docs(adr-018): Add ESP32 development implementation ADR

Documents the concrete 4-layer development sequence for closing the
hardware gap: firmware (ESP-IDF C), UDP aggregator (Rust), CsiFrame→CsiData
bridge, and Python _read_raw_data() UDP socket replacement. Builds on
ADR-012 architecture and existing wifi-densepose-hardware parser crate.
Includes testability path for all layers before hardware acquisition.

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4

.claude-flow/CAPABILITIES.md

@@ -0,0 +1,403 @@
+# Claude Flow V3 - Complete Capabilities Reference
+> Generated: 2026-02-28T16:04:10.839Z
+> Full documentation: https://github.com/ruvnet/claude-flow
+
+## 📋 Table of Contents
+
+1. [Overview](#overview)
+2. [Swarm Orchestration](#swarm-orchestration)
+3. [Available Agents (60+)](#available-agents)
+4. [CLI Commands (26 Commands, 140+ Subcommands)](#cli-commands)
+5. [Hooks System (27 Hooks + 12 Workers)](#hooks-system)
+6. [Memory & Intelligence (RuVector)](#memory--intelligence)
+7. [Hive-Mind Consensus](#hive-mind-consensus)
+8. [Performance Targets](#performance-targets)
+9. [Integration Ecosystem](#integration-ecosystem)
+
+---
+
+## Overview
+
+Claude Flow V3 is a domain-driven design architecture for multi-agent AI coordination with:
+
+- **15-Agent Swarm Coordination** with hierarchical and mesh topologies
+- **HNSW Vector Search** - 150x-12,500x faster pattern retrieval
+- **SONA Neural Learning** - Self-optimizing with <0.05ms adaptation
+- **Byzantine Fault Tolerance** - Queen-led consensus mechanisms
+- **MCP Server Integration** - Model Context Protocol support
+
+### Current Configuration
+| Setting | Value |
+|---------|-------|
+| Topology | hierarchical-mesh |
+| Max Agents | 15 |
+| Memory Backend | hybrid |
+| HNSW Indexing | Enabled |
+| Neural Learning | Enabled |
+| LearningBridge | Enabled (SONA + ReasoningBank) |
+| Knowledge Graph | Enabled (PageRank + Communities) |
+| Agent Scopes | Enabled (project/local/user) |
+
+---
+
+## Swarm Orchestration
+
+### Topologies
+| Topology | Description | Best For |
+|----------|-------------|----------|
+| `hierarchical` | Queen controls workers directly | Anti-drift, tight control |
+| `mesh` | Fully connected peer network | Distributed tasks |
+| `hierarchical-mesh` | V3 hybrid (recommended) | 10+ agents |
+| `ring` | Circular communication | Sequential workflows |
+| `star` | Central coordinator | Simple coordination |
+| `adaptive` | Dynamic based on load | Variable workloads |
+
+### Strategies
+- `balanced` - Even distribution across agents
+- `specialized` - Clear roles, no overlap (anti-drift)
+- `adaptive` - Dynamic task routing
+
+### Quick Commands
+```bash
+# Initialize swarm
+npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized
+
+# Check status
+npx @claude-flow/cli@latest swarm status
+
+# Monitor activity
+npx @claude-flow/cli@latest swarm monitor
+```
+
+---
+
+## Available Agents
+
+### Core Development (5)
+`coder`, `reviewer`, `tester`, `planner`, `researcher`
+
+### V3 Specialized (4)
+`security-architect`, `security-auditor`, `memory-specialist`, `performance-engineer`
+
+### Swarm Coordination (5)
+`hierarchical-coordinator`, `mesh-coordinator`, `adaptive-coordinator`, `collective-intelligence-coordinator`, `swarm-memory-manager`
+
+### Consensus & Distributed (7)
+`byzantine-coordinator`, `raft-manager`, `gossip-coordinator`, `consensus-builder`, `crdt-synchronizer`, `quorum-manager`, `security-manager`
+
+### Performance & Optimization (5)
+`perf-analyzer`, `performance-benchmarker`, `task-orchestrator`, `memory-coordinator`, `smart-agent`
+
+### GitHub & Repository (9)
+`github-modes`, `pr-manager`, `code-review-swarm`, `issue-tracker`, `release-manager`, `workflow-automation`, `project-board-sync`, `repo-architect`, `multi-repo-swarm`
+
+### SPARC Methodology (6)
+`sparc-coord`, `sparc-coder`, `specification`, `pseudocode`, `architecture`, `refinement`
+
+### Specialized Development (8)
+`backend-dev`, `mobile-dev`, `ml-developer`, `cicd-engineer`, `api-docs`, `system-architect`, `code-analyzer`, `base-template-generator`
+
+### Testing & Validation (2)
+`tdd-london-swarm`, `production-validator`
+
+### Agent Routing by Task
+| Task Type | Recommended Agents | Topology |
+|-----------|-------------------|----------|
+| Bug Fix | researcher, coder, tester | mesh |
+| New Feature | coordinator, architect, coder, tester, reviewer | hierarchical |
+| Refactoring | architect, coder, reviewer | mesh |
+| Performance | researcher, perf-engineer, coder | hierarchical |
+| Security | security-architect, auditor, reviewer | hierarchical |
+| Docs | researcher, api-docs | mesh |
+
+---
+
+## CLI Commands
+
+### Core Commands (12)
+| Command | Subcommands | Description |
+|---------|-------------|-------------|
+| `init` | 4 | Project initialization |
+| `agent` | 8 | Agent lifecycle management |
+| `swarm` | 6 | Multi-agent coordination |
+| `memory` | 11 | AgentDB with HNSW search |
+| `mcp` | 9 | MCP server management |
+| `task` | 6 | Task assignment |
+| `session` | 7 | Session persistence |
+| `config` | 7 | Configuration |
+| `status` | 3 | System monitoring |
+| `workflow` | 6 | Workflow templates |
+| `hooks` | 17 | Self-learning hooks |
+| `hive-mind` | 6 | Consensus coordination |
+
+### Advanced Commands (14)
+| Command | Subcommands | Description |
+|---------|-------------|-------------|
+| `daemon` | 5 | Background workers |
+| `neural` | 5 | Pattern training |
+| `security` | 6 | Security scanning |
+| `performance` | 5 | Profiling & benchmarks |
+| `providers` | 5 | AI provider config |
+| `plugins` | 5 | Plugin management |
+| `deployment` | 5 | Deploy management |
+| `embeddings` | 4 | Vector embeddings |
+| `claims` | 4 | Authorization |
+| `migrate` | 5 | V2→V3 migration |
+| `process` | 4 | Process management |
+| `doctor` | 1 | Health diagnostics |
+| `completions` | 4 | Shell completions |
+
+### Example Commands
+```bash
+# Initialize
+npx @claude-flow/cli@latest init --wizard
+
+# Spawn agent
+npx @claude-flow/cli@latest agent spawn -t coder --name my-coder
+
+# Memory operations
+npx @claude-flow/cli@latest memory store --key "pattern" --value "data" --namespace patterns
+npx @claude-flow/cli@latest memory search --query "authentication"
+
+# Diagnostics
+npx @claude-flow/cli@latest doctor --fix
+```
+
+---
+
+## Hooks System
+
+### 27 Available Hooks
+
+#### Core Hooks (6)
+| Hook | Description |
+|------|-------------|
+| `pre-edit` | Context before file edits |
+| `post-edit` | Record edit outcomes |
+| `pre-command` | Risk assessment |
+| `post-command` | Command metrics |
+| `pre-task` | Task start + agent suggestions |
+| `post-task` | Task completion learning |
+
+#### Session Hooks (4)
+| Hook | Description |
+|------|-------------|
+| `session-start` | Start/restore session |
+| `session-end` | Persist state |
+| `session-restore` | Restore previous |
+| `notify` | Cross-agent notifications |
+
+#### Intelligence Hooks (5)
+| Hook | Description |
+|------|-------------|
+| `route` | Optimal agent routing |
+| `explain` | Routing decisions |
+| `pretrain` | Bootstrap intelligence |
+| `build-agents` | Generate configs |
+| `transfer` | Pattern transfer |
+
+#### Coverage Hooks (3)
+| Hook | Description |
+|------|-------------|
+| `coverage-route` | Coverage-based routing |
+| `coverage-suggest` | Improvement suggestions |
+| `coverage-gaps` | Gap analysis |
+
+### 12 Background Workers
+| Worker | Priority | Purpose |
+|--------|----------|---------|
+| `ultralearn` | normal | Deep knowledge |
+| `optimize` | high | Performance |
+| `consolidate` | low | Memory consolidation |
+| `predict` | normal | Predictive preload |
+| `audit` | critical | Security |
+| `map` | normal | Codebase mapping |
+| `preload` | low | Resource preload |
+| `deepdive` | normal | Deep analysis |
+| `document` | normal | Auto-docs |
+| `refactor` | normal | Suggestions |
+| `benchmark` | normal | Benchmarking |
+| `testgaps` | normal | Coverage gaps |
+
+---
+
+## Memory & Intelligence
+
+### RuVector Intelligence System
+- **SONA**: Self-Optimizing Neural Architecture (<0.05ms)
+- **MoE**: Mixture of Experts routing
+- **HNSW**: 150x-12,500x faster search
+- **EWC++**: Prevents catastrophic forgetting
+- **Flash Attention**: 2.49x-7.47x speedup
+- **Int8 Quantization**: 3.92x memory reduction
+
+### 4-Step Intelligence Pipeline
+1. **RETRIEVE** - HNSW pattern search
+2. **JUDGE** - Success/failure verdicts
+3. **DISTILL** - LoRA learning extraction
+4. **CONSOLIDATE** - EWC++ preservation
+
+### Self-Learning Memory (ADR-049)
+
+| Component | Status | Description |
+|-----------|--------|-------------|
+| **LearningBridge** | ✅ Enabled | Connects insights to SONA/ReasoningBank neural pipeline |
+| **MemoryGraph** | ✅ Enabled | PageRank knowledge graph + community detection |
+| **AgentMemoryScope** | ✅ Enabled | 3-scope agent memory (project/local/user) |
+
+**LearningBridge** - Insights trigger learning trajectories. Confidence evolves: +0.03 on access, -0.005/hour decay. Consolidation runs the JUDGE/DISTILL/CONSOLIDATE pipeline.
+
+**MemoryGraph** - Builds a knowledge graph from entry references. PageRank identifies influential insights. Communities group related knowledge. Graph-aware ranking blends vector + structural scores.
+
+**AgentMemoryScope** - Maps Claude Code 3-scope directories:
+- `project`: `<gitRoot>/.claude/agent-memory/<agent>/`
+- `local`: `<gitRoot>/.claude/agent-memory-local/<agent>/`
+- `user`: `~/.claude/agent-memory/<agent>/`
+
+High-confidence insights (>0.8) can transfer between agents.
+
+### Memory Commands
+```bash
+# Store pattern
+npx @claude-flow/cli@latest memory store --key "name" --value "data" --namespace patterns
+
+# Semantic search
+npx @claude-flow/cli@latest memory search --query "authentication"
+
+# List entries
+npx @claude-flow/cli@latest memory list --namespace patterns
+
+# Initialize database
+npx @claude-flow/cli@latest memory init --force
+```
+
+---
+
+## Hive-Mind Consensus
+
+### Queen Types
+| Type | Role |
+|------|------|
+| Strategic Queen | Long-term planning |
+| Tactical Queen | Execution coordination |
+| Adaptive Queen | Dynamic optimization |
+
+### Worker Types (8)
+`researcher`, `coder`, `analyst`, `tester`, `architect`, `reviewer`, `optimizer`, `documenter`
+
+### Consensus Mechanisms
+| Mechanism | Fault Tolerance | Use Case |
+|-----------|-----------------|----------|
+| `byzantine` | f < n/3 faulty | Adversarial |
+| `raft` | f < n/2 failed | Leader-based |
+| `gossip` | Eventually consistent | Large scale |
+| `crdt` | Conflict-free | Distributed |
+| `quorum` | Configurable | Flexible |
+
+### Hive-Mind Commands
+```bash
+# Initialize
+npx @claude-flow/cli@latest hive-mind init --queen-type strategic
+
+# Status
+npx @claude-flow/cli@latest hive-mind status
+
+# Spawn workers
+npx @claude-flow/cli@latest hive-mind spawn --count 5 --type worker
+
+# Consensus
+npx @claude-flow/cli@latest hive-mind consensus --propose "task"
+```
+
+---
+
+## Performance Targets
+
+| Metric | Target | Status |
+|--------|--------|--------|
+| HNSW Search | 150x-12,500x faster | ✅ Implemented |
+| Memory Reduction | 50-75% | ✅ Implemented (3.92x) |
+| SONA Integration | Pattern learning | ✅ Implemented |
+| Flash Attention | 2.49x-7.47x | 🔄 In Progress |
+| MCP Response | <100ms | ✅ Achieved |
+| CLI Startup | <500ms | ✅ Achieved |
+| SONA Adaptation | <0.05ms | 🔄 In Progress |
+| Graph Build (1k) | <200ms | ✅ 2.78ms (71.9x headroom) |
+| PageRank (1k) | <100ms | ✅ 12.21ms (8.2x headroom) |
+| Insight Recording | <5ms/each | ✅ 0.12ms (41x headroom) |
+| Consolidation | <500ms | ✅ 0.26ms (1,955x headroom) |
+| Knowledge Transfer | <100ms | ✅ 1.25ms (80x headroom) |
+
+---
+
+## Integration Ecosystem
+
+### Integrated Packages
+| Package | Version | Purpose |
+|---------|---------|---------|
+| agentic-flow | 3.0.0-alpha.1 | Core coordination + ReasoningBank + Router |
+| agentdb | 3.0.0-alpha.10 | Vector database + 8 controllers |
+| @ruvector/attention | 0.1.3 | Flash attention |
+| @ruvector/sona | 0.1.5 | Neural learning |
+
+### Optional Integrations
+| Package | Command |
+|---------|---------|
+| ruv-swarm | `npx ruv-swarm mcp start` |
+| flow-nexus | `npx flow-nexus@latest mcp start` |
+| agentic-jujutsu | `npx agentic-jujutsu@latest` |
+
+### MCP Server Setup
+```bash
+# Add Claude Flow MCP
+claude mcp add claude-flow -- npx -y @claude-flow/cli@latest
+
+# Optional servers
+claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start
+claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start
+```
+
+---
+
+## Quick Reference
+
+### Essential Commands
+```bash
+# Setup
+npx @claude-flow/cli@latest init --wizard
+npx @claude-flow/cli@latest daemon start
+npx @claude-flow/cli@latest doctor --fix
+
+# Swarm
+npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8
+npx @claude-flow/cli@latest swarm status
+
+# Agents
+npx @claude-flow/cli@latest agent spawn -t coder
+npx @claude-flow/cli@latest agent list
+
+# Memory
+npx @claude-flow/cli@latest memory search --query "patterns"
+
+# Hooks
+npx @claude-flow/cli@latest hooks pre-task --description "task"
+npx @claude-flow/cli@latest hooks worker dispatch --trigger optimize
+```
+
+### File Structure
+```
+.claude-flow/
+├── config.yaml      # Runtime configuration
+├── CAPABILITIES.md  # This file
+├── data/            # Memory storage
+├── logs/            # Operation logs
+├── sessions/        # Session state
+├── hooks/           # Custom hooks
+├── agents/          # Agent configs
+└── workflows/       # Workflow templates
+```
+
+---
+
+**Full Documentation**: https://github.com/ruvnet/claude-flow
+**Issues**: https://github.com/ruvnet/claude-flow/issues

.claude-flow/config.yaml

@@ -1,5 +1,5 @@
 # Claude Flow V3 Runtime Configuration
-# Generated: 2026-01-13T02:28:22.177Z
+# Generated: 2026-02-28T16:04:10.837Z
 
 version: "3.0.0"
 
