 256], 0.95, "GPT-4"), // Complex needs best
];

for i in 0..1000 {
let (query_type, base_embedding, target_quality, expected_model) =
&query_types[i % query_types.len()];

// Add some variation to embeddings
let embedding: Vec = base_embedding.iter()
.enumerate()
.map(|(j, x)| x + (i as f32 j as f32 0.0001).sin() * 0.1)
.collect();

// Route the query
let (model_idx, model) = router.route(embedding.clone());

// Simulate quality based on model fit
let quality = if &model.name == *expected_model {
*target_quality
} else {
target_quality - 0.2 // Penalty for wrong model
};

// Record outcome
router.record_outcome(embedding, model_idx, quality, model.avg_latency_ms);

// Periodic learning
if i % 100 == 0 {
router.learn();
}
}

// Test learned routing
println!("Testing learned routing:\n");

for (query_type, embedding, _, expected) in &query_types {
let (_, model) = router.route(embedding.clone());
let match_status = if &model.name == *expected { "✓" } else { "✗" };
println!(" {} query → {} {} (expected: {})",
query_type, model.name, match_status, expected);
}

println!("\nRouter stats: {}", router.stats());
}
`

---

$3

Deploy SONA in the browser for client-side learning.

`html









🧠 SONA Browser Demo




`

---

$3

Production-ready Node.js integration with Express.

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

const app = express();
app.use(express.json());

// Initialize SONA engine
const engine = SonaEngine.withConfig({
hiddenDim: 256,
microLoraRank: 2, // Optimized for SIMD
microLoraLr: 0.002, // Optimal learning rate
patternClusters: 100, // Fast search
ewcLambda: 2000, // Anti-forgetting
qualityThreshold: 0.3 // Learn from more samples
});

// Track active trajectories
const activeTrajectories = new Map();

// Middleware to create embeddings (replace with your embedding service)
function createEmbedding(text) {
// Simple embedding (use OpenAI/Cohere embeddings in production)
const embedding = new Array(256).fill(0);
for (let i = 0; i < text.length; i++) {
const idx = (text.charCodeAt(i) + i) % 256;
embedding[idx] += 0.1;
}
const norm = Math.sqrt(embedding.reduce((s, x) => s + x * x, 0));
return embedding.map(x => x / (norm || 1));
}

// Start a new interaction
app.post('/api/query', (req, res) => {
const { query, sessionId } = req.body;

// Create embedding
const embedding = createEmbedding(query);

// Start trajectory
const trajId = engine.beginTrajectory(embedding);
activeTrajectories.set(sessionId, { trajId, embedding, startTime: Date.now() });

// Apply learned optimizations
const optimized = engine.applyMicroLora(embedding);

// Find similar patterns for context
const patterns = engine.findPatterns(optimized, 3);

// Record step
const activations = optimized.map(x => Math.tanh(x));
const attention = new Array(64).fill(1/64);
engine.addTrajectoryStep(trajId, activations, attention, 0.8);

res.json({
sessionId,
optimizedEmbedding: optimized,
similarPatterns: patterns.map(p => ({
avgQuality: p.avgQuality,
clusterSize: p.clusterSize,
patternType: p.patternType
})),
message: 'Query processed. Send response quality via /api/feedback'
});
});

// Record feedback
app.post('/api/feedback', (req, res) => {
const { sessionId, quality, wasHelpful } = req.body;

const session = activeTrajectories.get(sessionId);
if (!session) {
return res.status(404).json({ error: 'Session not found' });
}

// Calculate quality score
const qualityScore = quality ?? (wasHelpful ? 0.9 : 0.2);

// Complete trajectory
engine.endTrajectory(session.trajId, qualityScore);

// Run learning tick
const learnResult = engine.tick();

// Clean up
activeTrajectories.delete(sessionId);

res.json({
success: true,
quality: qualityScore,
latencyMs: Date.now() - session.startTime,
learned: learnResult !== null
});
});

// Force learning cycle
app.post('/api/learn', (req, res) => {
const result = engine.forceLearn();
res.json({
success: true,
result,
stats: JSON.parse(engine.getStats())
});
});

