Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/ruvLLM/benches/attention.rs

@@ -0,0 +1,160 @@
+//! Attention engine benchmarks for RuvLLM
+//!
+//! Benchmarks multi-head graph attention.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use rand::{Rng, SeedableRng};
+use ruvllm::attention::GraphAttentionEngine;
+use ruvllm::config::EmbeddingConfig;
+use ruvllm::memory::SubGraph;
+use ruvllm::types::{EdgeType, MemoryEdge, MemoryNode, NodeType};
+use std::collections::HashMap;
+
+fn create_random_node(id: &str, dim: usize, seed: u64) -> MemoryNode {
+    let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
+    let mut vec: Vec<f32> = (0..dim).map(|_| rng.gen::<f32>() - 0.5).collect();
+    let norm: f32 = vec.iter().map(|x| x * x).sum::<f32>().sqrt();
+    vec.iter_mut().for_each(|x| *x /= norm);
+
+    MemoryNode {
+        id: id.into(),
+        vector: vec,
+        text: format!("Node {}", id),
+        node_type: NodeType::Document,
+        source: "bench".into(),
+        metadata: HashMap::new(),
+    }
+}
+
+fn create_subgraph(num_nodes: usize, num_edges: usize, dim: usize) -> SubGraph {
+    let nodes: Vec<MemoryNode> = (0..num_nodes)
+        .map(|i| create_random_node(&format!("n-{}", i), dim, i as u64))
+        .collect();
+
+    let edges: Vec<MemoryEdge> = (0..num_edges.min(num_nodes.saturating_sub(1)))
+        .map(|i| MemoryEdge {
+            id: format!("e-{}", i),
+            src: format!("n-{}", i),
+            dst: format!("n-{}", (i + 1) % num_nodes),
+            edge_type: EdgeType::Follows,
+            weight: 0.8,
+            metadata: HashMap::new(),
+        })
+        .collect();
+
+    SubGraph {
+        nodes,
+        edges,
+        center_ids: vec!["n-0".into()],
+    }
+}
+
+fn benchmark_attention_forward(c: &mut Criterion) {
+    let config = EmbeddingConfig::default();
+    let engine = GraphAttentionEngine::new(&config).unwrap();
+
+    let query = vec![0.1f32; config.dimension];
+    let subgraph = create_subgraph(10, 9, config.dimension);
+
+    c.bench_function("attention_forward_10_nodes", |b| {
+        b.iter(|| black_box(engine.attend(&query, &subgraph).unwrap()))
+    });
+}
+
+fn benchmark_attention_varying_nodes(c: &mut Criterion) {
+    let config = EmbeddingConfig::default();
+    let engine = GraphAttentionEngine::new(&config).unwrap();
+
+    let query = vec![0.1f32; config.dimension];
+
+    let mut group = c.benchmark_group("attention_nodes");
+    for num_nodes in [5, 10, 20, 50, 100] {
+        let subgraph = create_subgraph(num_nodes, num_nodes - 1, config.dimension);
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(num_nodes),
+            &subgraph,
+            |b, subgraph| b.iter(|| black_box(engine.attend(&query, subgraph).unwrap())),
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_attention_varying_edges(c: &mut Criterion) {
+    let config = EmbeddingConfig::default();
+    let engine = GraphAttentionEngine::new(&config).unwrap();
+
+    let query = vec![0.1f32; config.dimension];
+
+    let mut group = c.benchmark_group("attention_edges");
+    for num_edges in [0, 10, 25, 50, 100] {
+        let subgraph = create_subgraph(50, num_edges, config.dimension);
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(num_edges),
+            &subgraph,
+            |b, subgraph| b.iter(|| black_box(engine.attend(&query, subgraph).unwrap())),
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_attention_varying_dims(c: &mut Criterion) {
+    let mut group = c.benchmark_group("attention_dimension");
+    for dim in [128, 256, 512, 768, 1024] {
+        let config = EmbeddingConfig {
+            dimension: dim,
+            ..EmbeddingConfig::default()
+        };
+        let engine = GraphAttentionEngine::new(&config).unwrap();
+
+        let query = vec![0.1f32; dim];
+        let subgraph = create_subgraph(20, 19, dim);
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(dim),
+            &subgraph,
+            |b, subgraph| b.iter(|| black_box(engine.attend(&query, subgraph).unwrap())),
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_cross_attention(c: &mut Criterion) {
+    let config = EmbeddingConfig::default();
+    let engine = GraphAttentionEngine::new(&config).unwrap();
+
+    let query = vec![0.1f32; config.dimension];
+    let subgraph = create_subgraph(20, 19, config.dimension);
+
+    c.bench_function("cross_attention_20_nodes", |b| {
+        b.iter(|| black_box(engine.cross_attend(&query, &subgraph).unwrap()))
+    });
+}
+
+fn benchmark_attention_empty_graph(c: &mut Criterion) {
+    let config = EmbeddingConfig::default();
+    let engine = GraphAttentionEngine::new(&config).unwrap();
+
+    let query = vec![0.1f32; config.dimension];
+    let subgraph = SubGraph {
+        nodes: vec![],
+        edges: vec![],
+        center_ids: vec![],
+    };
+
+    c.bench_function("attention_empty_graph", |b| {
+        b.iter(|| black_box(engine.attend(&query, &subgraph).unwrap()))
+    });
+}
+
+criterion_group!(
+    benches,
+    benchmark_attention_forward,
+    benchmark_attention_varying_nodes,
+    benchmark_attention_varying_edges,
+    benchmark_attention_varying_dims,
+    benchmark_cross_attention,
+    benchmark_attention_empty_graph,
+);
+criterion_main!(benches);

vendor/ruvector/examples/ruvLLM/benches/memory.rs

@@ -0,0 +1,222 @@
+//! Memory service benchmarks for RuvLLM
+//!