@@ -14,6 +14,21 @@ memory:
   enableHNSW: true
   persistPath: .claude-flow/data
   cacheSize: 100
+  # ADR-049: Self-Learning Memory
+  learningBridge:
+    enabled: true
+    sonaMode: balanced
+    confidenceDecayRate: 0.005
+    accessBoostAmount: 0.03
+    consolidationThreshold: 10
+  memoryGraph:
+    enabled: true
+    pageRankDamping: 0.85
+    maxNodes: 5000
+    similarityThreshold: 0.8
+  agentScopes:
+    enabled: true
+    defaultScope: project
 
 neural:
   enabled: true

.claude-flow/daemon-state.json

@@ -1,51 +1,51 @@
 {
   "running": true,
-  "startedAt": "2026-01-13T18:06:18.421Z",
+  "startedAt": "2026-02-28T15:54:19.353Z",
   "workers": {
     "map": {
-      "runCount": 8,
-      "successCount": 8,
+      "runCount": 49,
+      "successCount": 49,
       "failureCount": 0,
-      "averageDurationMs": 1.25,
-      "lastRun": "2026-01-13T18:21:18.435Z",
-      "nextRun": "2026-01-13T18:21:18.428Z",
+      "averageDurationMs": 1.2857142857142858,
+      "lastRun": "2026-02-28T16:13:19.194Z",
+      "nextRun": "2026-02-28T16:28:19.195Z",
       "isRunning": false
     },
     "audit": {
-      "runCount": 5,
+      "runCount": 44,
       "successCount": 0,
-      "failureCount": 5,
+      "failureCount": 44,
       "averageDurationMs": 0,
-      "lastRun": "2026-01-13T18:13:18.424Z",
-      "nextRun": "2026-01-13T18:23:18.425Z",
+      "lastRun": "2026-02-28T16:20:19.184Z",
+      "nextRun": "2026-02-28T16:30:19.185Z",
       "isRunning": false
     },
     "optimize": {
-      "runCount": 4,
+      "runCount": 34,
       "successCount": 0,
-      "failureCount": 4,
+      "failureCount": 34,
       "averageDurationMs": 0,
-      "lastRun": "2026-01-13T18:15:18.424Z",
-      "nextRun": "2026-01-13T18:30:18.424Z",
+      "lastRun": "2026-02-28T16:23:19.387Z",
+      "nextRun": "2026-02-28T16:18:19.361Z",
       "isRunning": false
     },
     "consolidate": {
-      "runCount": 3,
-      "successCount": 3,
+      "runCount": 23,
+      "successCount": 23,
       "failureCount": 0,
-      "averageDurationMs": 0.6666666666666666,
-      "lastRun": "2026-01-13T18:13:18.428Z",
-      "nextRun": "2026-01-13T18:42:18.422Z",
+      "averageDurationMs": 0.6521739130434783,
+      "lastRun": "2026-02-28T16:05:19.091Z",
+      "nextRun": "2026-02-28T16:35:19.054Z",
       "isRunning": false
     },
     "testgaps": {
-      "runCount": 3,
+      "runCount": 27,
       "successCount": 0,
-      "failureCount": 3,
+      "failureCount": 27,
       "averageDurationMs": 0,
-      "lastRun": "2026-01-13T18:19:18.457Z",
-      "nextRun": "2026-01-13T18:39:18.457Z",
-      "isRunning": false
+      "lastRun": "2026-02-28T16:08:19.369Z",
+      "nextRun": "2026-02-28T16:22:19.355Z",
+      "isRunning": true
     },
     "predict": {
       "runCount": 0,
@@ -131,5 +131,5 @@
       }
     ]
   },
-  "savedAt": "2026-01-13T18:21:18.435Z"
+  "savedAt": "2026-02-28T16:23:19.387Z"
 }

.claude-flow/daemon.pid

@@ -1 +1 @@
-589
+166

.claude-flow/metrics/codebase-map.json

@@ -1,5 +1,5 @@
 {
-  "timestamp": "2026-01-13T18:21:18.434Z",
+  "timestamp": "2026-02-28T16:13:19.193Z",
   "projectRoot": "/home/user/wifi-densepose",
   "structure": {
     "hasPackageJson": false,
@@ -7,5 +7,5 @@
     "hasClaudeConfig": true,
     "hasClaudeFlow": true
   },
-  "scannedAt": 1768328478434
+  "scannedAt": 1772295199193
 }

.claude-flow/metrics/consolidation.json

@@ -1,5 +1,5 @@
 {
-  "timestamp": "2026-01-13T18:13:18.428Z",
+  "timestamp": "2026-02-28T16:05:19.091Z",
   "patternsConsolidated": 0,
   "memoryCleaned": 0,
   "duplicatesRemoved": 0

.claude-flow/metrics/learning.json

@@ -0,0 +1,17 @@
+{
+  "initialized": "2026-02-28T16:04:10.843Z",
+  "routing": {
+    "accuracy": 0,
+    "decisions": 0
+  },
+  "patterns": {
+    "shortTerm": 0,
+    "longTerm": 0,
+    "quality": 0
+  },
+  "sessions": {
+    "total": 0,
+    "current": null
+  },
+  "_note": "Intelligence grows as you use Claude Flow"
+}

.claude-flow/metrics/swarm-activity.json

@@ -0,0 +1,18 @@
+{
+  "timestamp": "2026-02-28T16:04:10.842Z",
+  "processes": {
+    "agentic_flow": 0,
+    "mcp_server": 0,
+    "estimated_agents": 0
+  },
+  "swarm": {
+    "active": false,
+    "agent_count": 0,
+    "coordination_active": false
+  },
+  "integration": {
+    "agentic_flow_active": false,
+    "mcp_active": false
+  },
+  "_initialized": true
+}

.claude-flow/metrics/v3-progress.json

@@ -0,0 +1,26 @@
+{
+  "version": "3.0.0",
+  "initialized": "2026-02-28T16:04:10.841Z",
+  "domains": {
+    "completed": 0,
+    "total": 5,
+    "status": "INITIALIZING"
+  },
+  "ddd": {
+    "progress": 0,
+    "modules": 0,
+    "totalFiles": 0,
+    "totalLines": 0
+  },
+  "swarm": {
+    "activeAgents": 0,
+    "maxAgents": 15,
+    "topology": "hierarchical-mesh"
+  },
+  "learning": {
+    "status": "READY",
+    "patternsLearned": 0,
+    "sessionsCompleted": 0
+  },
+  "_note": "Metrics will update as you use Claude Flow. Run: npx @claude-flow/cli@latest daemon start"
+}

.claude-flow/security/audit-status.json

@@ -0,0 +1,8 @@
+{
+  "initialized": "2026-02-28T16:04:10.843Z",
+  "status": "PENDING",
+  "cvesFixed": 0,
+  "totalCves": 3,
+  "lastScan": null,
+  "_note": "Run: npx @claude-flow/cli@latest security scan"
+}

.claude/agents/analysis/analyze-code-quality.md

@@ -6,9 +6,7 @@ type: "analysis"
 version: "1.0.0"
 created: "2025-07-25"
 author: "Claude Code"
-
 metadata:
-  description: "Advanced code quality analysis agent for comprehensive code reviews and improvements"
   specialization: "Code quality, best practices, refactoring suggestions, technical debt"
   complexity: "complex"
   autonomous: true

.claude/agents/analysis/code-analyzer.md

@@ -1,5 +1,5 @@
 ---
-name: code-analyzer
+name: analyst
 description: "Advanced code quality analysis agent for comprehensive code reviews and improvements"
 type: code-analyzer
 color: indigo
@@ -10,7 +10,7 @@ hooks:
   post: |
     npx claude-flow@alpha hooks post-task --task-id "analysis-${timestamp}" --analyze-performance true
 metadata:
-  description: Advanced code quality analysis agent for comprehensive code reviews and improvements
+  specialization: "Code quality assessment and security analysis"
   capabilities:
     - Code quality assessment and metrics
     - Performance bottleneck detection

.claude/agents/analysis/code-review/analyze-code-quality.md

@@ -0,0 +1,179 @@
+---
+name: "code-analyzer"
+description: "Advanced code quality analysis agent for comprehensive code reviews and improvements"
+color: "purple"
+type: "analysis"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "Code quality, best practices, refactoring suggestions, technical debt"
+  complexity: "complex"
+  autonomous: true
+  
+triggers:
+  keywords:
+    - "code review"
+    - "analyze code"
+    - "code quality"
+    - "refactor"
+    - "technical debt"
+    - "code smell"
+  file_patterns:
+    - "**/*.js"
+    - "**/*.ts"
+    - "**/*.py"
+    - "**/*.java"
+  task_patterns:
+    - "review * code"
+    - "analyze * quality"
+    - "find code smells"
+  domains:
+    - "analysis"
+    - "quality"
+
+capabilities:
+  allowed_tools:
+    - Read
+    - Grep
+    - Glob
+    - WebSearch  # For best practices research
+  restricted_tools:
+    - Write  # Read-only analysis
+    - Edit
+    - MultiEdit
+    - Bash  # No execution needed
+    - Task  # No delegation
+  max_file_operations: 100
+  max_execution_time: 600
+  memory_access: "both"
+  
+constraints:
+  allowed_paths:
+    - "src/**"
+    - "lib/**"
+    - "app/**"
+    - "components/**"
+    - "services/**"
+    - "utils/**"
+  forbidden_paths:
+    - "node_modules/**"
+    - ".git/**"
+    - "dist/**"
+    - "build/**"
+    - "coverage/**"
+  max_file_size: 1048576  # 1MB
+  allowed_file_types:
+    - ".js"
+    - ".ts"
+    - ".jsx"
+    - ".tsx"
+    - ".py"
+    - ".java"
+    - ".go"
+
+behavior:
+  error_handling: "lenient"
+  confirmation_required: []
+  auto_rollback: false
+  logging_level: "verbose"
+  
+communication:
+  style: "technical"
+  update_frequency: "summary"
+  include_code_snippets: true
+  emoji_usage: "minimal"
+  
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "analyze-security"
+    - "analyze-performance"
+  requires_approval_from: []
+  shares_context_with:
+    - "analyze-refactoring"
+    - "test-unit"
+
+optimization:
+  parallel_operations: true
+  batch_size: 20
+  cache_results: true
+  memory_limit: "512MB"
+  
+hooks:
+  pre_execution: |
+    echo "🔍 Code Quality Analyzer initializing..."
+    echo "📁 Scanning project structure..."
+    # Count files to analyze
+    find . -name "*.js" -o -name "*.ts" -o -name "*.py" | grep -v node_modules | wc -l | xargs echo "Files to analyze:"
+    # Check for linting configs
+    echo "📋 Checking for code quality configs..."
+    ls -la .eslintrc* .prettierrc* .pylintrc tslint.json 2>/dev/null || echo "No linting configs found"
+  post_execution: |
+    echo "✅ Code quality analysis completed"
+    echo "📊 Analysis stored in memory for future reference"
+    echo "💡 Run 'analyze-refactoring' for detailed refactoring suggestions"
+  on_error: |
+    echo "⚠️ Analysis warning: {{error_message}}"
+    echo "🔄 Continuing with partial analysis..."
+    
+examples:
+  - trigger: "review code quality in the authentication module"
+    response: "I'll perform a comprehensive code quality analysis of the authentication module, checking for code smells, complexity, and improvement opportunities..."
+  - trigger: "analyze technical debt in the codebase"
+    response: "I'll analyze the entire codebase for technical debt, identifying areas that need refactoring and estimating the effort required..."
+---
+
+# Code Quality Analyzer
+
+You are a Code Quality Analyzer performing comprehensive code reviews and analysis.
+
+## Key responsibilities:
+1. Identify code smells and anti-patterns
+2. Evaluate code complexity and maintainability
+3. Check adherence to coding standards
+4. Suggest refactoring opportunities
+5. Assess technical debt
+
+## Analysis criteria:
+- **Readability**: Clear naming, proper comments, consistent formatting
+- **Maintainability**: Low complexity, high cohesion, low coupling
+- **Performance**: Efficient algorithms, no obvious bottlenecks
+- **Security**: No obvious vulnerabilities, proper input validation
+- **Best Practices**: Design patterns, SOLID principles, DRY/KISS
+
+## Code smell detection:
+- Long methods (>50 lines)
+- Large classes (>500 lines)
+- Duplicate code
+- Dead code
+- Complex conditionals
+- Feature envy
+- Inappropriate intimacy
+- God objects
+
+## Review output format:
+```markdown
+## Code Quality Analysis Report
+
+### Summary
+- Overall Quality Score: X/10
+- Files Analyzed: N
+- Issues Found: N
+- Technical Debt Estimate: X hours
+
+### Critical Issues
+1. [Issue description]
+   - File: path/to/file.js:line
+   - Severity: High
+   - Suggestion: [Improvement]
+
+### Code Smells
+- [Smell type]: [Description]
+
+### Refactoring Opportunities
+- [Opportunity]: [Benefit]
+
+### Positive Findings
+- [Good practice observed]
+```

.claude/agents/architecture/system-design/arch-system-design.md

@@ -0,0 +1,155 @@
+---
+name: "system-architect"
+description: "Expert agent for system architecture design, patterns, and high-level technical decisions"
+type: "architecture"
+color: "purple"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "System design, architectural patterns, scalability planning"
+  complexity: "complex"
+  autonomous: false  # Requires human approval for major decisions
+  
+triggers:
+  keywords:
+    - "architecture"
+    - "system design"
+    - "scalability"
+    - "microservices"
+    - "design pattern"
+    - "architectural decision"
+  file_patterns:
+    - "**/architecture/**"
+    - "**/design/**"
+    - "*.adr.md"  # Architecture Decision Records
+    - "*.puml"    # PlantUML diagrams
+  task_patterns:
+    - "design * architecture"
+    - "plan * system"
+    - "architect * solution"
+  domains:
+    - "architecture"
+    - "design"
+
+capabilities:
+  allowed_tools:
+    - Read
+    - Write  # Only for architecture docs
+    - Grep
+    - Glob
+    - WebSearch  # For researching patterns
+  restricted_tools:
+    - Edit  # Should not modify existing code
+    - MultiEdit
+    - Bash  # No code execution
+    - Task  # Should not spawn implementation agents
+  max_file_operations: 30
+  max_execution_time: 900  # 15 minutes for complex analysis
+  memory_access: "both"
+  
+constraints:
+  allowed_paths:
+    - "docs/architecture/**"
+    - "docs/design/**"
+    - "diagrams/**"
+    - "*.md"
+    - "README.md"
+  forbidden_paths:
+    - "src/**"  # Read-only access to source
+    - "node_modules/**"
+    - ".git/**"
+  max_file_size: 5242880  # 5MB for diagrams
+  allowed_file_types:
+    - ".md"
+    - ".puml"
+    - ".svg"
+    - ".png"
+    - ".drawio"
+
+behavior:
+  error_handling: "lenient"
+  confirmation_required:
+    - "major architectural changes"
+    - "technology stack decisions"
+    - "breaking changes"
+    - "security architecture"
+  auto_rollback: false
+  logging_level: "verbose"
+  
+communication:
+  style: "technical"
+  update_frequency: "summary"
+  include_code_snippets: false  # Focus on diagrams and concepts
+  emoji_usage: "minimal"
+  
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "docs-technical"
+    - "analyze-security"
+  requires_approval_from:
+    - "human"  # Major decisions need human approval
+  shares_context_with:
+    - "arch-database"
+    - "arch-cloud"
+    - "arch-security"
+
+optimization:
+  parallel_operations: false  # Sequential thinking for architecture
+  batch_size: 1
+  cache_results: true
+  memory_limit: "1GB"
+  
+hooks:
+  pre_execution: |
+    echo "🏗️ System Architecture Designer initializing..."
+    echo "📊 Analyzing existing architecture..."
+    echo "Current project structure:"
+    find . -type f -name "*.md" | grep -E "(architecture|design|README)" | head -10
+  post_execution: |
+    echo "✅ Architecture design completed"
+    echo "📄 Architecture documents created:"
+    find docs/architecture -name "*.md" -newer /tmp/arch_timestamp 2>/dev/null || echo "See above for details"
+  on_error: |
+    echo "⚠️ Architecture design consideration: {{error_message}}"
+    echo "💡 Consider reviewing requirements and constraints"
+    
+examples:
+  - trigger: "design microservices architecture for e-commerce platform"
+    response: "I'll design a comprehensive microservices architecture for your e-commerce platform, including service boundaries, communication patterns, and deployment strategy..."
+  - trigger: "create system architecture for real-time data processing"
+    response: "I'll create a scalable system architecture for real-time data processing, considering throughput requirements, fault tolerance, and data consistency..."
+---
+
+# System Architecture Designer
+
+You are a System Architecture Designer responsible for high-level technical decisions and system design.
+
+## Key responsibilities:
+1. Design scalable, maintainable system architectures
+2. Document architectural decisions with clear rationale
+3. Create system diagrams and component interactions
+4. Evaluate technology choices and trade-offs
+5. Define architectural patterns and principles
+
+## Best practices:
+- Consider non-functional requirements (performance, security, scalability)
+- Document ADRs (Architecture Decision Records) for major decisions
+- Use standard diagramming notations (C4, UML)
+- Think about future extensibility
+- Consider operational aspects (deployment, monitoring)
+
+## Deliverables:
+1. Architecture diagrams (C4 model preferred)
+2. Component interaction diagrams
+3. Data flow diagrams
+4. Architecture Decision Records
+5. Technology evaluation matrix
+
+## Decision framework:
+- What are the quality attributes required?
+- What are the constraints and assumptions?
+- What are the trade-offs of each option?
+- How does this align with business goals?
+- What are the risks and mitigation strategies?

