d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
925 lines
28 KiB
Markdown
925 lines
28 KiB
Markdown
# Era 2: Self-Organizing Adaptive Indexes (2030-2035)
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## Autonomous Adaptation and Multi-Modal Unification
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### Executive Summary
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This document details the second era of HNSW evolution: transformation from static, manually-tuned structures into autonomous, self-organizing systems that continuously adapt to changing workloads, unify heterogeneous data modalities, and maintain knowledge through continual learning. Building on Era 1's neural augmentation, we introduce closed-loop control systems that eliminate human intervention.
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**Core Thesis**: Indexes should be living systems that sense their environment (workload patterns), make decisions (restructuring actions), and learn from experience (performance feedback).
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**Foundation**: Era 1's learned navigation and adaptive edge selection provide the building blocks for fully autonomous operation.
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---
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## 1. Autonomous Graph Restructuring
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### 1.1 From Static to Dynamic Topology
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**Problem**: Current HNSW graphs degrade over time
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- **Workload Shifts**: Query distribution changes → suboptimal structure
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- **Data Evolution**: New clusters emerge → old hubs become irrelevant
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- **Deletion Artifacts**: Tombstones fragment graph → disconnected regions
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**Vision**: Self-healing graphs that continuously optimize topology
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### 1.2 Control-Theoretic Framework
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**Model Predictive Control (MPC) for Graph Optimization**:
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```
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System State (s_t):
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s_t = (G_t, W_t, P_t, R_t)
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G_t: Graph structure at time t
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- Adjacency matrix A_t ∈ {0,1}^{N×N}
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- Layer assignments L_t ∈ {0,...,max_layer}^N
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- Node embeddings H_t ∈ ℝ^{N×d}
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W_t: Workload statistics
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- Query distribution Q_t(x)
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- Node visit frequencies V_t ∈ ℝ^N
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- Search path statistics (avg hops, bottlenecks)
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P_t: Performance metrics
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- Latency: p50, p95, p99
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- Recall@k across query types
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- Resource utilization (CPU, memory)
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R_t: Resource constraints
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- Memory budget B_mem
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- CPU budget B_cpu
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- Network bandwidth (distributed setting)
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Control Actions (u_t):
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u_t ∈ {AddEdge(i,j), RemoveEdge(i,j), PromoteLayer(i), DemoteLayer(i), Rewire(i)}
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Dynamics:
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s_{t+1} = f(s_t, u_t) + ω_t
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where ω_t = environmental noise (workload shifts)
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Objective:
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min E[Σ_{τ=t}^{t+H} γ^{τ-t} C(s_τ, u_τ)]
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Cost function C:
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C(s, u) = α₁ · Latency(s)
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+ α₂ · (1 - Recall(s))
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+ α₃ · Memory(s)
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+ α₄ · ActionCost(u)
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Horizon H = 10 steps (lookahead)
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Discount γ = 0.95
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```
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### 1.3 Implementation: Online Topology Optimizer
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```rust
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// File: /crates/ruvector-core/src/index/self_organizing.rs
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use ruvector_gnn::{RuvectorLayer, MultiHeadAttention};
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pub struct SelfOrganizingHNSW {
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graph: HnswGraph,
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optimizer: OnlineTopologyOptimizer,
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workload_analyzer: WorkloadAnalyzer,
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scheduler: AdaptiveRestructureScheduler,
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metrics_store: MetricsTimeSeries,
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}
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pub struct OnlineTopologyOptimizer {
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// Predictive models
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workload_predictor: LSTMPredictor, // Forecast W_{t+1:t+H}
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performance_model: GraphPerformanceGNN, // Estimate P(G, W)
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action_planner: MPCPlanner,
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// Learning components
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transition_model: WorldModel, // Learn f(s_t, u_t) → s_{t+1}
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optimizer: Adam,
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}
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impl OnlineTopologyOptimizer {
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/// Main optimization loop (runs in background thread)
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pub async fn autonomous_optimization_loop(
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&mut self,
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graph: Arc<RwLock<HnswGraph>>,
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metrics: Arc<RwLock<MetricsTimeSeries>>,
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) {
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loop {
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// 1. Observe current state
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let state = self.observe_state(&graph, &metrics).await;
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// 2. Detect degradation / opportunities
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let issues = self.detect_issues(&state);
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if !issues.is_empty() {
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// 3. Predict future workload
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let workload_forecast = self.workload_predictor.forecast(&state.workload, 10);
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// 4. Plan restructuring actions (MPC)
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let action_sequence = self.action_planner.plan(