+//! Benchmarks HNSW insertion, search, and graph operations.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use rand::{Rng, SeedableRng};
+use ruvllm::config::MemoryConfig;
+use ruvllm::memory::MemoryService;
+use ruvllm::types::{EdgeType, MemoryEdge, MemoryNode, NodeType};
+use std::collections::HashMap;
+use tokio::runtime::Runtime;
+
+fn create_random_node(id: &str, dim: usize, seed: u64) -> MemoryNode {
+    let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
+    let mut vec: Vec<f32> = (0..dim).map(|_| rng.gen::<f32>() - 0.5).collect();
+    let norm: f32 = vec.iter().map(|x| x * x).sum::<f32>().sqrt();
+    vec.iter_mut().for_each(|x| *x /= norm);
+
+    MemoryNode {
+        id: id.into(),
+        vector: vec,
+        text: format!("Node {}", id),
+        node_type: NodeType::Document,
+        source: "bench".into(),
+        metadata: HashMap::new(),
+    }
+}
+
+fn benchmark_memory_insert(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+    let config = MemoryConfig::default();
+    let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+    let mut counter = 0u64;
+
+    c.bench_function("memory_insert_single", |b| {
+        b.iter(|| {
+            counter += 1;
+            let node = create_random_node(&format!("bench-{}", counter), 768, counter);
+            black_box(memory.insert_node(node).unwrap())
+        })
+    });
+}
+
+fn benchmark_memory_insert_batch(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let mut group = c.benchmark_group("memory_insert_batch");
+    for batch_size in [10, 50, 100, 500] {
+        group.throughput(Throughput::Elements(batch_size as u64));
+
+        let config = MemoryConfig::default();
+        let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+        let nodes: Vec<MemoryNode> = (0..batch_size)
+            .map(|i| create_random_node(&format!("batch-{}", i), 768, i as u64))
+            .collect();
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(batch_size),
+            &nodes,
+            |b, nodes| {
+                b.iter(|| {
+                    for node in nodes.clone() {
+                        black_box(memory.insert_node(node).unwrap());
+                    }
+                })
+            },
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_memory_search(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+    let config = MemoryConfig::default();
+    let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+    // Pre-populate with nodes
+    for i in 0..1000 {
+        let node = create_random_node(&format!("search-{}", i), 768, i as u64);
+        memory.insert_node(node).unwrap();
+    }
+
+    let query = vec![0.1f32; 768];
+
+    c.bench_function("memory_search_k10_1000", |b| {
+        b.to_async(&rt).iter(|| async {
+            black_box(memory.search_with_graph(&query, 10, 64, 0).await.unwrap())
+        })
+    });
+}
+
+fn benchmark_memory_search_varying_k(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+    let config = MemoryConfig::default();
+    let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+    // Pre-populate
+    for i in 0..1000 {
+        let node = create_random_node(&format!("k-{}", i), 768, i as u64);
+        memory.insert_node(node).unwrap();
+    }
+
+    let query = vec![0.1f32; 768];
+
+    let mut group = c.benchmark_group("memory_search_k");
+    for k in [1, 5, 10, 20, 50, 100] {
+        group.bench_with_input(BenchmarkId::from_parameter(k), &k, |b, &k| {
+            b.to_async(&rt).iter(|| async {
+                black_box(memory.search_with_graph(&query, k, 64, 0).await.unwrap())
+            })
+        });
+    }
+    group.finish();
+}
+
+fn benchmark_memory_search_varying_ef(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+    let config = MemoryConfig::default();
+    let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+    // Pre-populate
+    for i in 0..1000 {
+        let node = create_random_node(&format!("ef-{}", i), 768, i as u64);
+        memory.insert_node(node).unwrap();
+    }
+
+    let query = vec![0.1f32; 768];
+
+    let mut group = c.benchmark_group("memory_search_ef");
+    for ef in [16, 32, 64, 128, 256] {
+        group.bench_with_input(BenchmarkId::from_parameter(ef), &ef, |b, &ef| {
+            b.to_async(&rt).iter(|| async {
+                black_box(memory.search_with_graph(&query, 10, ef, 0).await.unwrap())
+            })
+        });
+    }
+    group.finish();
+}
+
+fn benchmark_memory_search_with_graph(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+    let config = MemoryConfig::default();
+    let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+    // Pre-populate with nodes and edges
+    for i in 0..500 {
+        let node = create_random_node(&format!("graph-{}", i), 768, i as u64);
+        memory.insert_node(node).unwrap();