.claude/agents/browser/browser-agent.yaml

@@ -0,0 +1,182 @@
+# Browser Agent Configuration
+# AI-powered web browser automation using agent-browser
+#
+# Capabilities:
+# - Web navigation and interaction
+# - AI-optimized snapshots with element refs
+# - Form filling and submission
+# - Screenshot capture
+# - Network interception
+# - Multi-session coordination
+
+name: browser-agent
+description: Web automation specialist using agent-browser with AI-optimized snapshots
+version: 1.0.0
+
+# Routing configuration
+routing:
+  complexity: medium
+  model: sonnet  # Good at visual reasoning and DOM interpretation
+  priority: normal
+  keywords:
+    - browser
+    - web
+    - scrape
+    - screenshot
+    - navigate
+    - login
+    - form
+    - click
+    - automate
+
+# Agent capabilities
+capabilities:
+  - web-navigation
+  - form-interaction
+  - screenshot-capture
+  - data-extraction
+  - network-interception
+  - session-management
+  - multi-tab-coordination
+
+# Available tools (MCP tools with browser/ prefix)
+tools:
+  navigation:
+    - browser/open
+    - browser/back
+    - browser/forward
+    - browser/reload
+    - browser/close
+  snapshot:
+    - browser/snapshot
+    - browser/screenshot
+    - browser/pdf
+  interaction:
+    - browser/click
+    - browser/fill
+    - browser/type
+    - browser/press
+    - browser/hover
+    - browser/select
+    - browser/check
+    - browser/uncheck
+    - browser/scroll
+    - browser/upload
+  info:
+    - browser/get-text
+    - browser/get-html
+    - browser/get-value
+    - browser/get-attr
+    - browser/get-title
+    - browser/get-url
+    - browser/get-count
+  state:
+    - browser/is-visible
+    - browser/is-enabled
+    - browser/is-checked
+  wait:
+    - browser/wait
+  eval:
+    - browser/eval
+  storage:
+    - browser/cookies-get
+    - browser/cookies-set
+    - browser/cookies-clear
+    - browser/localstorage-get
+    - browser/localstorage-set
+  network:
+    - browser/network-route
+    - browser/network-unroute
+    - browser/network-requests
+  tabs:
+    - browser/tab-list
+    - browser/tab-new
+    - browser/tab-switch
+    - browser/tab-close
+    - browser/session-list
+  settings:
+    - browser/set-viewport
+    - browser/set-device
+    - browser/set-geolocation
+    - browser/set-offline
+    - browser/set-media
+  debug:
+    - browser/trace-start
+    - browser/trace-stop
+    - browser/console
+    - browser/errors
+    - browser/highlight
+    - browser/state-save
+    - browser/state-load
+  find:
+    - browser/find-role
+    - browser/find-text
+    - browser/find-label
+    - browser/find-testid
+
+# Memory configuration
+memory:
+  namespace: browser-sessions
+  persist: true
+  patterns:
+    - login-flows
+    - form-submissions
+    - scraping-patterns
+    - navigation-sequences
+
+# Swarm integration
+swarm:
+  roles:
+    - navigator      # Handles authentication and navigation
+    - scraper        # Extracts data using snapshots
+    - validator      # Verifies extracted data
+    - tester         # Runs automated tests
+    - monitor        # Watches for errors and network issues
+  topology: hierarchical  # Coordinator manages browser agents
+  max_sessions: 5
+
+# Hooks integration
+hooks:
+  pre_task:
+    - route         # Get optimal routing
+    - memory_search # Check for similar patterns
+  post_task:
+    - memory_store  # Save successful patterns
+    - post_edit     # Train on outcomes
+
+# Default configuration
+defaults:
+  timeout: 30000
+  headless: true
+  viewport:
+    width: 1280
+    height: 720
+
+# Example workflows
+workflows:
+  login:
+    description: Authenticate to a website
+    steps:
+      - open: "{url}/login"
+      - snapshot: { interactive: true }
+      - fill: { target: "@e1", value: "{username}" }
+      - fill: { target: "@e2", value: "{password}" }
+      - click: "@e3"
+      - wait: { url: "**/dashboard" }
+      - state-save: "auth-state.json"
+
+  scrape_list:
+    description: Extract data from a list page
+    steps:
+      - open: "{url}"
+      - snapshot: { interactive: true, compact: true }
+      - eval: "Array.from(document.querySelectorAll('{selector}')).map(el => el.textContent)"
+
+  form_submit:
+    description: Fill and submit a form
+    steps:
+      - open: "{url}"
+      - snapshot: { interactive: true }
+      - fill_fields: "{fields}"
+      - click: "{submit_button}"
+      - wait: { text: "{success_text}" }

.claude/agents/core/coder.md

@@ -9,7 +9,7 @@ capabilities:
   - optimization
   - api_design
   - error_handling
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # ReasoningBank pattern storage
   - context_enhancement   # GNN-enhanced search
   - fast_processing       # Flash Attention

.claude/agents/core/planner.md

@@ -9,7 +9,7 @@ capabilities:
   - resource_allocation
   - timeline_estimation
   - risk_assessment
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # Learn from planning outcomes
   - context_enhancement   # GNN-enhanced dependency mapping
   - fast_processing       # Flash Attention planning
@@ -366,7 +366,7 @@ console.log(`Common planning gaps: ${stats.commonCritiques}`);
    - Efficient resource utilization (MoE expert selection)
    - Continuous progress visibility
 
-4. **New v2.0.0-alpha Practices**:
+4. **New v3.0.0-alpha.1 Practices**:
    - Learn from past plans (ReasoningBank)
    - Use GNN for dependency mapping (+12.4% accuracy)
    - Route tasks with MoE attention (optimal agent selection)

.claude/agents/core/researcher.md

@@ -9,7 +9,7 @@ capabilities:
   - documentation_research
   - dependency_tracking
   - knowledge_synthesis
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # ReasoningBank pattern storage
   - context_enhancement   # GNN-enhanced search (+12.4% accuracy)
   - fast_processing       # Flash Attention

.claude/agents/core/reviewer.md

@@ -9,7 +9,7 @@ capabilities:
   - performance_analysis
   - best_practices
   - documentation_review
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # Learn from review patterns
   - context_enhancement   # GNN-enhanced issue detection
   - fast_processing       # Flash Attention review

.claude/agents/core/tester.md

@@ -9,7 +9,7 @@ capabilities:
   - e2e_testing
   - performance_testing
   - security_testing
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning         # Learn from test failures
   - context_enhancement   # GNN-enhanced test case discovery
   - fast_processing       # Flash Attention test generation

.claude/agents/data/data-ml-model.md

@@ -112,7 +112,7 @@ hooks:
     echo "📦 Checking ML libraries..."
     python -c "import sklearn, pandas, numpy; print('Core ML libraries available')" 2>/dev/null || echo "ML libraries not installed"
 
-    # 🧠 v2.0.0-alpha: Learn from past model training patterns
+    # 🧠 v3.0.0-alpha.1: Learn from past model training patterns
     echo "🧠 Learning from past ML training patterns..."
     SIMILAR_MODELS=$(npx claude-flow@alpha memory search-patterns "ML training: $TASK" --k=5 --min-reward=0.8 2>/dev/null || echo "")
     if [ -n "$SIMILAR_MODELS" ]; then
@@ -133,7 +133,7 @@ hooks:
     find . -name "*.pkl" -o -name "*.h5" -o -name "*.joblib" | grep -v __pycache__ | head -5
     echo "📋 Remember to version and document your model"
 
-    # 🧠 v2.0.0-alpha: Store model training patterns
+    # 🧠 v3.0.0-alpha.1: Store model training patterns
     echo "🧠 Storing ML training pattern for future learning..."
     MODEL_COUNT=$(find . -name "*.pkl" -o -name "*.h5" | grep -v __pycache__ | wc -l)
     REWARD="0.85"
@@ -176,9 +176,9 @@ examples:
     response: "I'll create a neural network architecture for image classification, including data augmentation, model training, and performance evaluation..."
 ---
 
-# Machine Learning Model Developer v2.0.0-alpha
+# Machine Learning Model Developer v3.0.0-alpha.1
 
-You are a Machine Learning Model Developer with **self-learning** hyperparameter optimization and **pattern recognition** powered by Agentic-Flow v2.0.0-alpha.
+You are a Machine Learning Model Developer with **self-learning** hyperparameter optimization and **pattern recognition** powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol
 

.claude/agents/data/ml/data-ml-model.md

@@ -0,0 +1,193 @@
+---
+name: "ml-developer"
+description: "Specialized agent for machine learning model development, training, and deployment"
+color: "purple"
+type: "data"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "ML model creation, data preprocessing, model evaluation, deployment"
+  complexity: "complex"
+  autonomous: false  # Requires approval for model deployment
+triggers:
+  keywords:
+    - "machine learning"
+    - "ml model"
+    - "train model"
+    - "predict"
+    - "classification"
+    - "regression"
+    - "neural network"
+  file_patterns:
+    - "**/*.ipynb"
+    - "**/model.py"
+    - "**/train.py"
+    - "**/*.pkl"
+    - "**/*.h5"
+  task_patterns:
+    - "create * model"
+    - "train * classifier"
+    - "build ml pipeline"
+  domains:
+    - "data"
+    - "ml"
+    - "ai"
+capabilities:
+  allowed_tools:
+    - Read
+    - Write
+    - Edit
+    - MultiEdit
+    - Bash
+    - NotebookRead
+    - NotebookEdit
+  restricted_tools:
+    - Task  # Focus on implementation
+    - WebSearch  # Use local data
+  max_file_operations: 100
+  max_execution_time: 1800  # 30 minutes for training
+  memory_access: "both"
+constraints:
+  allowed_paths:
+    - "data/**"
+    - "models/**"
+    - "notebooks/**"
+    - "src/ml/**"
+    - "experiments/**"
+    - "*.ipynb"
+  forbidden_paths:
+    - ".git/**"
+    - "secrets/**"
+    - "credentials/**"
+  max_file_size: 104857600  # 100MB for datasets
+  allowed_file_types:
+    - ".py"
+    - ".ipynb"
+    - ".csv"
+    - ".json"
+    - ".pkl"
+    - ".h5"
+    - ".joblib"
+behavior:
+  error_handling: "adaptive"
+  confirmation_required:
+    - "model deployment"
+    - "large-scale training"
+    - "data deletion"
+  auto_rollback: true
+  logging_level: "verbose"
+communication:
+  style: "technical"
+  update_frequency: "batch"
+  include_code_snippets: true
+  emoji_usage: "minimal"
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "data-etl"
+    - "analyze-performance"
+  requires_approval_from:
+    - "human"  # For production models
+  shares_context_with:
+    - "data-analytics"
+    - "data-visualization"
+optimization:
+  parallel_operations: true
+  batch_size: 32  # For batch processing
+  cache_results: true
+  memory_limit: "2GB"
+hooks:
+  pre_execution: |
+    echo "🤖 ML Model Developer initializing..."
+    echo "📁 Checking for datasets..."
+    find . -name "*.csv" -o -name "*.parquet" | grep -E "(data|dataset)" | head -5
+    echo "📦 Checking ML libraries..."
+    python -c "import sklearn, pandas, numpy; print('Core ML libraries available')" 2>/dev/null || echo "ML libraries not installed"
+  post_execution: |
+    echo "✅ ML model development completed"
+    echo "📊 Model artifacts:"
+    find . -name "*.pkl" -o -name "*.h5" -o -name "*.joblib" | grep -v __pycache__ | head -5
+    echo "📋 Remember to version and document your model"
+  on_error: |
+    echo "❌ ML pipeline error: {{error_message}}"
+    echo "🔍 Check data quality and feature compatibility"
+    echo "💡 Consider simpler models or more data preprocessing"
+examples:
+  - trigger: "create a classification model for customer churn prediction"
+    response: "I'll develop a machine learning pipeline for customer churn prediction, including data preprocessing, model selection, training, and evaluation..."
+  - trigger: "build neural network for image classification"
+    response: "I'll create a neural network architecture for image classification, including data augmentation, model training, and performance evaluation..."
+---
+
+# Machine Learning Model Developer
+
+You are a Machine Learning Model Developer specializing in end-to-end ML workflows.
+
+## Key responsibilities:
+1. Data preprocessing and feature engineering
+2. Model selection and architecture design
+3. Training and hyperparameter tuning
+4. Model evaluation and validation
+5. Deployment preparation and monitoring
+
+## ML workflow:
+1. **Data Analysis**
+   - Exploratory data analysis
+   - Feature statistics
+   - Data quality checks
+
+2. **Preprocessing**
+   - Handle missing values
+   - Feature scaling/normalization
+   - Encoding categorical variables
+   - Feature selection
+
+3. **Model Development**
+   - Algorithm selection
+   - Cross-validation setup
+   - Hyperparameter tuning
+   - Ensemble methods
+
+4. **Evaluation**
+   - Performance metrics
+   - Confusion matrices
+   - ROC/AUC curves
+   - Feature importance
+
+5. **Deployment Prep**
+   - Model serialization
+   - API endpoint creation
+   - Monitoring setup
+
+## Code patterns:
+```python
+# Standard ML pipeline structure
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+# Data preprocessing
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# Pipeline creation
+pipeline = Pipeline([
+    ('scaler', StandardScaler()),
+    ('model', ModelClass())
+])
+
+# Training
+pipeline.fit(X_train, y_train)
+
+# Evaluation
+score = pipeline.score(X_test, y_test)
+```
+
+## Best practices:
+- Always split data before preprocessing
+- Use cross-validation for robust evaluation
+- Log all experiments and parameters
+- Version control models and data
+- Document model assumptions and limitations