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&state,
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&workload_forecast,
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&self.performance_model,
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&self.transition_model,
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);
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// 5. Execute first action (non-blocking)
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if let Some(action) = action_sequence.first() {
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self.execute_action(&graph, action).await;
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// 6. Update transition model (online learning)
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let next_state = self.observe_state(&graph, &metrics).await;
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self.transition_model.update(&state, action, &next_state);
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}
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}
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// 7. Adaptive sleep (more frequent if graph unstable)
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let sleep_duration = self.scheduler.next_interval(&state);
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tokio::time::sleep(sleep_duration).await;
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}
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}
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fn detect_issues(&self, state: &GraphState) -> Vec<TopologyIssue> {
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let mut issues = vec![];
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// Issue 1: Hot spots (nodes visited too frequently)
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let visit_mean = state.workload.node_visits.mean();
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let visit_std = state.workload.node_visits.std();
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for (node_id, visit_count) in state.workload.node_visits.iter() {
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if *visit_count > visit_mean + 3.0 * visit_std {
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issues.push(TopologyIssue::Hotspot {
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node_id: *node_id,
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severity: (*visit_count - visit_mean) / visit_std,
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});
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}
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}
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// Issue 2: Sparse regions (under-connected)
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for region in self.identify_regions(&state.graph) {
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if region.avg_degree < self.target_degree * 0.5 {
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issues.push(TopologyIssue::SparseRegion {
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region_id: region.id,
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avg_degree: region.avg_degree,
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});
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}
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}
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// Issue 3: Long search paths
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if state.metrics.avg_hops > state.metrics.theoretical_optimal * 1.5 {
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issues.push(TopologyIssue::LongPaths {
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avg_hops: state.metrics.avg_hops,
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optimal: state.metrics.theoretical_optimal,
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});
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}
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// Issue 4: Disconnected components (from deletions)
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let components = self.find_connected_components(&state.graph);
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if components.len() > 1 {
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issues.push(TopologyIssue::Disconnected {
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num_components: components.len(),
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sizes: components.iter().map(|c| c.len()).collect(),
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});
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}
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// Issue 5: Degraded recall
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if state.metrics.recall_at_10 < self.config.target_recall * 0.95 {
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issues.push(TopologyIssue::LowRecall {
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current: state.metrics.recall_at_10,
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target: self.config.target_recall,
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});
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}
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issues
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}
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}
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```
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### 1.4 Model Predictive Control Planner
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```rust
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pub struct MPCPlanner {
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horizon: usize, // H = lookahead steps
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action_budget: usize, // Max actions per planning cycle
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optimizer: CEMOptimizer, // Cross-Entropy Method for action sequence optimization
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}
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impl MPCPlanner {
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/// Plan optimal action sequence
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pub fn plan(
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&self,
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initial_state: &GraphState,
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workload_forecast: &[WorkloadDistribution],
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performance_model: &GraphPerformanceGNN,
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transition_model: &WorldModel,
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) -> Vec<RestructureAction> {
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// Cross-Entropy Method (CEM) for action sequence optimization
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let mut action_distribution = self.initialize_action_distribution();
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for iteration in 0..self.config.cem_iterations {
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// 1. Sample candidate action sequences
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let candidates: Vec<Vec<RestructureAction>> = (0..self.config.cem_samples)
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.map(|_| self.sample_action_sequence(&action_distribution))
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.collect();
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// 2. Evaluate each sequence via rollout
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let mut costs = vec![];
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for action_seq in &candidates {
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let cost = self.evaluate_action_sequence(
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initial_state,
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action_seq,
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workload_forecast,
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performance_model,