+    }
+
+    for i in 0..499 {
+        let edge = MemoryEdge {
+            id: format!("edge-{}", i),
+            src: format!("graph-{}", i),
+            dst: format!("graph-{}", i + 1),
+            edge_type: EdgeType::Follows,
+            weight: 0.8,
+            metadata: HashMap::new(),
+        };
+        memory.insert_edge(edge).unwrap();
+    }
+
+    let query = vec![0.1f32; 768];
+
+    let mut group = c.benchmark_group("memory_search_hops");
+    for hops in [0, 1, 2, 3] {
+        group.bench_with_input(BenchmarkId::from_parameter(hops), &hops, |b, &hops| {
+            b.to_async(&rt).iter(|| async {
+                black_box(
+                    memory
+                        .search_with_graph(&query, 10, 64, hops)
+                        .await
+                        .unwrap(),
+                )
+            })
+        });
+    }
+    group.finish();
+}
+
+fn benchmark_memory_scaling(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let mut group = c.benchmark_group("memory_scaling");
+    for num_nodes in [100, 500, 1000, 5000] {
+        let config = MemoryConfig::default();
+        let memory = rt.block_on(MemoryService::new(&config)).unwrap();
+
+        // Pre-populate
+        for i in 0..num_nodes {
+            let node = create_random_node(&format!("scale-{}", i), 768, i as u64);
+            memory.insert_node(node).unwrap();
+        }
+
+        let query = vec![0.1f32; 768];
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(num_nodes),
+            &num_nodes,
+            |b, _| {
+                b.to_async(&rt).iter(|| async {
+                    black_box(memory.search_with_graph(&query, 10, 64, 0).await.unwrap())
+                })
+            },
+        );
+    }
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    benchmark_memory_insert,
+    benchmark_memory_insert_batch,
+    benchmark_memory_search,
+    benchmark_memory_search_varying_k,
+    benchmark_memory_search_varying_ef,
+    benchmark_memory_search_with_graph,
+    benchmark_memory_scaling,
+);
+criterion_main!(benches);

vendor/ruvector/examples/ruvLLM/benches/pipeline.rs

@@ -0,0 +1,124 @@
+//! Pipeline benchmarks for RuvLLM
+//!
+//! Benchmarks the complete request-to-response pipeline.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ruvllm::{Config, Request, RuvLLM};
+use tokio::runtime::Runtime;
+
+fn benchmark_query(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let config = Config::builder()
+        .embedding_dim(128)
+        .router_hidden_dim(32)
+        .learning_enabled(false)
+        .build()
+        .unwrap();
+
+    let llm = rt.block_on(RuvLLM::new(config)).unwrap();
+
+    c.bench_function("query_simple", |b| {
+        b.to_async(&rt)
+            .iter(|| async { black_box(llm.query("What is Rust?").await.unwrap()) })
+    });
+}
+
+fn benchmark_query_lengths(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let config = Config::builder()
+        .embedding_dim(128)
+        .router_hidden_dim(32)
+        .learning_enabled(false)
+        .build()
+        .unwrap();
+
+    let llm = rt.block_on(RuvLLM::new(config)).unwrap();
+
+    let queries = vec![
+        ("short", "Hi"),
+        ("medium", "What is machine learning and how does it work?"),
+        ("long", "Please explain in detail how neural networks process information, including concepts like forward propagation, backpropagation, gradient descent, and the role of activation functions in learning complex patterns from data."),
+    ];
+
+    let mut group = c.benchmark_group("query_by_length");
+    for (name, query) in queries {
+        group.bench_with_input(BenchmarkId::from_parameter(name), &query, |b, query| {
+            b.to_async(&rt)
+                .iter(|| async { black_box(llm.query(*query).await.unwrap()) })
+        });
+    }
+    group.finish();
+}
+
+fn benchmark_concurrent_queries(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let config = Config::builder()
+        .embedding_dim(128)
+        .router_hidden_dim(32)
+        .learning_enabled(false)
+        .build()
+        .unwrap();
+
+    let llm = std::sync::Arc::new(rt.block_on(RuvLLM::new(config)).unwrap());
+
+    let mut group = c.benchmark_group("concurrent_queries");
+    for concurrency in [1, 2, 4, 8] {
+        group.bench_with_input(
+            BenchmarkId::from_parameter(concurrency),
+            &concurrency,
+            |b, &concurrency| {
+                b.to_async(&rt).iter(|| async {
+                    let mut handles = Vec::new();
+                    for _ in 0..concurrency {
+                        let llm_clone = llm.clone();
+                        handles.push(tokio::spawn(async move {
+                            llm_clone.query("Test query").await.unwrap()
+                        }));