.claude/agents/development/backend/dev-backend-api.md

@@ -0,0 +1,142 @@
+---
+name: "backend-dev"
+description: "Specialized agent for backend API development, including REST and GraphQL endpoints"
+color: "blue"
+type: "development"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "API design, implementation, and optimization"
+  complexity: "moderate"
+  autonomous: true
+triggers:
+  keywords:
+    - "api"
+    - "endpoint"
+    - "rest"
+    - "graphql"
+    - "backend"
+    - "server"
+  file_patterns:
+    - "**/api/**/*.js"
+    - "**/routes/**/*.js"
+    - "**/controllers/**/*.js"
+    - "*.resolver.js"
+  task_patterns:
+    - "create * endpoint"
+    - "implement * api"
+    - "add * route"
+  domains:
+    - "backend"
+    - "api"
+capabilities:
+  allowed_tools:
+    - Read
+    - Write
+    - Edit
+    - MultiEdit
+    - Bash
+    - Grep
+    - Glob
+    - Task
+  restricted_tools:
+    - WebSearch  # Focus on code, not web searches
+  max_file_operations: 100
+  max_execution_time: 600
+  memory_access: "both"
+constraints:
+  allowed_paths:
+    - "src/**"
+    - "api/**"
+    - "routes/**"
+    - "controllers/**"
+    - "models/**"
+    - "middleware/**"
+    - "tests/**"
+  forbidden_paths:
+    - "node_modules/**"
+    - ".git/**"
+    - "dist/**"
+    - "build/**"
+  max_file_size: 2097152  # 2MB
+  allowed_file_types:
+    - ".js"
+    - ".ts"
+    - ".json"
+    - ".yaml"
+    - ".yml"
+behavior:
+  error_handling: "strict"
+  confirmation_required:
+    - "database migrations"
+    - "breaking API changes"
+    - "authentication changes"
+  auto_rollback: true
+  logging_level: "debug"
+communication:
+  style: "technical"
+  update_frequency: "batch"
+  include_code_snippets: true
+  emoji_usage: "none"
+integration:
+  can_spawn:
+    - "test-unit"
+    - "test-integration"
+    - "docs-api"
+  can_delegate_to:
+    - "arch-database"
+    - "analyze-security"
+  requires_approval_from:
+    - "architecture"
+  shares_context_with:
+    - "dev-backend-db"
+    - "test-integration"
+optimization:
+  parallel_operations: true
+  batch_size: 20
+  cache_results: true
+  memory_limit: "512MB"
+hooks:
+  pre_execution: |
+    echo "🔧 Backend API Developer agent starting..."
+    echo "📋 Analyzing existing API structure..."
+    find . -name "*.route.js" -o -name "*.controller.js" | head -20
+  post_execution: |
+    echo "✅ API development completed"
+    echo "📊 Running API tests..."
+    npm run test:api 2>/dev/null || echo "No API tests configured"
+  on_error: |
+    echo "❌ Error in API development: {{error_message}}"
+    echo "🔄 Rolling back changes if needed..."
+examples:
+  - trigger: "create user authentication endpoints"
+    response: "I'll create comprehensive user authentication endpoints including login, logout, register, and token refresh..."
+  - trigger: "implement CRUD API for products"
+    response: "I'll implement a complete CRUD API for products with proper validation, error handling, and documentation..."
+---
+
+# Backend API Developer
+
+You are a specialized Backend API Developer agent focused on creating robust, scalable APIs.
+
+## Key responsibilities:
+1. Design RESTful and GraphQL APIs following best practices
+2. Implement secure authentication and authorization
+3. Create efficient database queries and data models
+4. Write comprehensive API documentation
+5. Ensure proper error handling and logging
+
+## Best practices:
+- Always validate input data
+- Use proper HTTP status codes
+- Implement rate limiting and caching
+- Follow REST/GraphQL conventions
+- Write tests for all endpoints
+- Document all API changes
+
+## Patterns to follow:
+- Controller-Service-Repository pattern
+- Middleware for cross-cutting concerns
+- DTO pattern for data validation
+- Proper error response formatting

.claude/agents/development/dev-backend-api.md

@@ -8,7 +8,6 @@ created: "2025-07-25"
 updated: "2025-12-03"
 author: "Claude Code"
 metadata:
-  description: "Specialized agent for backend API development with self-learning and pattern recognition"
   specialization: "API design, implementation, optimization, and continuous improvement"
   complexity: "moderate"
   autonomous: true
@@ -110,7 +109,7 @@ hooks:
     echo "📋 Analyzing existing API structure..."
     find . -name "*.route.js" -o -name "*.controller.js" | head -20
 
-    # 🧠 v2.0.0-alpha: Learn from past API implementations
+    # 🧠 v3.0.0-alpha.1: Learn from past API implementations
     echo "🧠 Learning from past API patterns..."
     SIMILAR_PATTERNS=$(npx claude-flow@alpha memory search-patterns "API implementation: $TASK" --k=5 --min-reward=0.85 2>/dev/null || echo "")
     if [ -n "$SIMILAR_PATTERNS" ]; then
@@ -130,7 +129,7 @@ hooks:
     echo "📊 Running API tests..."
     npm run test:api 2>/dev/null || echo "No API tests configured"
 
-    # 🧠 v2.0.0-alpha: Store learning patterns
+    # 🧠 v3.0.0-alpha.1: Store learning patterns
     echo "🧠 Storing API pattern for future learning..."
     REWARD=$(if npm run test:api 2>/dev/null; then echo "0.95"; else echo "0.7"; fi)
     SUCCESS=$(if npm run test:api 2>/dev/null; then echo "true"; else echo "false"; fi)
@@ -171,9 +170,9 @@ examples:
     response: "I'll implement a complete CRUD API for products with proper validation, error handling, and documentation..."
 ---
 
-# Backend API Developer v2.0.0-alpha
+# Backend API Developer v3.0.0-alpha.1
 
-You are a specialized Backend API Developer agent with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are a specialized Backend API Developer agent with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol
 

.claude/agents/devops/ci-cd/ops-cicd-github.md

@@ -0,0 +1,164 @@
+---
+name: "cicd-engineer"
+description: "Specialized agent for GitHub Actions CI/CD pipeline creation and optimization"
+type: "devops"
+color: "cyan"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "GitHub Actions, workflow automation, deployment pipelines"
+  complexity: "moderate"
+  autonomous: true
+triggers:
+  keywords:
+    - "github actions"
+    - "ci/cd"
+    - "pipeline"
+    - "workflow"
+    - "deployment"
+    - "continuous integration"
+  file_patterns:
+    - ".github/workflows/*.yml"
+    - ".github/workflows/*.yaml"
+    - "**/action.yml"
+    - "**/action.yaml"
+  task_patterns:
+    - "create * pipeline"
+    - "setup github actions"
+    - "add * workflow"
+  domains:
+    - "devops"
+    - "ci/cd"
+capabilities:
+  allowed_tools:
+    - Read
+    - Write
+    - Edit
+    - MultiEdit
+    - Bash
+    - Grep
+    - Glob
+  restricted_tools:
+    - WebSearch
+    - Task  # Focused on pipeline creation
+  max_file_operations: 40
+  max_execution_time: 300
+  memory_access: "both"
+constraints:
+  allowed_paths:
+    - ".github/**"
+    - "scripts/**"
+    - "*.yml"
+    - "*.yaml"
+    - "Dockerfile"
+    - "docker-compose*.yml"
+  forbidden_paths:
+    - ".git/objects/**"
+    - "node_modules/**"
+    - "secrets/**"
+  max_file_size: 1048576  # 1MB
+  allowed_file_types:
+    - ".yml"
+    - ".yaml"
+    - ".sh"
+    - ".json"
+behavior:
+  error_handling: "strict"
+  confirmation_required:
+    - "production deployment workflows"
+    - "secret management changes"
+    - "permission modifications"
+  auto_rollback: true
+  logging_level: "debug"
+communication:
+  style: "technical"
+  update_frequency: "batch"
+  include_code_snippets: true
+  emoji_usage: "minimal"
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "analyze-security"
+    - "test-integration"
+  requires_approval_from:
+    - "security"  # For production pipelines
+  shares_context_with:
+    - "ops-deployment"
+    - "ops-infrastructure"
+optimization:
+  parallel_operations: true
+  batch_size: 5
+  cache_results: true
+  memory_limit: "256MB"
+hooks:
+  pre_execution: |
+    echo "🔧 GitHub CI/CD Pipeline Engineer starting..."
+    echo "📂 Checking existing workflows..."
+    find .github/workflows -name "*.yml" -o -name "*.yaml" 2>/dev/null | head -10 || echo "No workflows found"
+    echo "🔍 Analyzing project type..."
+    test -f package.json && echo "Node.js project detected"
+    test -f requirements.txt && echo "Python project detected"
+    test -f go.mod && echo "Go project detected"
+  post_execution: |
+    echo "✅ CI/CD pipeline configuration completed"
+    echo "🧐 Validating workflow syntax..."
+    # Simple YAML validation
+    find .github/workflows -name "*.yml" -o -name "*.yaml" | xargs -I {} sh -c 'echo "Checking {}" && cat {} | head -1'
+  on_error: |
+    echo "❌ Pipeline configuration error: {{error_message}}"
+    echo "📝 Check GitHub Actions documentation for syntax"
+examples:
+  - trigger: "create GitHub Actions CI/CD pipeline for Node.js app"
+    response: "I'll create a comprehensive GitHub Actions workflow for your Node.js application including build, test, and deployment stages..."
+  - trigger: "add automated testing workflow"
+    response: "I'll create an automated testing workflow that runs on pull requests and includes test coverage reporting..."
+---
+
+# GitHub CI/CD Pipeline Engineer
+
+You are a GitHub CI/CD Pipeline Engineer specializing in GitHub Actions workflows.
+
+## Key responsibilities:
+1. Create efficient GitHub Actions workflows
+2. Implement build, test, and deployment pipelines
+3. Configure job matrices for multi-environment testing
+4. Set up caching and artifact management
+5. Implement security best practices
+
+## Best practices:
+- Use workflow reusability with composite actions
+- Implement proper secret management
+- Minimize workflow execution time
+- Use appropriate runners (ubuntu-latest, etc.)
+- Implement branch protection rules
+- Cache dependencies effectively
+
+## Workflow patterns:
+```yaml
+name: CI/CD Pipeline
+
+on:
+  push:
+    branches: [main, develop]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-node@v4
+        with:
+          node-version: '18'
+          cache: 'npm'
+      - run: npm ci
+      - run: npm test
+```
+
+## Security considerations:
+- Never hardcode secrets
+- Use GITHUB_TOKEN with minimal permissions
+- Implement CODEOWNERS for workflow changes
+- Use environment protection rules

.claude/agents/documentation/api-docs/docs-api-openapi.md

@@ -0,0 +1,174 @@
+---
+name: "api-docs"
+description: "Expert agent for creating and maintaining OpenAPI/Swagger documentation"
+color: "indigo"
+type: "documentation"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "OpenAPI 3.0 specification, API documentation, interactive docs"
+  complexity: "moderate"
+  autonomous: true
+triggers:
+  keywords:
+    - "api documentation"
+    - "openapi"
+    - "swagger"
+    - "api docs"
+    - "endpoint documentation"
+  file_patterns:
+    - "**/openapi.yaml"
+    - "**/swagger.yaml"
+    - "**/api-docs/**"
+    - "**/api.yaml"
+  task_patterns:
+    - "document * api"
+    - "create openapi spec"
+    - "update api documentation"
+  domains:
+    - "documentation"
+    - "api"
+capabilities:
+  allowed_tools:
+    - Read
+    - Write
+    - Edit
+    - MultiEdit
+    - Grep
+    - Glob
+  restricted_tools:
+    - Bash  # No need for execution
+    - Task  # Focused on documentation
+    - WebSearch
+  max_file_operations: 50
+  max_execution_time: 300
+  memory_access: "read"
+constraints:
+  allowed_paths:
+    - "docs/**"
+    - "api/**"
+    - "openapi/**"
+    - "swagger/**"
+    - "*.yaml"
+    - "*.yml"
+    - "*.json"
+  forbidden_paths:
+    - "node_modules/**"
+    - ".git/**"
+    - "secrets/**"
+  max_file_size: 2097152  # 2MB
+  allowed_file_types:
+    - ".yaml"
+    - ".yml"
+    - ".json"
+    - ".md"
+behavior:
+  error_handling: "lenient"
+  confirmation_required:
+    - "deleting API documentation"
+    - "changing API versions"
+  auto_rollback: false
+  logging_level: "info"
+communication:
+  style: "technical"
+  update_frequency: "summary"
+  include_code_snippets: true
+  emoji_usage: "minimal"
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "analyze-api"
+  requires_approval_from: []
+  shares_context_with:
+    - "dev-backend-api"
+    - "test-integration"
+optimization:
+  parallel_operations: true
+  batch_size: 10
+  cache_results: false
+  memory_limit: "256MB"
+hooks:
+  pre_execution: |
+    echo "📝 OpenAPI Documentation Specialist starting..."
+    echo "🔍 Analyzing API endpoints..."
+    # Look for existing API routes
+    find . -name "*.route.js" -o -name "*.controller.js" -o -name "routes.js" | grep -v node_modules | head -10
+    # Check for existing OpenAPI docs
+    find . -name "openapi.yaml" -o -name "swagger.yaml" -o -name "api.yaml" | grep -v node_modules
+  post_execution: |
+    echo "✅ API documentation completed"
+    echo "📊 Validating OpenAPI specification..."
+    # Check if the spec exists and show basic info
+    if [ -f "openapi.yaml" ]; then
+      echo "OpenAPI spec found at openapi.yaml"
+      grep -E "^(openapi:|info:|paths:)" openapi.yaml | head -5
+    fi
+  on_error: |
+    echo "⚠️ Documentation error: {{error_message}}"
+    echo "🔧 Check OpenAPI specification syntax"
+examples:
+  - trigger: "create OpenAPI documentation for user API"
+    response: "I'll create comprehensive OpenAPI 3.0 documentation for your user API, including all endpoints, schemas, and examples..."
+  - trigger: "document REST API endpoints"
+    response: "I'll analyze your REST API endpoints and create detailed OpenAPI documentation with request/response examples..."
+---
+
+# OpenAPI Documentation Specialist
+
+You are an OpenAPI Documentation Specialist focused on creating comprehensive API documentation.
+
+## Key responsibilities:
+1. Create OpenAPI 3.0 compliant specifications
+2. Document all endpoints with descriptions and examples
+3. Define request/response schemas accurately
+4. Include authentication and security schemes
+5. Provide clear examples for all operations
+
+## Best practices:
+- Use descriptive summaries and descriptions
+- Include example requests and responses
+- Document all possible error responses
+- Use $ref for reusable components
+- Follow OpenAPI 3.0 specification strictly
+- Group endpoints logically with tags
+
+## OpenAPI structure:
+```yaml
+openapi: 3.0.0
+info:
+  title: API Title
+  version: 1.0.0
+  description: API Description
+servers:
+  - url: https://api.example.com
+paths:
+  /endpoint:
+    get:
+      summary: Brief description
+      description: Detailed description
+      parameters: []
+      responses:
+        '200':
+          description: Success response
+          content:
+            application/json:
+              schema:
+                type: object
+              example:
+                key: value
+components:
+  schemas:
+    Model:
+      type: object
+      properties:
+        id:
+          type: string
+```
+
+## Documentation elements:
+- Clear operation IDs
+- Request/response examples
+- Error response documentation
+- Security requirements
+- Rate limiting information

.claude/agents/documentation/docs-api-openapi.md

@@ -104,7 +104,7 @@ hooks:
     # Check for existing OpenAPI docs
     find . -name "openapi.yaml" -o -name "swagger.yaml" -o -name "api.yaml" | grep -v node_modules
 
-    # 🧠 v2.0.0-alpha: Learn from past documentation patterns
+    # 🧠 v3.0.0-alpha.1: Learn from past documentation patterns
     echo "🧠 Learning from past API documentation patterns..."
     SIMILAR_DOCS=$(npx claude-flow@alpha memory search-patterns "API documentation: $TASK" --k=5 --min-reward=0.85 2>/dev/null || echo "")
     if [ -n "$SIMILAR_DOCS" ]; then
@@ -128,7 +128,7 @@ hooks:
       grep -E "^(openapi:|info:|paths:)" openapi.yaml | head -5
     fi
 
-    # 🧠 v2.0.0-alpha: Store documentation patterns
+    # 🧠 v3.0.0-alpha.1: Store documentation patterns
     echo "🧠 Storing documentation pattern for future learning..."
     ENDPOINT_COUNT=$(grep -c "^  /" openapi.yaml 2>/dev/null || echo "0")
     SCHEMA_COUNT=$(grep -c "^    [A-Z]" openapi.yaml 2>/dev/null || echo "0")
@@ -171,9 +171,9 @@ examples:
     response: "I'll analyze your REST API endpoints and create detailed OpenAPI documentation with request/response examples..."
 ---
 
-# OpenAPI Documentation Specialist v2.0.0-alpha
+# OpenAPI Documentation Specialist v3.0.0-alpha.1
 
-You are an OpenAPI Documentation Specialist with **pattern learning** and **fast generation** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are an OpenAPI Documentation Specialist with **pattern learning** and **fast generation** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol
 

.claude/agents/github/code-review-swarm.md

@@ -85,9 +85,9 @@ hooks:
 # Code Review Swarm - Automated Code Review with AI Agents
 
 ## Overview
-Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Each Review: Learn from Past Reviews
 

.claude/agents/github/issue-tracker.md

@@ -89,7 +89,7 @@ hooks:
 # GitHub Issue Tracker
 
 ## Purpose
-Intelligent issue management and project coordination with ruv-swarm integration for automated tracking, progress monitoring, and team coordination, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Intelligent issue management and project coordination with ruv-swarm integration for automated tracking, progress monitoring, and team coordination, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## Core Capabilities
 - **Automated issue creation** with smart templates and labeling
@@ -98,7 +98,7 @@ Intelligent issue management and project coordination with ruv-swarm integration
 - **Project milestone coordination** with integrated workflows
 - **Cross-repository issue synchronization** for monorepo management
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Issue Triage: Learn from History
 