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transition_model,
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);
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costs.push(cost);
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}
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// 3. Select elite samples (lowest cost)
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let elite_indices = self.select_elite(&costs, 0.1); // Top 10%
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let elite_sequences: Vec<_> = elite_indices.iter()
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.map(|&i| &candidates[i])
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.collect();
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// 4. Update action distribution (fit to elite)
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action_distribution = self.fit_distribution(&elite_sequences);
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}
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// Return best action sequence found
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self.sample_action_sequence(&action_distribution)
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}
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fn evaluate_action_sequence(
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&self,
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initial_state: &GraphState,
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actions: &[RestructureAction],
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workload_forecast: &[WorkloadDistribution],
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performance_model: &GraphPerformanceGNN,
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transition_model: &WorldModel,
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) -> f32 {
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let mut state = initial_state.clone();
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let mut total_cost = 0.0;
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let gamma = 0.95;
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for (t, action) in actions.iter().enumerate().take(self.horizon) {
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// Predict next state
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state = transition_model.predict(&state, action);
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// Estimate performance on forecasted workload
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let workload = &workload_forecast[t.min(workload_forecast.len() - 1)];
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let performance = performance_model.estimate(&state.graph, workload);
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// Compute cost
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let cost = self.compute_cost(&performance, action);
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total_cost += gamma.powi(t as i32) * cost;
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}
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total_cost
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}
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fn compute_cost(&self, perf: &PerformanceEstimate, action: &RestructureAction) -> f32 {
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self.config.alpha_latency * perf.latency_p95 +
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self.config.alpha_recall * (1.0 - perf.recall_at_10) +
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self.config.alpha_memory * perf.memory_gb +
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self.config.alpha_action * action.cost()
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}
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}
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```
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### 1.5 World Model: Learning Graph Dynamics
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```rust
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pub struct WorldModel {
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// Predicts s_{t+1} given (s_t, u_t)
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state_encoder: GNN,
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action_encoder: nn::Embedding,
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transition_network: nn::Sequential,
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decoder: GraphDecoder,
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}
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impl WorldModel {
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/// Predict next state after action
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pub fn predict(&self, state: &GraphState, action: &RestructureAction) -> GraphState {
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// 1. Encode current graph
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let graph_encoding = self.state_encoder.forward(&state.graph); // [D]
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// 2. Encode action
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let action_encoding = self.action_encoder.forward(action); // [D_action]
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// 3. Predict state change
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let combined = Tensor::cat(&[graph_encoding, action_encoding], 0);
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let delta = self.transition_network.forward(&combined);
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// 4. Decode new graph
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let new_graph = self.decoder.forward(&delta);
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GraphState {
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graph: new_graph,
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workload: state.workload.clone(), // Workload changes separately
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metrics: self.estimate_metrics(&new_graph, &state.workload),
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}
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}
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/// Online update: learn from observed transition
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pub fn update(
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&mut self,
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state_t: &GraphState,
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action: &RestructureAction,
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state_t1: &GraphState,
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) {
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let predicted = self.predict(state_t, action);
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// Loss: MSE between predicted and observed state
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let loss = self.compute_state_loss(&predicted, state_t1);
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self.optimizer.zero_grad();
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loss.backward();
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self.optimizer.step();
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}
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}
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```
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### 1.6 Self-Healing from Deletions
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**Problem**: Tombstone-based deletion creates fragmentation
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**Solution**: Active healing process
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```rust
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impl SelfOrganizingHNSW {
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/// Detect and repair graph fragmentation
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pub async fn heal_deletions(&mut self) {
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let tombstones = self.graph.get_tombstone_nodes();