+                    }
+                    for handle in handles {
+                        black_box(handle.await.unwrap());
+                    }
+                })
+            },
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_session(c: &mut Criterion) {
+    let rt = Runtime::new().unwrap();
+
+    let config = Config::builder()
+        .embedding_dim(128)
+        .router_hidden_dim(32)
+        .learning_enabled(false)
+        .build()
+        .unwrap();
+
+    let llm = rt.block_on(RuvLLM::new(config)).unwrap();
+
+    c.bench_function("session_multi_turn", |b| {
+        b.to_async(&rt).iter(|| async {
+            let session = llm.new_session();
+            black_box(llm.query_session(&session, "First question").await.unwrap());
+            black_box(llm.query_session(&session, "Follow up").await.unwrap());
+            black_box(
+                llm.query_session(&session, "Another follow up")
+                    .await
+                    .unwrap(),
+            );
+        })
+    });
+}
+
+criterion_group!(
+    benches,
+    benchmark_query,
+    benchmark_query_lengths,
+    benchmark_concurrent_queries,
+    benchmark_session,
+);
+criterion_main!(benches);

vendor/ruvector/examples/ruvLLM/benches/router.rs

@@ -0,0 +1,150 @@
+//! Router benchmarks for RuvLLM
+//!
+//! Benchmarks FastGRNN router forward pass and training.
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ruvllm::config::RouterConfig;
+use ruvllm::router::FastGRNNRouter;
+use ruvllm::types::RouterSample;
+
+fn benchmark_router_forward(c: &mut Criterion) {
+    let config = RouterConfig::default();
+    let router = FastGRNNRouter::new(&config).unwrap();
+
+    let features = vec![0.1f32; config.input_dim];
+    let hidden = vec![0.0f32; config.hidden_dim];
+
+    c.bench_function("router_forward", |b| {
+        b.iter(|| black_box(router.forward(&features, &hidden).unwrap()))
+    });
+}
+
+fn benchmark_router_forward_batch_sizes(c: &mut Criterion) {
+    let config = RouterConfig::default();
+    let router = FastGRNNRouter::new(&config).unwrap();
+    let hidden = vec![0.0f32; config.hidden_dim];
+
+    let mut group = c.benchmark_group("router_forward_features");
+    for feature_dim in [64, 128, 256, 512] {
+        let config = RouterConfig {
+            input_dim: feature_dim,
+            ..RouterConfig::default()
+        };
+        let router = FastGRNNRouter::new(&config).unwrap();
+        let features = vec![0.1f32; feature_dim];
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(feature_dim),
+            &features,
+            |b, features| b.iter(|| black_box(router.forward(features, &hidden).unwrap())),
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_router_training(c: &mut Criterion) {
+    let config = RouterConfig::default();
+    let mut router = FastGRNNRouter::new(&config).unwrap();
+
+    let samples: Vec<RouterSample> = (0..32)
+        .map(|i| RouterSample {
+            features: vec![0.1; config.input_dim],
+            label_model: i % 4,
+            label_context: i % 5,
+            label_temperature: 0.7,
+            label_top_p: 0.9,
+            quality: 0.8,
+            latency_ms: 100.0,
+        })
+        .collect();
+
+    c.bench_function("router_train_batch_32", |b| {
+        b.iter(|| black_box(router.train_batch(&samples, 0.001, 0.0, None, None)))
+    });
+}
+
+fn benchmark_router_training_batch_sizes(c: &mut Criterion) {
+    let config = RouterConfig::default();
+
+    let mut group = c.benchmark_group("router_train_batch");
+    for batch_size in [8, 16, 32, 64, 128] {
+        let mut router = FastGRNNRouter::new(&config).unwrap();
+        let samples: Vec<RouterSample> = (0..batch_size)
+            .map(|i| RouterSample {
+                features: vec![0.1; config.input_dim],
+                label_model: i % 4,
+                label_context: i % 5,
+                label_temperature: 0.7,
+                label_top_p: 0.9,
+                quality: 0.8,
+                latency_ms: 100.0,
+            })
+            .collect();
+
+        group.bench_with_input(
+            BenchmarkId::from_parameter(batch_size),
+            &samples,
+            |b, samples| b.iter(|| black_box(router.train_batch(samples, 0.001, 0.0, None, None))),
+        );
+    }
+    group.finish();
+}
+
+fn benchmark_router_ewc(c: &mut Criterion) {
+    let config = RouterConfig::default();
+    let mut router = FastGRNNRouter::new(&config).unwrap();
+
+    let samples: Vec<RouterSample> = (0..32)
+        .map(|i| RouterSample {
+            features: vec![0.1; config.input_dim],
+            label_model: i % 4,
+            label_context: i % 5,
+            label_temperature: 0.7,
+            label_top_p: 0.9,
+            quality: 0.8,
+            latency_ms: 100.0,
+        })
+        .collect();