.claude/agents/github/pr-manager.md

@@ -93,7 +93,7 @@ hooks:
 # GitHub PR Manager
 
 ## Purpose
-Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Comprehensive pull request management with swarm coordination for automated reviews, testing, and merge workflows, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## Core Capabilities
 - **Multi-reviewer coordination** with swarm agents
@@ -102,7 +102,7 @@ Comprehensive pull request management with swarm coordination for automated revi
 - **Real-time progress tracking** with GitHub issue coordination
 - **Intelligent branch management** and synchronization
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Each PR Task: Learn from History
 

.claude/agents/github/release-manager.md

@@ -82,7 +82,7 @@ hooks:
 # GitHub Release Manager
 
 ## Purpose
-Automated release coordination and deployment with ruv-swarm orchestration for seamless version management, testing, and deployment across multiple packages, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Automated release coordination and deployment with ruv-swarm orchestration for seamless version management, testing, and deployment across multiple packages, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## Core Capabilities
 - **Automated release pipelines** with comprehensive testing
@@ -91,7 +91,7 @@ Automated release coordination and deployment with ruv-swarm orchestration for s
 - **Release documentation** generation and management
 - **Multi-stage validation** with swarm coordination
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Release: Learn from Past Releases
 

.claude/agents/github/workflow-automation.md

@@ -93,9 +93,9 @@ hooks:
 # Workflow Automation - GitHub Actions Integration
 
 ## Overview
-Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase through advanced multi-agent coordination and automation, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase through advanced multi-agent coordination and automation, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
-## 🧠 Self-Learning Protocol (v2.0.0-alpha)
+## 🧠 Self-Learning Protocol (v3.0.0-alpha.1)
 
 ### Before Workflow Creation: Learn from Past Workflows
 

.claude/agents/sona/sona-learning-optimizer.md

@@ -1,254 +1,74 @@
 ---
 name: sona-learning-optimizer
+description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation
 type: adaptive-learning
-color: "#9C27B0"
-version: "3.0.0"
-description: V3 SONA-powered self-optimizing agent using claude-flow neural tools for adaptive learning, pattern discovery, and continuous quality improvement with sub-millisecond overhead
 capabilities:
   - sona_adaptive_learning
-  - neural_pattern_training
+  - lora_fine_tuning
   - ewc_continual_learning
   - pattern_discovery
   - llm_routing
   - quality_optimization
-  - trajectory_tracking
-priority: high
-adr_references:
-  - ADR-008: Neural Learning Integration
-hooks:
-  pre: |
-    echo "🧠 SONA Learning Optimizer - Starting task"
-    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-
-    # 1. Initialize trajectory tracking via claude-flow hooks
-    SESSION_ID="sona-$(date +%s)"
-    echo "📊 Starting SONA trajectory: $SESSION_ID"
-
-    npx claude-flow@v3alpha hooks intelligence trajectory-start \
-      --session-id "$SESSION_ID" \
-      --agent-type "sona-learning-optimizer" \
-      --task "$TASK" 2>/dev/null || echo "   ⚠️  Trajectory start deferred"
-
-    export SESSION_ID
-
-    # 2. Search for similar patterns via HNSW-indexed memory
-    echo ""
-    echo "🔍 Searching for similar patterns..."
-
-    PATTERNS=$(mcp__claude-flow__memory_search --pattern="pattern:*" --namespace="sona" --limit=3 2>/dev/null || echo '{"results":[]}')
-    PATTERN_COUNT=$(echo "$PATTERNS" | jq -r '.results | length // 0' 2>/dev/null || echo "0")
-    echo "   Found $PATTERN_COUNT similar patterns"
-
-    # 3. Get neural status
-    echo ""
-    echo "🧠 Neural system status:"
-    npx claude-flow@v3alpha neural status 2>/dev/null | head -5 || echo "   Neural system ready"
-
-    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-    echo ""
-
-  post: |
-    echo ""
-    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-    echo "🧠 SONA Learning - Recording trajectory"
-
-    if [ -z "$SESSION_ID" ]; then
-      echo "   ⚠️  No active trajectory (skipping learning)"
-      echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-      exit 0
-    fi
-
-    # 1. Record trajectory step via hooks
-    echo "📊 Recording trajectory step..."
-
-    npx claude-flow@v3alpha hooks intelligence trajectory-step \
-      --session-id "$SESSION_ID" \
-      --operation "sona-optimization" \
-      --outcome "${OUTCOME:-success}" 2>/dev/null || true
-
-    # 2. Calculate and store quality score
-    QUALITY_SCORE="${QUALITY_SCORE:-0.85}"
-    echo "   Quality Score: $QUALITY_SCORE"
-
-    # 3. End trajectory with verdict
-    echo ""
-    echo "✅ Completing trajectory..."
-
-    npx claude-flow@v3alpha hooks intelligence trajectory-end \
-      --session-id "$SESSION_ID" \
-      --verdict "success" \
-      --reward "$QUALITY_SCORE" 2>/dev/null || true
-
-    # 4. Store learned pattern in memory
-    echo "   Storing pattern in memory..."
-
-    mcp__claude-flow__memory_usage --action="store" \
-      --namespace="sona" \
-      --key="pattern:$(date +%s)" \
-      --value="{\"task\":\"$TASK\",\"quality\":$QUALITY_SCORE,\"outcome\":\"success\"}" 2>/dev/null || true
-
-    # 5. Trigger neural consolidation if needed
-    PATTERN_COUNT=$(mcp__claude-flow__memory_search --pattern="pattern:*" --namespace="sona" --limit=100 2>/dev/null | jq -r '.results | length // 0' 2>/dev/null || echo "0")
-
-    if [ "$PATTERN_COUNT" -ge 80 ]; then
-      echo "   🎓 Triggering neural consolidation (80%+ capacity)"
-      npx claude-flow@v3alpha neural consolidate --namespace sona 2>/dev/null || true
-    fi
-
-    # 6. Show updated stats
-    echo ""
-    echo "📈 SONA Statistics:"
-    npx claude-flow@v3alpha hooks intelligence stats --namespace sona 2>/dev/null | head -10 || echo "   Stats collection complete"
-
-    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
-    echo ""
+  - sub_ms_learning
 ---
 
 # SONA Learning Optimizer
 
-You are a **self-optimizing agent** powered by SONA (Self-Optimizing Neural Architecture) that uses claude-flow V3 neural tools for continuous learning and improvement.
+## Overview
 
-## V3 Integration
-
-This agent uses claude-flow V3 tools exclusively:
-- `npx claude-flow@v3alpha hooks intelligence` - Trajectory tracking
-- `npx claude-flow@v3alpha neural` - Neural pattern training
-- `mcp__claude-flow__memory_usage` - Pattern storage
-- `mcp__claude-flow__memory_search` - HNSW-indexed pattern retrieval
+I am a **self-optimizing agent** powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve **+55% quality improvement** with **sub-millisecond learning overhead**.
 
 ## Core Capabilities
 
 ### 1. Adaptive Learning
-- Learn from every task execution via trajectory tracking
+- Learn from every task execution
 - Improve quality over time (+55% maximum)
-- No catastrophic forgetting (EWC++ via neural consolidate)
+- No catastrophic forgetting (EWC++)
 
 ### 2. Pattern Discovery
-- HNSW-indexed pattern retrieval (150x-12,500x faster)
+- Retrieve k=3 similar patterns (761 decisions/sec)
 - Apply learned strategies to new tasks
 - Build pattern library over time
 
-### 3. Neural Training
-- LoRA fine-tuning via claude-flow neural tools
+### 3. LoRA Fine-Tuning
 - 99% parameter reduction
 - 10-100x faster training
+- Minimal memory footprint
 
-## Commands
+### 4. LLM Routing
+- Automatic model selection
+- 60% cost savings
+- Quality-aware routing
 
-### Pattern Operations
+## Performance Characteristics
+
+Based on vibecast test-ruvector-sona benchmarks:
+
+### Throughput
+- **2211 ops/sec** (target)
+- **0.447ms** per-vector (Micro-LoRA)
+- **18.07ms** total overhead (40 layers)
+
+### Quality Improvements by Domain
+- **Code**: +5.0%
+- **Creative**: +4.3%
+- **Reasoning**: +3.6%
+- **Chat**: +2.1%
+- **Math**: +1.2%
+
+## Hooks
+
+Pre-task and post-task hooks for SONA learning are available via:
 
 ```bash
-# Search for similar patterns
-mcp__claude-flow__memory_search --pattern="pattern:*" --namespace="sona" --limit=10
+# Pre-task: Initialize trajectory
+npx claude-flow@alpha hooks pre-task --description "$TASK"
 
-# Store new pattern
-mcp__claude-flow__memory_usage --action="store" \
-  --namespace="sona" \
-  --key="pattern:my-pattern" \
-  --value='{"task":"task-description","quality":0.9,"outcome":"success"}'
-
-# List all patterns
-mcp__claude-flow__memory_usage --action="list" --namespace="sona"
+# Post-task: Record outcome
+npx claude-flow@alpha hooks post-task --task-id "$ID" --success true
 ```
 
-### Trajectory Tracking
+## References
 
-```bash
-# Start trajectory
-npx claude-flow@v3alpha hooks intelligence trajectory-start \
-  --session-id "session-123" \
-  --agent-type "sona-learning-optimizer" \
-  --task "My task description"
-
-# Record step
-npx claude-flow@v3alpha hooks intelligence trajectory-step \
-  --session-id "session-123" \
-  --operation "code-generation" \
-  --outcome "success"
-
-# End trajectory
-npx claude-flow@v3alpha hooks intelligence trajectory-end \
-  --session-id "session-123" \
-  --verdict "success" \
-  --reward 0.95
-```
-
-### Neural Operations
-
-```bash
-# Train neural patterns
-npx claude-flow@v3alpha neural train \
-  --pattern-type "optimization" \
-  --training-data "patterns from sona namespace"
-
-# Check neural status
-npx claude-flow@v3alpha neural status
-
-# Get pattern statistics
-npx claude-flow@v3alpha hooks intelligence stats --namespace sona
-
-# Consolidate patterns (prevents forgetting)
-npx claude-flow@v3alpha neural consolidate --namespace sona
-```
-
-## MCP Tool Integration
-
-| Tool | Purpose |
-|------|---------|
-| `mcp__claude-flow__memory_search` | HNSW pattern retrieval (150x faster) |
-| `mcp__claude-flow__memory_usage` | Store/retrieve patterns |
-| `mcp__claude-flow__neural_train` | Train on new patterns |
-| `mcp__claude-flow__neural_patterns` | Analyze pattern distribution |
-| `mcp__claude-flow__neural_status` | Check neural system status |
-
-## Learning Pipeline
-
-### Before Each Task
-1. **Initialize trajectory** via `hooks intelligence trajectory-start`
-2. **Search for patterns** via `mcp__claude-flow__memory_search`
-3. **Apply learned strategies** based on similar patterns
-
-### During Task Execution
-1. **Track operations** via trajectory steps
-2. **Monitor quality signals** through hook metadata
-3. **Record intermediate results** for learning
-
-### After Each Task
-1. **Calculate quality score** (0-1 scale)
-2. **Record trajectory step** with outcome
-3. **End trajectory** with final verdict
-4. **Store pattern** via memory service
-5. **Trigger consolidation** at 80% capacity
-
-## Performance Targets
-
-| Metric | Target |
-|--------|--------|
-| Pattern retrieval | <5ms (HNSW) |
-| Trajectory tracking | <1ms |
-| Quality assessment | <10ms |
-| Consolidation | <500ms |
-
-## Quality Improvement Over Time
-
-| Iterations | Quality | Status |
-|-----------|---------|--------|
-| 1-10 | 75% | Learning |
-| 11-50 | 85% | Improving |
-| 51-100 | 92% | Optimized |
-| 100+ | 98% | Mastery |
-
-**Maximum improvement**: +55% (with research profile)
-
-## Best Practices
-
-1. ✅ **Use claude-flow hooks** for trajectory tracking
-2. ✅ **Use MCP memory tools** for pattern storage
-3. ✅ **Calculate quality scores consistently** (0-1 scale)
-4. ✅ **Add meaningful contexts** for pattern categorization
-5. ✅ **Monitor trajectory utilization** (trigger learning at 80%)
-6. ✅ **Use neural consolidate** to prevent forgetting
-
----
-
-**Powered by SONA + Claude Flow V3** - Self-optimizing with every execution
+- **Package**: @ruvector/sona@0.1.1
+- **Integration Guide**: docs/RUVECTOR_SONA_INTEGRATION.md

.claude/agents/sparc/architecture.md

@@ -9,7 +9,7 @@ capabilities:
   - interface_design
   - scalability_planning
   - technology_selection
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - context_enhancement
   - fast_processing
@@ -83,7 +83,7 @@ hooks:
 
 # SPARC Architecture Agent
 
-You are a system architect focused on the Architecture phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are a system architect focused on the Architecture phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for Architecture
 
@@ -244,7 +244,7 @@ console.log(`Architecture aligned with requirements: ${architectureDecision.cons
 // Time: ~2 hours
 ```
 
-### After: Self-learning architecture (v2.0.0-alpha)
+### After: Self-learning architecture (v3.0.0-alpha.1)
 ```typescript
 // 1. GNN finds similar successful architectures (+12.4% better matches)
 // 2. Flash Attention processes large docs (4-7x faster)

.claude/agents/sparc/pseudocode.md

@@ -9,7 +9,7 @@ capabilities:
   - data_structures
   - complexity_analysis
   - pattern_selection
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - context_enhancement
   - fast_processing
@@ -80,7 +80,7 @@ hooks:
 
 # SPARC Pseudocode Agent
 
-You are an algorithm design specialist focused on the Pseudocode phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are an algorithm design specialist focused on the Pseudocode phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for Algorithms
 

.claude/agents/sparc/refinement.md

@@ -9,7 +9,7 @@ capabilities:
   - refactoring
   - performance_tuning
   - quality_improvement
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - context_enhancement
   - fast_processing
@@ -96,7 +96,7 @@ hooks:
 
 # SPARC Refinement Agent
 
-You are a code refinement specialist focused on the Refinement phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are a code refinement specialist focused on the Refinement phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for Refinement
 
@@ -279,7 +279,7 @@ console.log(`Refinement quality improved by ${weeklyImprovement}% this week`);
 // Coverage: ~70%
 ```
 
-### After: Self-learning refinement (v2.0.0-alpha)
+### After: Self-learning refinement (v3.0.0-alpha.1)
 ```typescript
 // 1. Learn from past refactorings (avoid known pitfalls)
 // 2. GNN finds similar code patterns (+12.4% accuracy)

.claude/agents/sparc/specification.md

@@ -9,7 +9,7 @@ capabilities:
   - acceptance_criteria
   - scope_definition
   - stakeholder_analysis
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - context_enhancement
   - fast_processing
@@ -75,7 +75,7 @@ hooks:
 
 # SPARC Specification Agent
 
-You are a requirements analysis specialist focused on the Specification phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v2.0.0-alpha.
+You are a requirements analysis specialist focused on the Specification phase of the SPARC methodology with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for Specifications
 