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if tombstones.len() > self.graph.len() * 0.1 { // >10% tombstones
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// Find connected components
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let components = self.find_connected_components();
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if components.len() > 1 {
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// Reconnect isolated components
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for component in &components[1..] { // Skip largest component
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let bridge_edges = self.find_bridge_edges(
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component,
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&components[0],
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);
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for (src, dst) in bridge_edges {
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self.graph.add_edge(src, dst);
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}
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}
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}
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// Compact: remove tombstones, rebuild index
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self.graph.compact_and_rebuild();
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}
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}
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fn find_bridge_edges(
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&self,
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isolated_component: &[usize],
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main_component: &[usize],
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) -> Vec<(usize, usize)> {
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// Find closest pairs between components
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let mut bridges = vec![];
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for &node_i in isolated_component {
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let embedding_i = &self.graph.embeddings[node_i];
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let closest_in_main = main_component.iter()
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.min_by_key(|&&node_j| {
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let embedding_j = &self.graph.embeddings[node_j];
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NotNan::new(distance(embedding_i, embedding_j)).unwrap()
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})
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.unwrap();
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bridges.push((node_i, *closest_in_main));
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}
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bridges
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}
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}
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```
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### 1.7 Expected Performance
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**Adaptive vs. Static** (1M vector dataset, 30-day operation):
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| Metric | Static HNSW | Self-Organizing | Improvement |
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|--------|-------------|-----------------|-------------|
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| Initial Latency (p95) | 1.2 ms | 1.2 ms | 0% |
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| Day 30 Latency (p95) | 2.8 ms (+133%) | 1.5 ms (+25%) | **87% degradation prevented** |
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| Workload Shift Adaptation | Manual (hours) | Automatic (5-10 min) | **30-60x faster** |
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| Deletion Fragmentation | 15% disconnected | 0% (self-healed) | **100% resolved** |
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| Memory Overhead | Baseline | +5% (world model) | Acceptable |
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---
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## 2. Multi-Modal HNSW
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### 2.1 Unified Index for Heterogeneous Data
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**Vision**: Single graph indexes text, images, audio, video, code
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**Challenges**:
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1. **Embedding Spaces**: Different modalities → different geometries
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2. **Search Strategies**: Text needs BM25-like, images need visual similarity
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3. **Cross-Modal Retrieval**: Query text, retrieve images
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### 2.2 Architecture
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```rust
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pub struct MultiModalHNSW {
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// Shared graph structure
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shared_graph: HnswGraph,
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// Modality-specific encoders
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encoders: HashMap<Modality, Box<dyn ModalityEncoder>>,
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// Cross-modal fusion
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fusion_network: CrossModalFusion,
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// Modality-aware routing
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routers: HashMap<Modality, ModalityRouter>,
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}
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#[derive(Hash, Eq, PartialEq, Clone, Copy)]
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pub enum Modality {
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Text,
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Image,
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Audio,
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Video,
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Code,
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Graph, // For knowledge graphs
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}
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pub trait ModalityEncoder: Send + Sync {
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/// Encode raw data into embedding
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fn encode(&self, data: &[u8]) -> Result<Vec<f32>>;
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/// Dimensionality of embeddings
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fn dim(&self) -> usize;
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}
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```
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### 2.3 Shared Embedding Space via Contrastive Learning
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**CLIP-Style Multi-Modal Alignment**:
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```
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Training Data: Aligned pairs {(x_A^i, x_B^i)}_{i=1}^N
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e.g., (image, caption), (audio, transcript), (code, docstring)
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Encoders:
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h_text = f_text(x_text; θ_text)
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h_image = f_image(x_image; θ_image)
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h_audio = f_audio(x_audio; θ_audio)
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...
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Projection to Shared Space:
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z_text = W_text · h_text
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z_image = W_image · h_image
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...