+
+    // Pre-compute Fisher and optimal weights
+    let fisher = router.compute_fisher(&samples);
+    let optimal = router.get_weights();
+
+    c.bench_function("router_train_with_ewc", |b| {
+        b.iter(|| {
+            black_box(router.train_batch(&samples, 0.001, 0.4, Some(&fisher), Some(&optimal)))
+        })
+    });
+}
+
+fn benchmark_fisher_computation(c: &mut Criterion) {
+    let config = RouterConfig::default();
+    let router = FastGRNNRouter::new(&config).unwrap();
+
+    let samples: Vec<RouterSample> = (0..100)
+        .map(|i| RouterSample {
+            features: vec![0.1; config.input_dim],
+            label_model: i % 4,
+            label_context: i % 5,
+            label_temperature: 0.7,
+            label_top_p: 0.9,
+            quality: 0.8,
+            latency_ms: 100.0,
+        })
+        .collect();
+
+    c.bench_function("router_compute_fisher_100", |b| {
+        b.iter(|| black_box(router.compute_fisher(&samples)))
+    });
+}
+
+criterion_group!(
+    benches,
+    benchmark_router_forward,
+    benchmark_router_forward_batch_sizes,
+    benchmark_router_training,
+    benchmark_router_training_batch_sizes,
+    benchmark_router_ewc,
+    benchmark_fisher_computation,
+);
+criterion_main!(benches);

vendor/ruvector/examples/ruvLLM/benches/sona_bench.rs

@@ -0,0 +1,579 @@
+//! SONA (Self-Optimizing Neural Architecture) Performance Benchmarks
+//!
+//! Comprehensive benchmarks for all SONA components:
+//! - MicroLoRA forward pass (target: <100μs)
+//! - Trajectory recording (target: <1μs per step)
+//! - ReasoningBank pattern extraction
+//! - InstantLoop full cycle (target: <1ms)
+//! - EWC++ loss computation
+
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use ruvllm::sona::*;
+
+// ============================================================================
+// MicroLoRA Benchmarks
+// ============================================================================
+
+fn micro_lora_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("micro_lora");
+
+    // Test different hidden dimensions
+    for dim in [128, 256, 512] {
+        group.throughput(Throughput::Elements(dim as u64));
+
+        // Rank 1 benchmarks
+        group.bench_with_input(BenchmarkId::new("forward_rank1", dim), &dim, |b, &dim| {
+            let lora = MicroLoRA::new(dim, 1);
+            let input = vec![1.0f32; dim];
+            let mut output = vec![0.0f32; dim];
+
+            b.iter(|| {
+                lora.forward(black_box(&input), black_box(&mut output));
+            });
+        });
+
+        // Rank 2 benchmarks
+        group.bench_with_input(BenchmarkId::new("forward_rank2", dim), &dim, |b, &dim| {
+            let lora = MicroLoRA::new(dim, 2);
+            let input = vec![1.0f32; dim];
+            let mut output = vec![0.0f32; dim];
+
+            b.iter(|| {
+                lora.forward(black_box(&input), black_box(&mut output));
+            });
+        });
+
+        // Scalar (non-SIMD) forward pass for comparison
+        group.bench_with_input(BenchmarkId::new("forward_scalar", dim), &dim, |b, &dim| {
+            let lora = MicroLoRA::new(dim, 1);
+            let input = vec![1.0f32; dim];
+            let mut output = vec![0.0f32; dim];
+
+            b.iter(|| {
+                lora.forward_scalar(black_box(&input), black_box(&mut output));
+            });
+        });
+
+        // Gradient accumulation
+        group.bench_with_input(
+            BenchmarkId::new("accumulate_gradient", dim),
+            &dim,
+            |b, &dim| {
+                let mut lora = MicroLoRA::new(dim, 1);
+                let signal = LearningSignal::with_gradient(vec![0.5; dim], vec![0.1; dim], 0.8);
+
+                b.iter(|| {
+                    lora.accumulate_gradient(black_box(&signal));
+                });
+            },
+        );
+
+        // Apply accumulated gradients
+        group.bench_with_input(
+            BenchmarkId::new("apply_accumulated", dim),
+            &dim,
+            |b, &dim| {
+                let mut lora = MicroLoRA::new(dim, 1);
+
+                // Pre-accumulate some gradients
+                let signal = LearningSignal::with_gradient(vec![0.5; dim], vec![0.1; dim], 0.8);
+                for _ in 0..10 {
+                    lora.accumulate_gradient(&signal);
+                }
+
+                b.iter(|| {
+                    lora.apply_accumulated(black_box(0.001));
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Trajectory Recording Benchmarks
+// ============================================================================
+
+fn trajectory_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("trajectory");
+
+    // Single step recording
+    group.bench_function("record_step", |b| {
+        let buffer = TrajectoryBuffer::new(10000);