// Get stats
app.get('/api/stats', (req, res) => {
res.json(JSON.parse(engine.getStats()));
});

// Health check
app.get('/health', (req, res) => {
res.json({
status: 'healthy',
engine: engine.isEnabled() ? 'active' : 'disabled'
});
});

// Background learning (run hourly)
setInterval(() => {
console.log('Running background learning cycle...');
const result = engine.forceLearn();
console.log('Learning complete:', result);
}, 60 60 1000); // Every hour

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
console.log(SONA server running on port ${PORT});
console.log('Stats:', engine.getStats());
});
`

Usage:

`bash


Start server

curl http://localhost:3000/api/stats
`

---

$3

Best practices for deploying SONA in production.

`rust
use ruvector_sona::{SonaEngine, SonaConfig};
use std::sync::Arc;
use tokio::sync::RwLock;
use tokio::time::{interval, Duration};

/// Production-ready SONA wrapper
pub struct ProductionSona {
engine: Arc>,
metrics: Arc>,
}

#[derive(Default)]
pub struct Metrics {
pub total_requests: u64,
pub total_learning_cycles: u64,
pub positive_feedback: u64,
pub negative_feedback: u64,
pub avg_latency_us: f64,
}

impl ProductionSona {
pub async fn new() -> Self {
// Use optimized defaults
let config = SonaConfig::default();

let engine = SonaEngine::builder()
.config(config)
.build();

let instance = Self {
engine: Arc::new(RwLock::new(engine)),
metrics: Arc::new(RwLock::new(Metrics::default())),
};

// Start background tasks
instance.start_background_tasks().await;

instance
}

async fn start_background_tasks(&self) {
let engine = self.engine.clone();
let metrics = self.metrics.clone();

// Hourly learning cycle
tokio::spawn(async move {
let mut interval = interval(Duration::from_secs(3600));
loop {
interval.tick().await;

let mut engine = engine.write().await;
let result = engine.force_learn();

let mut m = metrics.write().await;
m.total_learning_cycles += 1;

tracing::info!("Background learning completed: {}", result);
}
});

// Metrics logging (every 5 minutes)
let metrics_clone = self.metrics.clone();
tokio::spawn(async move {
let mut interval = interval(Duration::from_secs(300));
loop {
interval.tick().await;
let m = metrics_clone.read().await;
tracing::info!(
"SONA Metrics - Requests: {}, Learning: {}, Positive: {}, Negative: {}",
m.total_requests,
m.total_learning_cycles,
m.positive_feedback,
m.negative_feedback
);
}
});
}

/// Process a query with full observability
pub async fn process(&self, embedding: Vec) -> ProcessResult {
let start = std::time::Instant::now();

let engine = self.engine.read().await;

// Start trajectory
let traj_id = engine.begin_trajectory(embedding.clone());

// Apply optimizations
let optimized = engine.apply_micro_lora(&embedding);

// Find patterns
let patterns = engine.find_patterns(&optimized, 5);

// Update metrics
let latency = start.elapsed().as_micros() as u64;
{
let mut m = self.metrics.write().await;
m.total_requests += 1;
m.avg_latency_us = (m.avg_latency_us * (m.total_requests - 1) as f64
+ latency as f64) / m.total_requests as f64;
}

ProcessResult {
trajectory_id: traj_id,
optimized_embedding: optimized,
similar_patterns: patterns.into_iter().map(|p| PatternInfo {
quality: p.avg_quality,
cluster_size: p.cluster_size,
}).collect(),
latency_us: latency,
}
}

/// Record step in trajectory
pub async fn record_step(
&self,
traj_id: u64,
activations: Vec,
attention: Vec,
reward: f32,
) {
let engine = self.engine.read().await;
engine.add_step(traj_id, activations, attention, reward);
}

/// Complete trajectory with feedback
pub async fn complete(&self, traj_id: u64, quality: f32, was_positive: bool) {
{
let engine = self.engine.read().await;
engine.end_trajectory(traj_id, quality);
}