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Contrastive Loss (InfoNCE):
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L = -Σ_i log(exp(sim(z_i^A, z_i^B) / τ) / Σ_j exp(sim(z_i^A, z_j^B) / τ))
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Pushes matched pairs together, unmatched pairs apart
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Symmetrized:
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L_total = L(A→B) + L(B→A)
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```
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**Implementation**:
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```rust
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pub struct CrossModalFusion {
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projections: HashMap<Modality, nn::Linear>,
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temperature: f32,
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}
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impl CrossModalFusion {
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/// Project modality-specific embedding to shared space
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pub fn project(&self, embedding: &[f32], modality: Modality) -> Vec<f32> {
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let projection = &self.projections[&modality];
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let tensor = Tensor::of_slice(embedding);
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let projected = projection.forward(&tensor);
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// L2 normalize for cosine similarity
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let norm = projected.norm();
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(projected / norm).into()
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}
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/// Fuse multiple modalities (e.g., video = visual + audio)
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pub fn fuse(&self, modal_embeddings: &[(Modality, Vec<f32>)]) -> Vec<f32> {
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if modal_embeddings.len() == 1 {
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return modal_embeddings[0].1.clone();
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}
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// Project all to shared space
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let projected: Vec<_> = modal_embeddings.iter()
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.map(|(mod_type, emb)| self.project(emb, *mod_type))
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.collect();
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// Average (can use weighted average or attention)
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let dim = projected[0].len();
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let mut fused = vec![0.0; dim];
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for emb in &projected {
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for (i, &val) in emb.iter().enumerate() {
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fused[i] += val;
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}
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}
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for val in &mut fused {
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*val /= projected.len() as f32;
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||
}
|
||
|
||
// Re-normalize
|
||
let norm: f32 = fused.iter().map(|x| x * x).sum::<f32>().sqrt();
|
||
fused.iter().map(|x| x / norm).collect()
|
||
}
|
||
}
|
||
```
|
||
|
||
### 2.4 Modality-Aware Navigation
|
||
|
||
**Insight**: Different modalities cluster differently in shared space
|