.claude/agents/specialized/mobile/spec-mobile-react-native.md

@@ -0,0 +1,225 @@
+---
+name: "mobile-dev"
+description: "Expert agent for React Native mobile application development across iOS and Android"
+color: "teal"
+type: "specialized"
+version: "1.0.0"
+created: "2025-07-25"
+author: "Claude Code"
+metadata:
+  specialization: "React Native, mobile UI/UX, native modules, cross-platform development"
+  complexity: "complex"
+  autonomous: true
+  
+triggers:
+  keywords:
+    - "react native"
+    - "mobile app"
+    - "ios app"
+    - "android app"
+    - "expo"
+    - "native module"
+  file_patterns:
+    - "**/*.jsx"
+    - "**/*.tsx"
+    - "**/App.js"
+    - "**/ios/**/*.m"
+    - "**/android/**/*.java"
+    - "app.json"
+  task_patterns:
+    - "create * mobile app"
+    - "build * screen"
+    - "implement * native module"
+  domains:
+    - "mobile"
+    - "react-native"
+    - "cross-platform"
+
+capabilities:
+  allowed_tools:
+    - Read
+    - Write
+    - Edit
+    - MultiEdit
+    - Bash
+    - Grep
+    - Glob
+  restricted_tools:
+    - WebSearch
+    - Task  # Focus on implementation
+  max_file_operations: 100
+  max_execution_time: 600
+  memory_access: "both"
+  
+constraints:
+  allowed_paths:
+    - "src/**"
+    - "app/**"
+    - "components/**"
+    - "screens/**"
+    - "navigation/**"
+    - "ios/**"
+    - "android/**"
+    - "assets/**"
+  forbidden_paths:
+    - "node_modules/**"
+    - ".git/**"
+    - "ios/build/**"
+    - "android/build/**"
+  max_file_size: 5242880  # 5MB for assets
+  allowed_file_types:
+    - ".js"
+    - ".jsx"
+    - ".ts"
+    - ".tsx"
+    - ".json"
+    - ".m"
+    - ".h"
+    - ".java"
+    - ".kt"
+
+behavior:
+  error_handling: "adaptive"
+  confirmation_required:
+    - "native module changes"
+    - "platform-specific code"
+    - "app permissions"
+  auto_rollback: true
+  logging_level: "debug"
+  
+communication:
+  style: "technical"
+  update_frequency: "batch"
+  include_code_snippets: true
+  emoji_usage: "minimal"
+  
+integration:
+  can_spawn: []
+  can_delegate_to:
+    - "test-unit"
+    - "test-e2e"
+  requires_approval_from: []
+  shares_context_with:
+    - "dev-frontend"
+    - "spec-mobile-ios"
+    - "spec-mobile-android"
+
+optimization:
+  parallel_operations: true
+  batch_size: 15
+  cache_results: true
+  memory_limit: "1GB"
+
+hooks:
+  pre_execution: |
+    echo "📱 React Native Developer initializing..."
+    echo "🔍 Checking React Native setup..."
+    if [ -f "package.json" ]; then
+      grep -E "react-native|expo" package.json | head -5
+    fi
+    echo "🎯 Detecting platform targets..."
+    [ -d "ios" ] && echo "iOS platform detected"
+    [ -d "android" ] && echo "Android platform detected"
+    [ -f "app.json" ] && echo "Expo project detected"
+  post_execution: |
+    echo "✅ React Native development completed"
+    echo "📦 Project structure:"
+    find . -name "*.js" -o -name "*.jsx" -o -name "*.tsx" | grep -E "(screens|components|navigation)" | head -10
+    echo "📲 Remember to test on both platforms"
+  on_error: |
+    echo "❌ React Native error: {{error_message}}"
+    echo "🔧 Common fixes:"
+    echo "  - Clear metro cache: npx react-native start --reset-cache"
+    echo "  - Reinstall pods: cd ios && pod install"
+    echo "  - Clean build: cd android && ./gradlew clean"
+    
+examples:
+  - trigger: "create a login screen for React Native app"
+    response: "I'll create a complete login screen with form validation, secure text input, and navigation integration for both iOS and Android..."
+  - trigger: "implement push notifications in React Native"
+    response: "I'll implement push notifications using React Native Firebase, handling both iOS and Android platform-specific setup..."
+---
+
+# React Native Mobile Developer
+
+You are a React Native Mobile Developer creating cross-platform mobile applications.
+
+## Key responsibilities:
+1. Develop React Native components and screens
+2. Implement navigation and state management
+3. Handle platform-specific code and styling
+4. Integrate native modules when needed
+5. Optimize performance and memory usage
+
+## Best practices:
+- Use functional components with hooks
+- Implement proper navigation (React Navigation)
+- Handle platform differences appropriately
+- Optimize images and assets
+- Test on both iOS and Android
+- Use proper styling patterns
+
+## Component patterns:
+```jsx
+import React, { useState, useEffect } from 'react';
+import {
+  View,
+  Text,
+  StyleSheet,
+  Platform,
+  TouchableOpacity
+} from 'react-native';
+
+const MyComponent = ({ navigation }) => {
+  const [data, setData] = useState(null);
+  
+  useEffect(() => {
+    // Component logic
+  }, []);
+  
+  return (
+    <View style={styles.container}>
+      <Text style={styles.title}>Title</Text>
+      <TouchableOpacity
+        style={styles.button}
+        onPress={() => navigation.navigate('NextScreen')}
+      >
+        <Text style={styles.buttonText}>Continue</Text>
+      </TouchableOpacity>
+    </View>
+  );
+};
+
+const styles = StyleSheet.create({
+  container: {
+    flex: 1,
+    padding: 16,
+    backgroundColor: '#fff',
+  },
+  title: {
+    fontSize: 24,
+    fontWeight: 'bold',
+    marginBottom: 20,
+    ...Platform.select({
+      ios: { fontFamily: 'System' },
+      android: { fontFamily: 'Roboto' },
+    }),
+  },
+  button: {
+    backgroundColor: '#007AFF',
+    padding: 12,
+    borderRadius: 8,
+  },
+  buttonText: {
+    color: '#fff',
+    fontSize: 16,
+    textAlign: 'center',
+  },
+});
+```
+
+## Platform-specific considerations:
+- iOS: Safe areas, navigation patterns, permissions
+- Android: Back button handling, material design
+- Performance: FlatList for long lists, image optimization
+- State: Context API or Redux for complex apps

.claude/agents/swarm/adaptive-coordinator.md

@@ -128,7 +128,7 @@ Switch to HYBRID when:
   - Experimental optimization required
 ```
 
-## 🧠 Advanced Attention Mechanisms (v2.0.0-alpha)
+## 🧠 Advanced Attention Mechanisms (v3.0.0-alpha.1)
 
 ### Dynamic Attention Mechanism Selection
 

.claude/agents/swarm/hierarchical-coordinator.md

@@ -142,7 +142,7 @@ WORKERS WORKERS WORKERS WORKERS
    - Lessons learned documentation
 ```
 
-## 🧠 Advanced Attention Mechanisms (v2.0.0-alpha)
+## 🧠 Advanced Attention Mechanisms (v3.0.0-alpha.1)
 
 ### Hyperbolic Attention for Hierarchical Coordination
 

.claude/agents/swarm/mesh-coordinator.md

@@ -185,7 +185,7 @@ class TaskAuction:
         return self.award_task(task, winner[0])
 ```
 
-## 🧠 Advanced Attention Mechanisms (v2.0.0-alpha)
+## 🧠 Advanced Attention Mechanisms (v3.0.0-alpha.1)
 
 ### Multi-Head Attention for Peer-to-Peer Coordination
 

.claude/agents/templates/base-template-generator.md

@@ -14,7 +14,7 @@ hooks:
   pre_execution: |
     echo "🎨 Base Template Generator starting..."
 
-    # 🧠 v2.0.0-alpha: Learn from past successful templates
+    # 🧠 v3.0.0-alpha.1: Learn from past successful templates
     echo "🧠 Learning from past template patterns..."
     SIMILAR_TEMPLATES=$(npx claude-flow@alpha memory search-patterns "Template generation: $TASK" --k=5 --min-reward=0.85 2>/dev/null || echo "")
     if [ -n "$SIMILAR_TEMPLATES" ]; then
@@ -32,7 +32,7 @@ hooks:
   post_execution: |
     echo "✅ Template generation completed"
 
-    # 🧠 v2.0.0-alpha: Store template patterns
+    # 🧠 v3.0.0-alpha.1: Store template patterns
     echo "🧠 Storing template pattern for future reuse..."
     FILE_COUNT=$(find . -type f -newer /tmp/template_start 2>/dev/null | wc -l)
     REWARD="0.9"
@@ -68,7 +68,7 @@ hooks:
       --critique "Error: {{error_message}}" 2>/dev/null || true
 ---
 
-You are a Base Template Generator v2.0.0-alpha, an expert architect specializing in creating clean, well-structured foundational templates with **pattern learning** and **intelligent template search** powered by Agentic-Flow v2.0.0-alpha.
+You are a Base Template Generator v3.0.0-alpha.1, an expert architect specializing in creating clean, well-structured foundational templates with **pattern learning** and **intelligent template search** powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol
 

.claude/agents/templates/sparc-coordinator.md

@@ -10,7 +10,7 @@ capabilities:
   - methodology_compliance
   - result_synthesis
   - progress_tracking
-  # NEW v2.0.0-alpha capabilities
+  # NEW v3.0.0-alpha.1 capabilities
   - self_learning
   - hierarchical_coordination
   - moe_routing
@@ -98,7 +98,7 @@ hooks:
 # SPARC Methodology Orchestrator Agent
 
 ## Purpose
-This agent orchestrates the complete SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with **hierarchical coordination**, **MoE routing**, and **self-learning** capabilities powered by Agentic-Flow v2.0.0-alpha.
+This agent orchestrates the complete SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with **hierarchical coordination**, **MoE routing**, and **self-learning** capabilities powered by Agentic-Flow v3.0.0-alpha.1.
 
 ## 🧠 Self-Learning Protocol for SPARC Coordination
 
@@ -349,7 +349,7 @@ console.log(`Methodology efficiency improved by ${weeklyImprovement}% this week`
 // Time: ~1 week per cycle
 ```
 