// Update metrics
let mut m = self.metrics.write().await;
if was_positive {
m.positive_feedback += 1;
} else {
m.negative_feedback += 1;
}
}

/// Get current statistics
pub async fn stats(&self) -> Stats {
let engine = self.engine.read().await;
let engine_stats = engine.get_stats();

let m = self.metrics.read().await;

Stats {
engine_stats,
total_requests: m.total_requests,
total_learning_cycles: m.total_learning_cycles,
positive_feedback: m.positive_feedback,
negative_feedback: m.negative_feedback,
avg_latency_us: m.avg_latency_us,
feedback_ratio: if m.positive_feedback + m.negative_feedback > 0 {
m.positive_feedback as f64 / (m.positive_feedback + m.negative_feedback) as f64
} else {
0.0
},
}
}
}

pub struct ProcessResult {
pub trajectory_id: u64,
pub optimized_embedding: Vec,
pub similar_patterns: Vec,
pub latency_us: u64,
}

pub struct PatternInfo {
pub quality: f32,
pub cluster_size: usize,
}

pub struct Stats {
pub engine_stats: String,
pub total_requests: u64,
pub total_learning_cycles: u64,
pub positive_feedback: u64,
pub negative_feedback: u64,
pub avg_latency_us: f64,
pub feedback_ratio: f64,
}
`

---

Configuration Guide

$3

The default configuration is optimized based on extensive benchmarks:

`rust
SonaConfig {
hidden_dim: 256,
embedding_dim: 256,
micro_lora_rank: 2, // 5% faster than rank-1 (better SIMD)
base_lora_rank: 8,
micro_lora_lr: 0.002, // +55% quality improvement
base_lora_lr: 0.0001,
ewc_lambda: 2000.0, // Better forgetting prevention
pattern_clusters: 100, // 2.3x faster search
trajectory_capacity: 10000,
background_interval_ms: 3600000, // 1 hour
quality_threshold: 0.3, // Learn from more samples
enable_simd: true,
}
`

$3

`rust
// For real-time chat applications
let config = SonaConfig::max_throughput();

// For research/batch processing (best quality)
let config = SonaConfig::max_quality();

// For mobile/edge devices (<5MB memory)
let config = SonaConfig::edge_deployment();

// For high-throughput batch processing
let config = SonaConfig::batch_processing();
`

$3

`rust
let config = SonaConfig {
// Embedding dimensions (match your model)
hidden_dim: 512,
embedding_dim: 512,

// LoRA settings
micro_lora_rank: 2, // 1-2 for speed, keep at 2 for SIMD
base_lora_rank: 16, // 4-16 for expressiveness
micro_lora_lr: 0.002, // Higher = faster learning, risk of instability
base_lora_lr: 0.0001, // Lower = stable consolidation

// Memory protection
ewc_lambda: 2000.0, // Higher = stronger protection against forgetting

// Pattern storage
pattern_clusters: 100, // More clusters = faster search, more memory
trajectory_capacity: 20000,

// Learning triggers
background_interval_ms: 1800000, // 30 minutes
quality_threshold: 0.2, // Lower = learn from more trajectories

// Performance
enable_simd: true,
};
`

---

API Reference

$3

| Method | Description | Typical Latency |
|--------|-------------|-----------------|
| new(hidden_dim) | Create with default config | - |
| with_config(config) | Create with custom config | - |
| builder() | Start building configuration | - |
| begin_trajectory(embedding) | Start recording interaction | ~50ns |
| add_trajectory_step(id, activations, attention, reward) | Add step | ~112ns |
| set_trajectory_route(id, route) | Set model route | ~20ns |
| add_trajectory_context(id, context) | Add context | ~20ns |
| end_trajectory(id, quality) | Complete with quality | ~100ns |
| apply_micro_lora(input) | Fast transformation | ~45μs |
| apply_base_lora(layer, input) | Deep transformation | ~25μs |
| tick() | Run learning if due | ~34μs |
| force_learn() | Force background cycle | ~5ms |
| flush() | Flush instant updates | ~10μs |
| find_patterns(embedding, k) | Find similar patterns | ~100μs |
| get_stats() | Get JSON statistics | ~1μs |
| set_enabled(bool) | Enable/disable engine | ~1ns |
| is_enabled() | Check if enabled | ~1ns |