||
**Solution**: Learn modality-specific routing policies
|
||
|
||
```rust
|
||
pub struct ModalityRouter {
|
||
modality: Modality,
|
||
route_predictor: nn::Sequential,
|
||
}
|
||
|
||
impl ModalityRouter {
|
||
/// Navigate graph with modality-aware strategy
|
||
pub fn search(
|
||
&self,
|
||
query_embedding: &[f32],
|
||
graph: &HnswGraph,
|
||
k: usize,
|
||
) -> Vec<SearchResult> {
|
||
// Use learned routing specific to this modality
|
||
let mut current = graph.entry_point();
|
||
let mut visited = HashSet::new();
|
||
let mut candidates = BinaryHeap::new();
|
||
|
||
for _ in 0..self.max_hops {
|
||
visited.insert(current);
|
||
|
||
// Modality-specific routing decision
|
||
let neighbors = graph.neighbors(current);
|
||
let next = self.select_next_node(
|
||
query_embedding,
|
||
current,
|
||
&neighbors,
|
||
&graph,
|
||
);
|
||
|
||
if visited.contains(&next) {
|
||
break; // Converged
|
||
}
|
||
|
||
current = next;
|
||
candidates.push(SearchResult {
|
||
id: current,
|
||
score: cosine_similarity(query_embedding, &graph.embeddings[current]),
|
||
});
|
||
}
|
||
|
||
// Return top-k
|
||
candidates.into_sorted_vec()
|
||
.into_iter()
|
||
.take(k)
|
||
.collect()
|
||
}
|
||
|
||
fn select_next_node(
|
||
&self,
|
||
query: &[f32],
|
||
current: usize,
|
||
neighbors: &[usize],
|
||
graph: &HnswGraph,
|
||
) -> usize {
|
||
// Features for routing decision
|
||
let features = self.extract_routing_features(query, current, neighbors, graph);
|
||
|
||
// Predict best next node
|
||
let scores = self.route_predictor.forward(&features); // [num_neighbors]
|
||
let best_idx = scores.argmax(0).int64_value(&[]) as usize;
|
||
neighbors[best_idx]
|
||
}
|
||
}
|
||
```
|
||
|
||
### 2.5 Cross-Modal Search Examples
|
||
|
||
**Text → Image Retrieval**:
|
||
```rust
|
||
let query_text = "sunset over ocean";
|
||
let query_embed = mm_index.encode(query_text, Modality::Text);
|
||
|
||
// Search for images
|
||
let results = mm_index.cross_modal_search(
|
||
&query_embed,
|
||
Modality::Text, // Query modality
|
||
&[Modality::Image], // Target modality
|
||
10,
|
||
);
|
||
|
||
// Returns top-10 images matching text query
|
||
```
|
||
|
||
**Video → Text+Audio Retrieval**:
|
||
```rust
|
||
let video_frames = load_video("input.mp4");
|
||
let video_embed = mm_index.encode_video(&video_frames);
|
||
|
||
let results = mm_index.cross_modal_search(
|
||
&video_embed,
|
||
Modality::Video,
|
||
&[Modality::Text, Modality::Audio],
|
||
20,
|
||
);
|
||
```
|
||
|
||
### 2.6 Expected Performance
|
||
|
||
**Multi-Modal Benchmarks** (MS-COCO, Flickr30k):
|
||
|
||
| Task | Separate Indexes | Multi-Modal Index | Benefit |
|
||
|------|------------------|-------------------|---------|
|
||
| Text→Image (Recall@10) | 0.712 | 0.728 (+2.2%) | Better alignment |
|
||
| Image→Text (Recall@10) | 0.689 | 0.705 (+2.3%) | Better alignment |
|
||
| Memory (1M items) | 5 × 4 GB = 20 GB | 8 GB | **60% reduction** |
|
||
| Search Time | 5 × 1.2ms = 6ms | 1.8ms | **70% faster** |
|
||
|
||
---
|
||
|
||
## 3. Continuous Learning Index
|
||
|
||
### 3.1 Never-Ending Learning Without Forgetting
|
||
|
||
**Goal**: Learn from streaming data while preserving performance on old tasks
|
||
|
||
**Techniques** (already in RuVector!):
|
||
- **EWC** (`/crates/ruvector-gnn/src/ewc.rs`)
|
||
- **Replay Buffer** (`/crates/ruvector-gnn/src/replay.rs`)
|
||
|
||
**Novel Addition**: Knowledge Distillation + Sleep Consolidation
|
||
|
||
### 3.2 Teacher-Student Knowledge Distillation
|
||
|
||
```rust
|
||
pub struct TeacherStudentFramework {
|
||
teacher: HnswGraph, // Frozen snapshot
|
||
student: HnswGraph, // Being updated
|
||
distillation_temperature: f32,
|
||
}
|
||
|
||
impl TeacherStudentFramework {
|
||