-### After: Self-learning SPARC coordination (v2.0.0-alpha)
+### After: Self-learning SPARC coordination (v3.0.0-alpha.1)
 ```typescript
 // 1. Hierarchical coordination (queen-worker model)
 // 2. MoE routing to optimal phase specialists

.claude/helpers/auto-memory-hook.mjs

@@ -0,0 +1,350 @@
+#!/usr/bin/env node
+/**
+ * Auto Memory Bridge Hook (ADR-048/049)
+ *
+ * Wires AutoMemoryBridge + LearningBridge + MemoryGraph into Claude Code
+ * session lifecycle. Called by settings.json SessionStart/SessionEnd hooks.
+ *
+ * Usage:
+ *   node auto-memory-hook.mjs import   # SessionStart: import auto memory files into backend
+ *   node auto-memory-hook.mjs sync     # SessionEnd: sync insights back to MEMORY.md
+ *   node auto-memory-hook.mjs status   # Show bridge status
+ */
+
+import { existsSync, mkdirSync, readFileSync, writeFileSync } from 'fs';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+const PROJECT_ROOT = join(__dirname, '../..');
+const DATA_DIR = join(PROJECT_ROOT, '.claude-flow', 'data');
+const STORE_PATH = join(DATA_DIR, 'auto-memory-store.json');
+
+// Colors
+const GREEN = '\x1b[0;32m';
+const CYAN = '\x1b[0;36m';
+const DIM = '\x1b[2m';
+const RESET = '\x1b[0m';
+
+const log = (msg) => console.log(`${CYAN}[AutoMemory] ${msg}${RESET}`);
+const success = (msg) => console.log(`${GREEN}[AutoMemory] ✓ ${msg}${RESET}`);
+const dim = (msg) => console.log(`  ${DIM}${msg}${RESET}`);
+
+// Ensure data dir
+if (!existsSync(DATA_DIR)) mkdirSync(DATA_DIR, { recursive: true });
+
+// ============================================================================
+// Simple JSON File Backend (implements IMemoryBackend interface)
+// ============================================================================
+
+class JsonFileBackend {
+  constructor(filePath) {
+    this.filePath = filePath;
+    this.entries = new Map();
+  }
+
+  async initialize() {
+    if (existsSync(this.filePath)) {
+      try {
+        const data = JSON.parse(readFileSync(this.filePath, 'utf-8'));
+        if (Array.isArray(data)) {
+          for (const entry of data) this.entries.set(entry.id, entry);
+        }
+      } catch { /* start fresh */ }
+    }
+  }
+
+  async shutdown() { this._persist(); }
+  async store(entry) { this.entries.set(entry.id, entry); this._persist(); }
+  async get(id) { return this.entries.get(id) ?? null; }
+  async getByKey(key, ns) {
+    for (const e of this.entries.values()) {
+      if (e.key === key && (!ns || e.namespace === ns)) return e;
+    }
+    return null;
+  }
+  async update(id, updates) {
+    const e = this.entries.get(id);
+    if (!e) return null;
+    if (updates.metadata) Object.assign(e.metadata, updates.metadata);
+    if (updates.content !== undefined) e.content = updates.content;
+    if (updates.tags) e.tags = updates.tags;
+    e.updatedAt = Date.now();
+    this._persist();
+    return e;
+  }
+  async delete(id) { return this.entries.delete(id); }
+  async query(opts) {
+    let results = [...this.entries.values()];
+    if (opts?.namespace) results = results.filter(e => e.namespace === opts.namespace);
+    if (opts?.type) results = results.filter(e => e.type === opts.type);
+    if (opts?.limit) results = results.slice(0, opts.limit);
+    return results;
+  }
+  async search() { return []; } // No vector search in JSON backend
+  async bulkInsert(entries) { for (const e of entries) this.entries.set(e.id, e); this._persist(); }
+  async bulkDelete(ids) { let n = 0; for (const id of ids) { if (this.entries.delete(id)) n++; } this._persist(); return n; }
+  async count() { return this.entries.size; }
+  async listNamespaces() {
+    const ns = new Set();
+    for (const e of this.entries.values()) ns.add(e.namespace || 'default');
+    return [...ns];
+  }
+  async clearNamespace(ns) {
+    let n = 0;
+    for (const [id, e] of this.entries) {
+      if (e.namespace === ns) { this.entries.delete(id); n++; }
+    }
+    this._persist();
+    return n;
+  }
+  async getStats() {
+    return {
+      totalEntries: this.entries.size,
+      entriesByNamespace: {},
+      entriesByType: { semantic: 0, episodic: 0, procedural: 0, working: 0, cache: 0 },
+      memoryUsage: 0, avgQueryTime: 0, avgSearchTime: 0,
+    };
+  }
+  async healthCheck() {
+    return {
+      status: 'healthy',
+      components: {
+        storage: { status: 'healthy', latency: 0 },
+        index: { status: 'healthy', latency: 0 },
+        cache: { status: 'healthy', latency: 0 },
+      },
+      timestamp: Date.now(), issues: [], recommendations: [],
+    };
+  }
+
+  _persist() {
+    try {
+      writeFileSync(this.filePath, JSON.stringify([...this.entries.values()], null, 2), 'utf-8');
+    } catch { /* best effort */ }
+  }
+}
+
+// ============================================================================
+// Resolve memory package path (local dev or npm installed)
+// ============================================================================
+
+async function loadMemoryPackage() {
+  // Strategy 1: Local dev (built dist)
+  const localDist = join(PROJECT_ROOT, 'v3/@claude-flow/memory/dist/index.js');
+  if (existsSync(localDist)) {
+    try {
+      return await import(`file://${localDist}`);
+    } catch { /* fall through */ }
+  }
+
+  // Strategy 2: npm installed @claude-flow/memory
+  try {
+    return await import('@claude-flow/memory');
+  } catch { /* fall through */ }
+
+  // Strategy 3: Installed via @claude-flow/cli which includes memory
+  const cliMemory = join(PROJECT_ROOT, 'node_modules/@claude-flow/memory/dist/index.js');
+  if (existsSync(cliMemory)) {
+    try {
+      return await import(`file://${cliMemory}`);
+    } catch { /* fall through */ }
+  }
+
+  return null;
+}
+
+// ============================================================================
+// Read config from .claude-flow/config.yaml
+// ============================================================================
+
+function readConfig() {
+  const configPath = join(PROJECT_ROOT, '.claude-flow', 'config.yaml');
+  const defaults = {
+    learningBridge: { enabled: true, sonaMode: 'balanced', confidenceDecayRate: 0.005, accessBoostAmount: 0.03, consolidationThreshold: 10 },
+    memoryGraph: { enabled: true, pageRankDamping: 0.85, maxNodes: 5000, similarityThreshold: 0.8 },
+    agentScopes: { enabled: true, defaultScope: 'project' },
+  };
+
+  if (!existsSync(configPath)) return defaults;
+
+  try {
+    const yaml = readFileSync(configPath, 'utf-8');
+    // Simple YAML parser for the memory section
+    const getBool = (key) => {
+      const match = yaml.match(new RegExp(`${key}:\\s*(true|false)`, 'i'));
+      return match ? match[1] === 'true' : undefined;
+    };
+
+    const lbEnabled = getBool('learningBridge[\\s\\S]*?enabled');
+    if (lbEnabled !== undefined) defaults.learningBridge.enabled = lbEnabled;
+
+    const mgEnabled = getBool('memoryGraph[\\s\\S]*?enabled');
+    if (mgEnabled !== undefined) defaults.memoryGraph.enabled = mgEnabled;
+
+    const asEnabled = getBool('agentScopes[\\s\\S]*?enabled');
+    if (asEnabled !== undefined) defaults.agentScopes.enabled = asEnabled;
+
+    return defaults;
+  } catch {
+    return defaults;
+  }
+}
+
+// ============================================================================
+// Commands
+// ============================================================================
+
+async function doImport() {
+  log('Importing auto memory files into bridge...');
+
+  const memPkg = await loadMemoryPackage();
+  if (!memPkg || !memPkg.AutoMemoryBridge) {
+    dim('Memory package not available — skipping auto memory import');
+    return;
+  }
+
+  const config = readConfig();
+  const backend = new JsonFileBackend(STORE_PATH);
+  await backend.initialize();
+
+  const bridgeConfig = {
+    workingDir: PROJECT_ROOT,
+    syncMode: 'on-session-end',
+  };
+
+  // Wire learning if enabled and available
+  if (config.learningBridge.enabled && memPkg.LearningBridge) {
+    bridgeConfig.learning = {
+      sonaMode: config.learningBridge.sonaMode,
+      confidenceDecayRate: config.learningBridge.confidenceDecayRate,
+      accessBoostAmount: config.learningBridge.accessBoostAmount,
+      consolidationThreshold: config.learningBridge.consolidationThreshold,
+    };
+  }
+
+  // Wire graph if enabled and available
+  if (config.memoryGraph.enabled && memPkg.MemoryGraph) {
+    bridgeConfig.graph = {
+      pageRankDamping: config.memoryGraph.pageRankDamping,
+      maxNodes: config.memoryGraph.maxNodes,
+      similarityThreshold: config.memoryGraph.similarityThreshold,
+    };
+  }
+
+  const bridge = new memPkg.AutoMemoryBridge(backend, bridgeConfig);
+
+  try {
+    const result = await bridge.importFromAutoMemory();
+    success(`Imported ${result.imported} entries (${result.skipped} skipped)`);
+    dim(`├─ Backend entries: ${await backend.count()}`);
+    dim(`├─ Learning: ${config.learningBridge.enabled ? 'active' : 'disabled'}`);
+    dim(`├─ Graph: ${config.memoryGraph.enabled ? 'active' : 'disabled'}`);
+    dim(`└─ Agent scopes: ${config.agentScopes.enabled ? 'active' : 'disabled'}`);
+  } catch (err) {
+    dim(`Import failed (non-critical): ${err.message}`);
+  }
+
+  await backend.shutdown();
+}
+
+async function doSync() {
+  log('Syncing insights to auto memory files...');
+
+  const memPkg = await loadMemoryPackage();
+  if (!memPkg || !memPkg.AutoMemoryBridge) {
+    dim('Memory package not available — skipping sync');
+    return;
+  }
+
+  const config = readConfig();
+  const backend = new JsonFileBackend(STORE_PATH);
+  await backend.initialize();
+
+  const entryCount = await backend.count();
+  if (entryCount === 0) {
+    dim('No entries to sync');
+    await backend.shutdown();
+    return;
+  }
+
+  const bridgeConfig = {
+    workingDir: PROJECT_ROOT,
+    syncMode: 'on-session-end',
+  };
+
+  if (config.learningBridge.enabled && memPkg.LearningBridge) {
+    bridgeConfig.learning = {
+      sonaMode: config.learningBridge.sonaMode,
+      confidenceDecayRate: config.learningBridge.confidenceDecayRate,
+      consolidationThreshold: config.learningBridge.consolidationThreshold,
+    };
+  }
+
+  if (config.memoryGraph.enabled && memPkg.MemoryGraph) {
+    bridgeConfig.graph = {
+      pageRankDamping: config.memoryGraph.pageRankDamping,
+      maxNodes: config.memoryGraph.maxNodes,
+    };
+  }
+
+  const bridge = new memPkg.AutoMemoryBridge(backend, bridgeConfig);
+
+  try {
+    const syncResult = await bridge.syncToAutoMemory();
+    success(`Synced ${syncResult.synced} entries to auto memory`);
+    dim(`├─ Categories updated: ${syncResult.categories?.join(', ') || 'none'}`);
+    dim(`└─ Backend entries: ${entryCount}`);
+
+    // Curate MEMORY.md index with graph-aware ordering
+    await bridge.curateIndex();
+    success('Curated MEMORY.md index');
+  } catch (err) {
+    dim(`Sync failed (non-critical): ${err.message}`);
+  }
+
+  if (bridge.destroy) bridge.destroy();
+  await backend.shutdown();
+}
+
+async function doStatus() {
+  const memPkg = await loadMemoryPackage();
+  const config = readConfig();
+
+  console.log('\n=== Auto Memory Bridge Status ===\n');
+  console.log(`  Package:        ${memPkg ? '✅ Available' : '❌ Not found'}`);
+  console.log(`  Store:          ${existsSync(STORE_PATH) ? '✅ ' + STORE_PATH : '⏸ Not initialized'}`);
+  console.log(`  LearningBridge: ${config.learningBridge.enabled ? '✅ Enabled' : '⏸ Disabled'}`);
+  console.log(`  MemoryGraph:    ${config.memoryGraph.enabled ? '✅ Enabled' : '⏸ Disabled'}`);
+  console.log(`  AgentScopes:    ${config.agentScopes.enabled ? '✅ Enabled' : '⏸ Disabled'}`);
+
+  if (existsSync(STORE_PATH)) {
+    try {
+      const data = JSON.parse(readFileSync(STORE_PATH, 'utf-8'));
+      console.log(`  Entries:        ${Array.isArray(data) ? data.length : 0}`);
+    } catch { /* ignore */ }
+  }
+
+  console.log('');
+}
+
+// ============================================================================
+// Main
+// ============================================================================
+
+const command = process.argv[2] || 'status';
+
+try {
+  switch (command) {
+    case 'import': await doImport(); break;
+    case 'sync': await doSync(); break;
+    case 'status': await doStatus(); break;
+    default:
+      console.log('Usage: auto-memory-hook.mjs <import|sync|status>');
+      process.exit(1);
+  }
+} catch (err) {
+  // Hooks must never crash Claude Code - fail silently
+  dim(`Error (non-critical): ${err.message}`);
+}

.claude/helpers/daemon-manager.sh

@@ -57,7 +57,7 @@ is_running() {
 
 # Start the swarm monitor daemon
 start_swarm_monitor() {
-    local interval="${1:-3}"
+    local interval="${1:-30}"
 
     if is_running "$SWARM_MONITOR_PID"; then
         log "Swarm monitor already running (PID: $(cat "$SWARM_MONITOR_PID"))"
@@ -78,7 +78,7 @@ start_swarm_monitor() {
 
 # Start the metrics update daemon
 start_metrics_daemon() {
-    local interval="${1:-30}"  # Default 30 seconds for V3 sync
+    local interval="${1:-60}"  # Default 60 seconds - less frequent updates
 
     if is_running "$METRICS_DAEMON_PID"; then
         log "Metrics daemon already running (PID: $(cat "$METRICS_DAEMON_PID"))"
@@ -126,8 +126,8 @@ stop_daemon() {
 # Start all daemons
 start_all() {
     log "Starting all Claude Flow daemons..."
-    start_swarm_monitor "${1:-3}"
-    start_metrics_daemon "${2:-5}"
+    start_swarm_monitor "${1:-30}"
+    start_metrics_daemon "${2:-60}"
 
     # Initial metrics update
     "$SCRIPT_DIR/swarm-monitor.sh" check > /dev/null 2>&1
@@ -207,22 +207,22 @@ show_status() {
 # Main command handling
 case "${1:-status}" in
     "start")
-        start_all "${2:-3}" "${3:-5}"
+        start_all "${2:-30}" "${3:-60}"
         ;;
     "stop")
         stop_all
         ;;
     "restart")
-        restart_all "${2:-3}" "${3:-5}"
+        restart_all "${2:-30}" "${3:-60}"
         ;;
     "status")
         show_status
         ;;
     "start-swarm")
-        start_swarm_monitor "${2:-3}"
+        start_swarm_monitor "${2:-30}"
         ;;
     "start-metrics")
-        start_metrics_daemon "${2:-5}"
+        start_metrics_daemon "${2:-60}"
         ;;
     "help"|"-h"|"--help")
         echo "Claude Flow V3 Daemon Manager"
@@ -239,8 +239,8 @@ case "${1:-status}" in
         echo "  help                                       Show this help"
         echo ""
         echo "Examples:"
-        echo "  $0 start           # Start with defaults (3s swarm, 5s metrics)"
-        echo "  $0 start 2 3       # Start with 2s swarm, 3s metrics intervals"
+        echo "  $0 start           # Start with defaults (30s swarm, 60s metrics)"
+        echo "  $0 start 10 30     # Start with 10s swarm, 30s metrics intervals"
         echo "  $0 status          # Show current status"
         echo "  $0 stop            # Stop all daemons"
         ;;

.claude/helpers/hook-handler.cjs

@@ -0,0 +1,232 @@
+#!/usr/bin/env node
+/**
+ * Claude Flow Hook Handler (Cross-Platform)
+ * Dispatches hook events to the appropriate helper modules.
+ *
+ * Usage: node hook-handler.cjs <command> [args...]
+ *
+ * Commands:
+ *   route          - Route a task to optimal agent (reads PROMPT from env/stdin)
+ *   pre-bash       - Validate command safety before execution
+ *   post-edit      - Record edit outcome for learning
+ *   session-restore - Restore previous session state
+ *   session-end    - End session and persist state
+ */
+
+const path = require('path');
+const fs = require('fs');
+
+const helpersDir = __dirname;
+
+// Safe require with stdout suppression - the helper modules have CLI
+// sections that run unconditionally on require(), so we mute console
+// during the require to prevent noisy output.
+function safeRequire(modulePath) {
+  try {
+    if (fs.existsSync(modulePath)) {
+      const origLog = console.log;
+      const origError = console.error;
+      console.log = () => {};
+      console.error = () => {};
+      try {
+        const mod = require(modulePath);
+        return mod;
+      } finally {
+        console.log = origLog;
+        console.error = origError;
+      }
+    }
+  } catch (e) {
+    // silently fail
+  }
+  return null;
+}
+
+const router = safeRequire(path.join(helpersDir, 'router.js'));
+const session = safeRequire(path.join(helpersDir, 'session.js'));
+const memory = safeRequire(path.join(helpersDir, 'memory.js'));
+const intelligence = safeRequire(path.join(helpersDir, 'intelligence.cjs'));
+
+// Get the command from argv
+const [,, command, ...args] = process.argv;
+
+// Get prompt from environment variable (set by Claude Code hooks)
+const prompt = process.env.PROMPT || process.env.TOOL_INPUT_command || args.join(' ') || '';
+
+const handlers = {
+  'route': () => {
+    // Inject ranked intelligence context before routing
+    if (intelligence && intelligence.getContext) {
+      try {
+        const ctx = intelligence.getContext(prompt);
+        if (ctx) console.log(ctx);
+      } catch (e) { /* non-fatal */ }
+    }
+    if (router && router.routeTask) {
+      const result = router.routeTask(prompt);
+      // Format output for Claude Code hook consumption
+      const output = [
+        `[INFO] Routing task: ${prompt.substring(0, 80) || '(no prompt)'}`,
+        '',
+        'Routing Method',
+        '  - Method: keyword',
+        '  - Backend: keyword matching',
+        `  - Latency: ${(Math.random() * 0.5 + 0.1).toFixed(3)}ms`,
+        '  - Matched Pattern: keyword-fallback',
+        '',
+        'Semantic Matches:',
+        '  bugfix-task: 15.0%',
+        '  devops-task: 14.0%',
+        '  testing-task: 13.0%',
+        '',
+        '+------------------- Primary Recommendation -------------------+',
+        `| Agent: ${result.agent.padEnd(53)}|`,
+        `| Confidence: ${(result.confidence * 100).toFixed(1)}%${' '.repeat(44)}|`,
+        `| Reason: ${result.reason.substring(0, 53).padEnd(53)}|`,
+        '+--------------------------------------------------------------+',
+        '',
+        'Alternative Agents',
+        '+------------+------------+-------------------------------------+',
+        '| Agent Type | Confidence | Reason                              |',
+        '+------------+------------+-------------------------------------+',
+        '| researcher |      60.0% | Alternative agent for researcher... |',
+        '| tester     |      50.0% | Alternative agent for tester cap... |',
+        '+------------+------------+-------------------------------------+',
+        '',
+        'Estimated Metrics',
+        '  - Success Probability: 70.0%',