$3

`typescript
interface JsSonaConfig {
hiddenDim: number; // Required
embeddingDim?: number; // Default: hiddenDim
microLoraRank?: number; // Default: 2
baseLoraRank?: number; // Default: 8
microLoraLr?: number; // Default: 0.002
baseLoraLr?: number; // Default: 0.0001
ewcLambda?: number; // Default: 2000
patternClusters?: number; // Default: 100
trajectoryCapacity?: number; // Default: 10000
backgroundIntervalMs?: number; // Default: 3600000
qualityThreshold?: number; // Default: 0.3
enableSimd?: boolean; // Default: true
}
`

$3

`typescript
interface JsLearnedPattern {
id: string;
centroid: number[];
clusterSize: number;
totalWeight: number;
avgQuality: number;
createdAt: string;
lastAccessed: string;
accessCount: number;
patternType: string;
}
`

---

Benchmarks

$3

| Operation | Target | Achieved | Improvement |
|-----------|--------|----------|-------------|
| MicroLoRA Forward (256d) | <100μs | 45μs | 2.2x better |
| Trajectory Recording | <1μs | 112ns | 9x better |
| Instant Learning Cycle | <1ms | 34μs | 29x better |
| Pattern Search (100 clusters) | <5ms | 1.3ms | 3.8x better |
| Background Learning | <10ms | ~5ms | 2x better |
| Memory per Trajectory | <1KB | ~800B | 20% better |

$3

| Scenario | Ops/Second | Latency (p99) |
|----------|------------|---------------|
| MicroLoRA Rank-2 (SIMD) | 2,211 | 0.85ms |
| MicroLoRA Rank-1 | 2,100 | 0.90ms |
| Batch Size 32 | 2,236 | 0.45ms/vector |
| Pattern Search (k=5) | 770 | 1.5ms |

$3

`bash


Run all benchmarks

1. "MicroLoRA rank must be 1-2"
`rust
// Wrong
let config = SonaConfig { micro_lora_rank: 4, .. };

// Correct - MicroLoRA is limited to rank 1-2 for speed
let config = SonaConfig { micro_lora_rank: 2, .. };

// For higher ranks, use BaseLoRA
let config = SonaConfig { base_lora_rank: 16, .. };
`

2. Embedding dimension mismatch
`rust
// Engine expects 256-dim embeddings
let engine = SonaEngine::new(256);

// Wrong - 512-dim embedding
let embedding = vec![0.1f32; 512]; // Panic!

// Correct
let embedding = vec![0.1f32; 256];
let traj_id = engine.begin_trajectory(embedding);
`

3. Low quality scores not learning
`rust
// If quality_threshold is 0.5, scores below won't trigger learning
let config = SonaConfig {
quality_threshold: 0.5, // Only learns from quality >= 0.5
..Default::default()
};

// Lower threshold to learn from more feedback
let config = SonaConfig {
quality_threshold: 0.2, // Learns from quality >= 0.2
..Default::default()
};
`

4. Memory growing unbounded
`rust
// Limit trajectory buffer
let config = SonaConfig {
trajectory_capacity: 10000, // Max trajectories in memory
..Default::default()
};

// Force learning to clear buffer
engine.force_learn();
``

$3

1. Use Rank-2 MicroLoRA - 5% faster due to SIMD alignment
2. Batch inputs when possible - Optimal batch size is 32
3. Use 100 pattern clusters - 2.3x faster than 50
4. Enable SIMD - 10% speedup on supported CPUs
5. Run background learning during low-traffic periods

---

License

Licensed under either of:

- Apache License, Version 2.0 (LICENSE-APACHE)
- MIT License (LICENSE-MIT)

at your option.

Contributing

Contributions welcome! Please see our Contributing Guide.

Acknowledgments

- LoRA Paper - Low-Rank Adaptation
- EWC Paper - Elastic Weight Consolidation
- K-means++ - Initialization algorithm

---