+        let id_gen = TrajectoryIdGen::new();
+
+        b.iter(|| {
+            let trajectory = QueryTrajectory::new(id_gen.next(), vec![0.1, 0.2, 0.3, 0.4]);
+            buffer.record(black_box(trajectory));
+        });
+    });
+
+    // Builder - complete trajectory construction
+    for steps in [5, 10, 20] {
+        group.bench_with_input(
+            BenchmarkId::new("build_trajectory", steps),
+            &steps,
+            |b, &steps| {
+                b.iter(|| {
+                    let mut builder = TrajectoryBuilder::new(1, vec![0.1, 0.2, 0.3, 0.4]);
+
+                    for i in 0..steps {
+                        builder.add_step(vec![0.5; 128], vec![0.3; 64], 0.7);
+                    }
+
+                    black_box(builder.build(0.85));
+                });
+            },
+        );
+    }
+
+    // Drain operations
+    group.bench_function("drain_all", |b| {
+        let buffer = TrajectoryBuffer::new(10000);
+
+        // Pre-fill buffer
+        for i in 0..1000 {
+            buffer.record(QueryTrajectory::new(i, vec![0.1, 0.2]));
+        }
+
+        b.iter(|| {
+            let drained = buffer.drain();
+            black_box(drained);
+
+            // Refill for next iteration
+            for i in 0..1000 {
+                buffer.record(QueryTrajectory::new(i, vec![0.1, 0.2]));
+            }
+        });
+    });
+
+    group.bench_function("drain_batch_100", |b| {
+        let buffer = TrajectoryBuffer::new(10000);
+
+        // Pre-fill buffer
+        for i in 0..1000 {
+            buffer.record(QueryTrajectory::new(i, vec![0.1, 0.2]));
+        }
+
+        b.iter(|| {
+            let drained = buffer.drain_n(100);
+            black_box(drained);
+
+            // Refill what we drained
+            for i in 0..100 {
+                buffer.record(QueryTrajectory::new(i, vec![0.1, 0.2]));
+            }
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// ReasoningBank Benchmarks
+// ============================================================================
+
+fn reasoning_bank_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("reasoning_bank");
+
+    // Pattern extraction with K-means++
+    for trajectory_count in [100, 500, 1000] {
+        group.bench_with_input(
+            BenchmarkId::new("extract_patterns", trajectory_count),
+            &trajectory_count,
+            |b, &count| {
+                let config = PatternConfig {
+                    k_clusters: 10,
+                    embedding_dim: 128,
+                    max_iterations: 50,
+                    min_cluster_size: 3,
+                    quality_threshold: 0.5,
+                    ..Default::default()
+                };
+
+                let mut bank = ReasoningBank::new(config);
+
+                // Add trajectories
+                for i in 0..count {
+                    let mut trajectory = QueryTrajectory::new(
+                        i,
+                        vec![
+                            (i as f32 * 0.1) % 1.0,
+                            (i as f32 * 0.2) % 1.0,
+                            (i as f32 * 0.3) % 1.0,
+                        ],
+                    );
+                    trajectory.finalize(0.7 + (i as f32 * 0.001) % 0.3, 1000);
+                    bank.add_trajectory(&trajectory);
+                }
+
+                b.iter(|| {
+                    let patterns = bank.extract_patterns();
+                    black_box(patterns);
+                });
+            },
+        );
+    }
+
+    // Query similar patterns
+    group.bench_function("query_patterns", |b| {
+        let config = PatternConfig {
+            k_clusters: 20,
+            embedding_dim: 128,
+            min_cluster_size: 3,
+            quality_threshold: 0.5,
+            ..Default::default()
+        };
+
+        let mut bank = ReasoningBank::new(config);
+
+        // Build up pattern database
+        for i in 0..1000 {
+            let mut trajectory = QueryTrajectory::new(i, vec![(i as f32 * 0.1) % 1.0; 128]);
+            trajectory.finalize(0.8, 1000);
+            bank.add_trajectory(&trajectory);
+        }
+        bank.extract_patterns();
+
+        let query = vec![0.5; 128];
+
+        b.iter(|| {
+            let similar = bank.find_similar(black_box(&query), 5);
+            black_box(similar);
+        });
+    });
+
+    // Pattern consolidation
+    group.bench_function("consolidate_patterns", |b| {
+        let config = PatternConfig {
+            k_clusters: 30,
+            embedding_dim: 128,
+            min_cluster_size: 2,
+            quality_threshold: 0.4,
+            ..Default::default()
+        };
+
+        let mut bank = ReasoningBank::new(config);
+
+        // Create many similar patterns
+        for i in 0..500 {