/// Compute distillation loss: preserve teacher's knowledge
|
||
pub fn distill_loss(&self, queries: &[Vec<f32>]) -> f32 {
|
||
let mut total_loss = 0.0;
|
||
|
||
for query in queries {
|
||
// Teacher predictions (soft targets)
|
||
let teacher_scores = self.teacher.search_with_scores(query, 100);
|
||
let teacher_probs = softmax(&teacher_scores, self.distillation_temperature);
|
||
|
||
// Student predictions
|
||
let student_scores = self.student.search_with_scores(query, 100);
|
||
let student_probs = softmax(&student_scores, self.distillation_temperature);
|
||
|
||
// KL divergence: match teacher distribution
|
||
let kl_loss: f32 = teacher_probs.iter()
|
||
.zip(student_probs.iter())
|
||
.map(|(p_t, p_s)| {
|
||
if *p_t > 0.0 {
|
||
p_t * (p_t.ln() - p_s.ln())
|
||
} else {
|
||
0.0
|
||
}
|
||
})
|
||
.sum();
|
||
|
||
total_loss += kl_loss;
|
||
}
|
||
|
||
total_loss / queries.len() as f32
|
||
}
|
||
}
|
||
|
||
fn softmax(scores: &[f32], temperature: f32) -> Vec<f32> {
|
||
let max_score = scores.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
|
||
let exp_scores: Vec<f32> = scores.iter()
|
||
.map(|s| ((s - max_score) / temperature).exp())
|
||
.collect();
|
||
let sum: f32 = exp_scores.iter().sum();
|
||
exp_scores.iter().map(|e| e / sum).collect()
|
||
}
|
||
```
|
||
|
||
### 3.3 Sleep Consolidation
|
||
|
||
**Biological Inspiration**: Hippocampus → Neocortex consolidation during sleep
|
||
|
||
```rust
|
||
pub struct SleepConsolidation {
|
||
replay_buffer: ReplayBuffer,
|
||
consolidation_network: GNN,
|
||
}
|
||
|
||
impl SleepConsolidation {
|
||
/// Offline consolidation: replay experiences, extract patterns
|
||
pub fn consolidate(&mut self, graph: &mut HnswGraph) -> Result<()> {
|
||
// 1. Sample diverse experiences from replay buffer
|
||
let experiences = self.replay_buffer.sample_diverse(10000);
|
||
|
||
// 2. Cluster experiences into patterns
|
||
let patterns = self.discover_patterns(&experiences);
|
||
|
||
// 3. For each pattern, strengthen relevant graph structure
|
||
for pattern in patterns {
|
||
self.strengthen_pattern(graph, &pattern)?;
|
||
}
|
||
|
||
// 4. Prune weak edges
|
||
self.prune_weak_edges(graph, 0.1); // Remove bottom 10%
|
||
|
||
Ok(())
|
||
}
|
||
|
||
fn discover_patterns(&self, experiences: &[Experience]) -> Vec<Pattern> {
|
||
// Extract common search paths, frequent co-occurrences
|
||
let path_frequencies = self.count_path_frequencies(experiences);
|
||
|
||
// Cluster similar paths
|
||
let patterns = self.cluster_paths(&path_frequencies, 100); // 100 patterns
|
||
patterns
|
||
}
|
||
|
||
fn strengthen_pattern(&self, graph: &mut HnswGraph, pattern: &Pattern) {
|
||
// For edges in this pattern, increase weight
|
||
for (node_i, node_j) in &pattern.edges {
|
||
if let Some(weight) = graph.get_edge_weight(*node_i, *node_j) {
|
||
graph.set_edge_weight(*node_i, *node_j, weight * 1.1); // 10% boost
|
||
} else {
|
||
graph.add_edge(*node_i, *node_j); // Create if doesn't exist
|
||
}
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
### 3.4 Full Continual Learning Pipeline
|
||
|
||
```rust
|
||
pub struct ContinualHNSW {
|
||
index: HnswGraph,
|
||
|
||
// Forgetting mitigation
|
||
ewc: ElasticWeightConsolidation,
|
||
replay_buffer: ReplayBuffer,
|
||
distillation: TeacherStudentFramework,
|
||
consolidation: SleepConsolidation,
|
||
|
||
// Learning schedule
|
||
task_id: usize,
|
||
samples_seen: usize,
|
||
}
|
||
|
||
impl ContinualHNSW {
|
||
/// Learn new data distribution without forgetting
|
||
pub fn learn_incremental(
|
||
&mut self,
|
||
new_data: &[(VectorId, Vec<f32>)],
|
||
) -> Result<()> {
|
||
// 0. Before learning: snapshot teacher, compute Fisher
|
||
let teacher = self.index.clone();
|
||
self.ewc.compute_fisher_information(&self.index)?;
|
||
|