+            let mut trajectory = QueryTrajectory::new(i, vec![1.0 + (i as f32 * 0.001); 128]);
+            trajectory.finalize(0.8, 1000);
+            bank.add_trajectory(&trajectory);
+        }
+        bank.extract_patterns();
+
+        b.iter(|| {
+            let mut bank_clone = bank.clone();
+            bank_clone.consolidate(black_box(0.95));
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// EWC++ Benchmarks
+// ============================================================================
+
+fn ewc_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ewc_plus_plus");
+
+    // Fisher information update
+    for param_count in [256, 512, 1024] {
+        group.bench_with_input(
+            BenchmarkId::new("update_fisher", param_count),
+            &param_count,
+            |b, &count| {
+                let config = EwcConfig {
+                    param_count: count,
+                    ..Default::default()
+                };
+                let mut ewc = EwcPlusPlus::new(config);
+                let gradients = vec![0.1; count];
+
+                b.iter(|| {
+                    ewc.update_fisher(black_box(&gradients));
+                });
+            },
+        );
+    }
+
+    // Task boundary detection
+    group.bench_function("detect_boundary", |b| {
+        let config = EwcConfig {
+            param_count: 512,
+            gradient_history_size: 100,
+            ..Default::default()
+        };
+        let mut ewc = EwcPlusPlus::new(config);
+
+        // Build up history
+        for _ in 0..100 {
+            ewc.update_fisher(&vec![0.1; 512]);
+        }
+
+        let test_gradients = vec![0.15; 512];
+
+        b.iter(|| {
+            let is_boundary = ewc.detect_task_boundary(black_box(&test_gradients));
+            black_box(is_boundary);
+        });
+    });
+
+    // Apply constraints
+    for task_count in [1, 5, 10] {
+        group.bench_with_input(
+            BenchmarkId::new("apply_constraints", task_count),
+            &task_count,
+            |b, &tasks| {
+                let config = EwcConfig {
+                    param_count: 512,
+                    max_tasks: tasks,
+                    ..Default::default()
+                };
+                let mut ewc = EwcPlusPlus::new(config);
+
+                // Create multiple tasks
+                for _ in 0..tasks {
+                    for _ in 0..50 {
+                        ewc.update_fisher(&vec![0.1; 512]);
+                    }
+                    ewc.start_new_task();
+                }
+
+                let gradients = vec![0.5; 512];
+
+                b.iter(|| {
+                    let constrained = ewc.apply_constraints(black_box(&gradients));
+                    black_box(constrained);
+                });
+            },
+        );
+    }
+
+    // Regularization loss computation
+    group.bench_function("regularization_loss", |b| {
+        let config = EwcConfig {
+            param_count: 512,
+            max_tasks: 5,
+            initial_lambda: 1000.0,
+            ..Default::default()
+        };
+        let mut ewc = EwcPlusPlus::new(config);
+
+        // Create tasks
+        for _ in 0..5 {
+            ewc.set_optimal_weights(&vec![0.0; 512]);
+            for _ in 0..50 {
+                ewc.update_fisher(&vec![0.1; 512]);
+            }
+            ewc.start_new_task();
+        }
+
+        let current_weights = vec![0.1; 512];
+
+        b.iter(|| {
+            let loss = ewc.regularization_loss(black_box(&current_weights));
+            black_box(loss);
+        });
+    });
+
+    // Task consolidation
+    group.bench_function("consolidate_tasks", |b| {
+        let config = EwcConfig {
+            param_count: 512,
+            max_tasks: 10,
+            ..Default::default()
+        };
+
+        b.iter(|| {
+            let mut ewc = EwcPlusPlus::new(config.clone());
+
+            // Create 10 tasks
+            for _ in 0..10 {
+                for _ in 0..20 {
+                    ewc.update_fisher(&vec![0.1; 512]);
+                }
+                ewc.start_new_task();
+            }
+
+            ewc.consolidate_all_tasks();
+            black_box(ewc.task_count());
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Integrated Benchmarks (Complete SONA Cycles)
+// ============================================================================
+
+fn integrated_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("integrated");
+
+    // Complete instant learning cycle
+    group.bench_function("instant_loop_full_cycle", |b| {
+        let dim = 256;
+        let mut lora = MicroLoRA::new(dim, 1);
+        let buffer = TrajectoryBuffer::new(1000);
+        let id_gen = TrajectoryIdGen::new();
+
+        b.iter(|| {
+            // 1. Record trajectory (simulate 10 steps)
+            let mut builder = TrajectoryBuilder::new(id_gen.next(), vec![0.5; dim]);
+
+            for i in 0..10 {
+                builder.add_step(vec![0.3; dim], vec![0.2; 128], 0.7 + (i as f32 * 0.02));
+            }
+
+            let trajectory = builder.build(0.85);
+
+            // 2. Convert to learning signal
+            let signal = LearningSignal::from_trajectory(&trajectory);
+
+            // 3. Accumulate gradient
+            lora.accumulate_gradient(&signal);
+
+            // 4. Apply if batch ready (every 10 iterations in real use)
+            if lora.pending_updates() >= 10 {
+                lora.apply_accumulated(0.001);
+            }
+
+            // 5. Store trajectory
+            buffer.record(black_box(trajectory));
+        });
+    });
+
+    // Pattern-based learning cycle
+    group.bench_function("pattern_learning_cycle", |b| {
+        let config = PatternConfig {
+            k_clusters: 10,
+            embedding_dim: 128,
+            min_cluster_size: 3,
+            quality_threshold: 0.6,
+            ..Default::default()
+        };
+        let mut bank = ReasoningBank::new(config);
+
+        // Pre-populate with some trajectories
+        for i in 0..100 {
+            let mut trajectory = QueryTrajectory::new(i, vec![0.5; 128]);
+            trajectory.finalize(0.8, 1000);
+            bank.add_trajectory(&trajectory);
+        }
+
+        b.iter(|| {
+            // 1. Add new trajectory
+            let mut trajectory = QueryTrajectory::new(1000, vec![0.6; 128]);
+            trajectory.finalize(0.85, 1000);
+            bank.add_trajectory(&trajectory);
+
+            // 2. Extract patterns (would be done periodically)
+            if bank.trajectory_count() % 50 == 0 {
+                let patterns = bank.extract_patterns();
+                black_box(patterns);
+            }
+
+            // 3. Query similar patterns
+            let query = vec![0.6; 128];
+            let similar = bank.find_similar(&query, 3);
+            black_box(similar);
+        });
+    });
+
+    // EWC-protected learning
+    group.bench_function("ewc_protected_learning", |b| {
+        let param_count = 512;
+        let config = EwcConfig {
+            param_count,
+            max_tasks: 5,
+            initial_lambda: 1000.0,
+            ..Default::default()
+        };
+        let mut ewc = EwcPlusPlus::new(config);
+
+        // Setup with one completed task
+        ewc.set_optimal_weights(&vec![0.0; param_count]);
+        for _ in 0..50 {
+            ewc.update_fisher(&vec![0.1; param_count]);
+        }
+        ewc.start_new_task();
+
+        let mut lora = MicroLoRA::new(param_count, 1);
+
+        b.iter(|| {
+            // 1. Get raw gradients from learning signal
+            let signal =
+                LearningSignal::with_gradient(vec![0.5; param_count], vec![0.1; param_count], 0.8);
+
+            // 2. Apply EWC constraints
+            let constrained = ewc.apply_constraints(&signal.gradient_estimate);
+
+            // 3. Create constrained signal
+            let constrained_signal = LearningSignal::with_gradient(
+                signal.query_embedding.clone(),
+                constrained,
+                signal.quality_score,
+            );
+
+            // 4. Apply to LoRA
+            lora.accumulate_gradient(&constrained_signal);
+
+            // 5. Update Fisher
+            ewc.update_fisher(&signal.gradient_estimate);
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Learning Signal Benchmarks
+// ============================================================================
+
+fn learning_signal_benchmarks(c: &mut Criterion) {
+    let mut group = c.benchmark_group("learning_signal");
+
+    // Gradient estimation from trajectory
+    for step_count in [5, 10, 20] {
+        group.bench_with_input(
+            BenchmarkId::new("from_trajectory", step_count),
+            &step_count,
+            |b, &steps| {
+                let mut trajectory = QueryTrajectory::new(1, vec![0.5; 256]);
+
+                for i in 0..steps {
+                    trajectory.add_step(TrajectoryStep::new(
+                        vec![0.3; 256],
+                        vec![0.2; 128],
+                        0.7 + (i as f32 * 0.02),
+                        i,
+                    ));
+                }
+                trajectory.finalize(0.85, 1000);
+
+                b.iter(|| {
+                    let signal = LearningSignal::from_trajectory(black_box(&trajectory));
+                    black_box(signal);
+                });
+            },
+        );
+    }
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    micro_lora_benchmarks,
+    trajectory_benchmarks,
+    reasoning_bank_benchmarks,
+    ewc_benchmarks,
+    integrated_benchmarks,
+    learning_signal_benchmarks,
+);
+
+criterion_main!(benches);