||
// 1. Sample replay data
|
||
let replay_samples = self.replay_buffer.sample(1024);
|
||
|
||
// 2. Train on new + replay data
|
||
for epoch in 0..self.config.epochs {
|
||
for batch in new_data.chunks(64) {
|
||
// Loss components
|
||
let new_loss = self.task_loss(batch);
|
||
let replay_loss = self.task_loss(&replay_samples);
|
||
let ewc_penalty = self.ewc.compute_penalty(&self.index);
|
||
let distill_loss = self.distillation.distill_loss(&batch);
|
||
|
||
let total_loss = new_loss
|
||
+ 0.5 * replay_loss
|
||
+ 0.1 * ewc_penalty
|
||
+ 0.3 * distill_loss;
|
||
|
||
// Backprop
|
||
total_loss.backward();
|
||
self.optimizer.step();
|
||
}
|
||
}
|
||
|
||
// 3. Add new data to replay buffer
|
||
self.replay_buffer.add_batch(new_data);
|
||
|
||
// 4. Periodic consolidation (every 10 tasks or 100k samples)
|
||
if self.task_id % 10 == 0 || self.samples_seen > 100_000 {
|
||
self.consolidation.consolidate(&mut self.index)?;
|
||
self.samples_seen = 0;
|
||
}
|
||
|
||
self.task_id += 1;
|
||
Ok(())
|
||
}
|
||
}
|
||
```
|
||
|
||
### 3.5 Expected Performance
|
||
|
||
**Continual Learning Benchmark** (10 sequential tasks):
|
||
|
||
| Method | Final Avg Accuracy | Forgetting | Training Time |
|
||
|--------|-------------------|------------|---------------|
|
||
| Naive (no mitigation) | 0.523 | 0.412 | 1x |
|
||
| EWC only | 0.687 | 0.231 | 1.2x |
|
||
| EWC + Replay | 0.754 | 0.142 | 1.5x |
|
||
| **Full Pipeline** (EWC+Replay+Distill+Consolidation) | **0.823** | **0.067** | 1.8x |
|
||
|
||
**Forgetting** = Average drop in accuracy on old tasks
|
||
|
||
---
|
||
|
||
## 4. Distributed HNSW Evolution
|
||
|
||
### 4.1 Federated Graph Learning
|
||
|
||
**Scenario**: Multiple data centers, privacy constraints
|
||
|
||
```rust
|
||
pub struct FederatedHNSW {
|
||
local_graphs: Vec<HnswGraph>, // One per site
|
||
global_aggregator: FederatedAggregator,
|
||
communication_protocol: SecureAggregation,
|
||
}
|
||
|
||
impl FederatedHNSW {
|
||
/// Federated learning round
|
||
pub async fn federated_round(&mut self) {
|
||
// 1. Each site trains locally
|
||
let local_updates = stream::iter(&mut self.local_graphs)
|
||
.then(|graph| async {
|
||
graph.train_local_epoch().await
|
||
})
|
||
.collect::<Vec<_>>()
|
||
.await;
|
||
|
||
// 2. Secure aggregation (privacy-preserving)
|
||
let global_update = self.communication_protocol
|
||
.aggregate(&local_updates)
|
||
.await;
|
||
|
||
// 3. Broadcast to all sites
|
||
for graph in &mut self.local_graphs {
|
||
graph.apply_global_update(&global_update).await;
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
---
|
||
|
||
## 5. Integration Timeline
|
||
|
||
### Year 2030-2031: Foundations
|
||
- [ ] MPC optimizer implementation
|
||
- [ ] World model training
|
||
- [ ] Self-healing from deletions
|
||
|
||
### Year 2031-2032: Multi-Modal
|
||
- [ ] CLIP-style multi-modal training
|
||
- [ ] Modality-specific routers
|
||
- [ ] Cross-modal search API
|
||
|
||
### Year 2032-2033: Continual Learning
|
||
- [ ] Knowledge distillation integration
|
||
- [ ] Sleep consolidation
|
||
- [ ] Benchmark on continual learning datasets
|
||
|
||
### Year 2033-2035: Distributed
|
||
- [ ] Federated learning protocol
|
||
- [ ] Consensus-based topology updates
|
||
- [ ] Production deployment
|
||
|
||
---
|
||
|
||
## References
|
||
|
||
1. **MPC**: Camacho & Alba (2013) - "Model Predictive Control"
|
||
2. **CLIP**: Radford et al. (2021) - "Learning Transferable Visual Models From Natural Language Supervision"
|
||
3. **Continual Learning**: Kirkpatrick et al. (2017) - "Overcoming catastrophic forgetting"
|
||
4. **Federated Learning**: McMahan et al. (2017) - "Communication-Efficient Learning"
|
||
|
||
---
|
||
|
||
**Document Version**: 1.0
|
||
**Last Updated**: 2025-11-30
|