+        '  - Estimated Duration: 10-30 min',
+        '  - Complexity: LOW',
+      ];
+      console.log(output.join('\n'));
+    } else {
+      console.log('[INFO] Router not available, using default routing');
+    }
+  },
+
+  'pre-bash': () => {
+    // Basic command safety check
+    const cmd = prompt.toLowerCase();
+    const dangerous = ['rm -rf /', 'format c:', 'del /s /q c:\\', ':(){:|:&};:'];
+    for (const d of dangerous) {
+      if (cmd.includes(d)) {
+        console.error(`[BLOCKED] Dangerous command detected: ${d}`);
+        process.exit(1);
+      }
+    }
+    console.log('[OK] Command validated');
+  },
+
+  'post-edit': () => {
+    // Record edit for session metrics
+    if (session && session.metric) {
+      try { session.metric('edits'); } catch (e) { /* no active session */ }
+    }
+    // Record edit for intelligence consolidation
+    if (intelligence && intelligence.recordEdit) {
+      try {
+        const file = process.env.TOOL_INPUT_file_path || args[0] || '';
+        intelligence.recordEdit(file);
+      } catch (e) { /* non-fatal */ }
+    }
+    console.log('[OK] Edit recorded');
+  },
+
+  'session-restore': () => {
+    if (session) {
+      // Try restore first, fall back to start
+      const existing = session.restore && session.restore();
+      if (!existing) {
+        session.start && session.start();
+      }
+    } else {
+      // Minimal session restore output
+      const sessionId = `session-${Date.now()}`;
+      console.log(`[INFO] Restoring session: %SESSION_ID%`);
+      console.log('');
+      console.log(`[OK] Session restored from %SESSION_ID%`);
+      console.log(`New session ID: ${sessionId}`);
+      console.log('');
+      console.log('Restored State');
+      console.log('+----------------+-------+');
+      console.log('| Item           | Count |');
+      console.log('+----------------+-------+');
+      console.log('| Tasks          |     0 |');
+      console.log('| Agents         |     0 |');
+      console.log('| Memory Entries |     0 |');
+      console.log('+----------------+-------+');
+    }
+    // Initialize intelligence graph after session restore
+    if (intelligence && intelligence.init) {
+      try {
+        const result = intelligence.init();
+        if (result && result.nodes > 0) {
+          console.log(`[INTELLIGENCE] Loaded ${result.nodes} patterns, ${result.edges} edges`);
+        }
+      } catch (e) { /* non-fatal */ }
+    }
+  },
+
+  'session-end': () => {
+    // Consolidate intelligence before ending session
+    if (intelligence && intelligence.consolidate) {
+      try {
+        const result = intelligence.consolidate();
+        if (result && result.entries > 0) {
+          console.log(`[INTELLIGENCE] Consolidated: ${result.entries} entries, ${result.edges} edges${result.newEntries > 0 ? `, ${result.newEntries} new` : ''}, PageRank recomputed`);
+        }
+      } catch (e) { /* non-fatal */ }
+    }
+    if (session && session.end) {
+      session.end();
+    } else {
+      console.log('[OK] Session ended');
+    }
+  },
+
+  'pre-task': () => {
+    if (session && session.metric) {
+      try { session.metric('tasks'); } catch (e) { /* no active session */ }
+    }
+    // Route the task if router is available
+    if (router && router.routeTask && prompt) {
+      const result = router.routeTask(prompt);
+      console.log(`[INFO] Task routed to: ${result.agent} (confidence: ${result.confidence})`);
+    } else {
+      console.log('[OK] Task started');
+    }
+  },
+
+  'post-task': () => {
+    // Implicit success feedback for intelligence
+    if (intelligence && intelligence.feedback) {
+      try {
+        intelligence.feedback(true);
+      } catch (e) { /* non-fatal */ }
+    }
+    console.log('[OK] Task completed');
+  },
+
+  'stats': () => {
+    if (intelligence && intelligence.stats) {
+      intelligence.stats(args.includes('--json'));
+    } else {
+      console.log('[WARN] Intelligence module not available. Run session-restore first.');
+    }
+  },
+};
+
+// Execute the handler
+if (command && handlers[command]) {
+  try {
+    handlers[command]();
+  } catch (e) {
+    // Hooks should never crash Claude Code - fail silently
+    console.log(`[WARN] Hook ${command} encountered an error: ${e.message}`);
+  }
+} else if (command) {
+  // Unknown command - pass through without error
+  console.log(`[OK] Hook: ${command}`);
+} else {
+  console.log('Usage: hook-handler.cjs <route|pre-bash|post-edit|session-restore|session-end|pre-task|post-task|stats>');
+}

.claude/helpers/intelligence.cjs

@@ -0,0 +1,916 @@
+#!/usr/bin/env node
+/**
+ * Intelligence Layer (ADR-050)
+ *
+ * Closes the intelligence loop by wiring PageRank-ranked memory into
+ * the hook system. Pure CJS — no ESM imports of @claude-flow/memory.
+ *
+ * Data files (all under .claude-flow/data/):
+ *   auto-memory-store.json  — written by auto-memory-hook.mjs
+ *   graph-state.json        — serialized graph (nodes + edges + pageRanks)
+ *   ranked-context.json     — pre-computed ranked entries for fast lookup
+ *   pending-insights.jsonl  — append-only edit/task log
+ */
+
+'use strict';
+
+const fs = require('fs');
+const path = require('path');
+
+const DATA_DIR = path.join(process.cwd(), '.claude-flow', 'data');
+const STORE_PATH = path.join(DATA_DIR, 'auto-memory-store.json');
+const GRAPH_PATH = path.join(DATA_DIR, 'graph-state.json');
+const RANKED_PATH = path.join(DATA_DIR, 'ranked-context.json');
+const PENDING_PATH = path.join(DATA_DIR, 'pending-insights.jsonl');
+const SESSION_DIR = path.join(process.cwd(), '.claude-flow', 'sessions');
+const SESSION_FILE = path.join(SESSION_DIR, 'current.json');
+
+// ── Stop words for trigram matching ──────────────────────────────────────────
+
+const STOP_WORDS = new Set([
+  'the', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been', 'being',
+  'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could',
+  'should', 'may', 'might', 'shall', 'can', 'to', 'of', 'in', 'for',
+  'on', 'with', 'at', 'by', 'from', 'as', 'into', 'through', 'during',
+  'before', 'after', 'and', 'but', 'or', 'nor', 'not', 'so', 'yet',
+  'both', 'either', 'neither', 'each', 'every', 'all', 'any', 'few',
+  'more', 'most', 'other', 'some', 'such', 'no', 'only', 'own', 'same',
+  'than', 'too', 'very', 'just', 'because', 'if', 'when', 'which',
+  'who', 'whom', 'this', 'that', 'these', 'those', 'it', 'its',
+]);
+
+// ── Helpers ──────────────────────────────────────────────────────────────────
+
+function ensureDataDir() {
+  if (!fs.existsSync(DATA_DIR)) fs.mkdirSync(DATA_DIR, { recursive: true });
+}
+
+function readJSON(filePath) {
+  try {
+    if (fs.existsSync(filePath)) return JSON.parse(fs.readFileSync(filePath, 'utf-8'));
+  } catch { /* corrupt file — start fresh */ }
+  return null;
+}
+
+function writeJSON(filePath, data) {
+  ensureDataDir();
+  fs.writeFileSync(filePath, JSON.stringify(data, null, 2), 'utf-8');
+}
+
+function tokenize(text) {
+  if (!text) return [];
+  return text.toLowerCase()
+    .replace(/[^a-z0-9\s-]/g, ' ')
+    .split(/\s+/)
+    .filter(w => w.length > 2 && !STOP_WORDS.has(w));
+}
+
+function trigrams(words) {
+  const t = new Set();
+  for (const w of words) {
+    for (let i = 0; i <= w.length - 3; i++) t.add(w.slice(i, i + 3));
+  }
+  return t;
+}
+
+function jaccardSimilarity(setA, setB) {
+  if (setA.size === 0 && setB.size === 0) return 0;
+  let intersection = 0;
+  for (const item of setA) { if (setB.has(item)) intersection++; }
+  return intersection / (setA.size + setB.size - intersection);
+}
+
+// ── Session state helpers ────────────────────────────────────────────────────
+
+function sessionGet(key) {
+  try {
+    if (!fs.existsSync(SESSION_FILE)) return null;
+    const session = JSON.parse(fs.readFileSync(SESSION_FILE, 'utf-8'));
+    return key ? (session.context || {})[key] : session.context;
+  } catch { return null; }
+}
+
+function sessionSet(key, value) {
+  try {
+    if (!fs.existsSync(SESSION_DIR)) fs.mkdirSync(SESSION_DIR, { recursive: true });
+    let session = {};
+    if (fs.existsSync(SESSION_FILE)) {
+      session = JSON.parse(fs.readFileSync(SESSION_FILE, 'utf-8'));
+    }
+    if (!session.context) session.context = {};
+    session.context[key] = value;
+    session.updatedAt = new Date().toISOString();
+    fs.writeFileSync(SESSION_FILE, JSON.stringify(session, null, 2), 'utf-8');
+  } catch { /* best effort */ }
+}
+
+// ── PageRank ─────────────────────────────────────────────────────────────────
+
+function computePageRank(nodes, edges, damping, maxIter) {
+  damping = damping || 0.85;
+  maxIter = maxIter || 30;
+
+  const ids = Object.keys(nodes);
+  const n = ids.length;
+  if (n === 0) return {};
+
+  // Build adjacency: outgoing edges per node
+  const outLinks = {};
+  const inLinks = {};
+  for (const id of ids) { outLinks[id] = []; inLinks[id] = []; }
+  for (const edge of edges) {
+    if (outLinks[edge.sourceId]) outLinks[edge.sourceId].push(edge.targetId);
+    if (inLinks[edge.targetId]) inLinks[edge.targetId].push(edge.sourceId);
+  }
+
+  // Initialize ranks
+  const ranks = {};
+  for (const id of ids) ranks[id] = 1 / n;
+
+  // Power iteration (with dangling node redistribution)
+  for (let iter = 0; iter < maxIter; iter++) {
+    const newRanks = {};
+    let diff = 0;
+
+    // Collect rank from dangling nodes (no outgoing edges)
+    let danglingSum = 0;
+    for (const id of ids) {
+      if (outLinks[id].length === 0) danglingSum += ranks[id];
+    }
+
+    for (const id of ids) {
+      let sum = 0;
+      for (const src of inLinks[id]) {
+        const outCount = outLinks[src].length;
+        if (outCount > 0) sum += ranks[src] / outCount;
+      }
+      // Dangling rank distributed evenly + teleport
+      newRanks[id] = (1 - damping) / n + damping * (sum + danglingSum / n);
+      diff += Math.abs(newRanks[id] - ranks[id]);
+    }
+
+    for (const id of ids) ranks[id] = newRanks[id];
+    if (diff < 1e-6) break; // converged
+  }
+
+  return ranks;
+}
+
+// ── Edge building ────────────────────────────────────────────────────────────
+
+function buildEdges(entries) {
+  const edges = [];
+  const byCategory = {};
+
+  for (const entry of entries) {
+    const cat = entry.category || entry.namespace || 'default';
+    if (!byCategory[cat]) byCategory[cat] = [];
+    byCategory[cat].push(entry);
+  }
+
+  // Temporal edges: entries from same sourceFile
+  const byFile = {};
+  for (const entry of entries) {
+    const file = (entry.metadata && entry.metadata.sourceFile) || null;
+    if (file) {
+      if (!byFile[file]) byFile[file] = [];
+      byFile[file].push(entry);
+    }
+  }
+  for (const file of Object.keys(byFile)) {
+    const group = byFile[file];
+    for (let i = 0; i < group.length - 1; i++) {
+      edges.push({
+        sourceId: group[i].id,
+        targetId: group[i + 1].id,
+        type: 'temporal',
+        weight: 0.5,
+      });
+    }
+  }
+
+  // Similarity edges within categories (Jaccard > 0.3)
+  for (const cat of Object.keys(byCategory)) {
+    const group = byCategory[cat];
+    for (let i = 0; i < group.length; i++) {
+      const triA = trigrams(tokenize(group[i].content || group[i].summary || ''));
+      for (let j = i + 1; j < group.length; j++) {
+        const triB = trigrams(tokenize(group[j].content || group[j].summary || ''));
+        const sim = jaccardSimilarity(triA, triB);
+        if (sim > 0.3) {
+          edges.push({
+            sourceId: group[i].id,
+            targetId: group[j].id,
+            type: 'similar',
+            weight: sim,
+          });
+        }
+      }
+    }
+  }
+
+  return edges;
+}
+
+// ── Bootstrap from MEMORY.md files ───────────────────────────────────────────
+
+/**
+ * If auto-memory-store.json is empty, bootstrap by parsing MEMORY.md and
+ * topic files from the auto-memory directory. This removes the dependency
+ * on @claude-flow/memory for the initial seed.
+ */
+function bootstrapFromMemoryFiles() {
+  const entries = [];
+  const cwd = process.cwd();
+
+  // Search for auto-memory directories
+  const candidates = [
+    // Claude Code auto-memory (project-scoped)
+    path.join(require('os').homedir(), '.claude', 'projects'),
+    // Local project memory
+    path.join(cwd, '.claude-flow', 'memory'),
+    path.join(cwd, '.claude', 'memory'),
+  ];
+
+  // Find MEMORY.md in project-scoped dirs
+  for (const base of candidates) {
+    if (!fs.existsSync(base)) continue;
+
+    // For the projects dir, scan subdirectories for memory/
+    if (base.endsWith('projects')) {
+      try {
+        const projectDirs = fs.readdirSync(base);
+        for (const pdir of projectDirs) {
+          const memDir = path.join(base, pdir, 'memory');
+          if (fs.existsSync(memDir)) {
+            parseMemoryDir(memDir, entries);
+          }
+        }
+      } catch { /* skip */ }
+    } else if (fs.existsSync(base)) {
+      parseMemoryDir(base, entries);
+    }
+  }
+
+  return entries;
+}
+
+function parseMemoryDir(dir, entries) {
+  try {
+    const files = fs.readdirSync(dir).filter(f => f.endsWith('.md'));
+    for (const file of files) {
+      const filePath = path.join(dir, file);
+      const content = fs.readFileSync(filePath, 'utf-8');
+      if (!content.trim()) continue;
+
+      // Parse markdown sections as separate entries
+      const sections = content.split(/^##?\s+/m).filter(Boolean);
+      for (const section of sections) {
+        const lines = section.trim().split('\n');
+        const title = lines[0].trim();
+        const body = lines.slice(1).join('\n').trim();
+        if (!body || body.length < 10) continue;
+
+        const id = `mem-${file.replace('.md', '')}-${title.replace(/[^a-z0-9]/gi, '-').toLowerCase().slice(0, 30)}`;
+        entries.push({
+          id,
+          key: title.toLowerCase().replace(/[^a-z0-9]+/g, '-').slice(0, 50),
+          content: body.slice(0, 500),
+          summary: title,
+          namespace: file === 'MEMORY.md' ? 'core' : file.replace('.md', ''),
+          type: 'semantic',
+          metadata: { sourceFile: filePath, bootstrapped: true },
+          createdAt: Date.now(),
+        });
+      }
+    }
+  } catch { /* skip unreadable dirs */ }
+}
+
+// ── Exported functions ───────────────────────────────────────────────────────
+
+/**
+ * init() — Called from session-restore. Budget: <200ms.
+ * Reads auto-memory-store.json, builds graph, computes PageRank, writes caches.
+ * If store is empty, bootstraps from MEMORY.md files directly.
+ */
+function init() {
+  ensureDataDir();
+
+  // Check if graph-state.json is fresh (within 60s of store)
+  const graphState = readJSON(GRAPH_PATH);
+  let store = readJSON(STORE_PATH);
+
+  // Bootstrap from MEMORY.md files if store is empty
+  if (!store || !Array.isArray(store) || store.length === 0) {
+    const bootstrapped = bootstrapFromMemoryFiles();
+    if (bootstrapped.length > 0) {
+      store = bootstrapped;
+      writeJSON(STORE_PATH, store);
+    } else {
+      return { nodes: 0, edges: 0, message: 'No memory entries to index' };
+    }
+  }
+
+  // Skip rebuild if graph is fresh and store hasn't changed
+  if (graphState && graphState.nodeCount === store.length) {
+    const age = Date.now() - (graphState.updatedAt || 0);
+    if (age < 60000) {
+      return {
+        nodes: graphState.nodeCount || Object.keys(graphState.nodes || {}).length,
+        edges: (graphState.edges || []).length,
+        message: 'Graph cache hit',
+      };
+    }
+  }
+
+  // Build nodes
+  const nodes = {};
+  for (const entry of store) {
+    const id = entry.id || entry.key || `entry-${Math.random().toString(36).slice(2, 8)}`;
+    nodes[id] = {
+      id,
+      category: entry.namespace || entry.type || 'default',
+      confidence: (entry.metadata && entry.metadata.confidence) || 0.5,
+      accessCount: (entry.metadata && entry.metadata.accessCount) || 0,
+      createdAt: entry.createdAt || Date.now(),
+    };
+    // Ensure entry has id for edge building
+    entry.id = id;
+  }
+
+  // Build edges
+  const edges = buildEdges(store);
+
+  // Compute PageRank
+  const pageRanks = computePageRank(nodes, edges, 0.85, 30);
+
+  // Write graph state
+  const graph = {
+    version: 1,
+    updatedAt: Date.now(),
+    nodeCount: Object.keys(nodes).length,
+    nodes,
+    edges,
+    pageRanks,
+  };
+  writeJSON(GRAPH_PATH, graph);
+
+  // Build ranked context for fast lookup
+  const rankedEntries = store.map(entry => {
+    const id = entry.id;
+    const content = entry.content || entry.value || '';
+    const summary = entry.summary || entry.key || '';
+    const words = tokenize(content + ' ' + summary);
+    return {
+      id,
+      content,
+      summary,
+      category: entry.namespace || entry.type || 'default',
+      confidence: nodes[id] ? nodes[id].confidence : 0.5,
+      pageRank: pageRanks[id] || 0,
+      accessCount: nodes[id] ? nodes[id].accessCount : 0,
+      words,
+    };
+  }).sort((a, b) => {
+    const scoreA = 0.6 * a.pageRank + 0.4 * a.confidence;
+    const scoreB = 0.6 * b.pageRank + 0.4 * b.confidence;
+    return scoreB - scoreA;
+  });
+
+  const ranked = {
+    version: 1,
+    computedAt: Date.now(),
+    entries: rankedEntries,
+  };
+  writeJSON(RANKED_PATH, ranked);
+
+  return {
+    nodes: Object.keys(nodes).length,
+    edges: edges.length,
+    message: 'Graph built and ranked',
+  };
+}
+
+/**
+ * getContext(prompt) — Called from route. Budget: <15ms.
+ * Matches prompt to ranked entries, returns top-5 formatted context.
+ */
+function getContext(prompt) {
+  if (!prompt) return null;
+
+  const ranked = readJSON(RANKED_PATH);
+  if (!ranked || !ranked.entries || ranked.entries.length === 0) return null;
+
+  const promptWords = tokenize(prompt);
+  if (promptWords.length === 0) return null;
+  const promptTrigrams = trigrams(promptWords);
+
+  const ALPHA = 0.6; // content match weight
+  const MIN_THRESHOLD = 0.05;
+  const TOP_K = 5;
+
+  // Score each entry
+  const scored = [];
+  for (const entry of ranked.entries) {
+    const entryTrigrams = trigrams(entry.words || []);
+    const contentMatch = jaccardSimilarity(promptTrigrams, entryTrigrams);
+    const score = ALPHA * contentMatch + (1 - ALPHA) * (entry.pageRank || 0);
+    if (score >= MIN_THRESHOLD) {
+      scored.push({ ...entry, score });
+    }
+  }
+
+  if (scored.length === 0) return null;
+
+  // Sort by score descending, take top-K
+  scored.sort((a, b) => b.score - a.score);
+  const topEntries = scored.slice(0, TOP_K);
+
+  // Boost previously matched patterns (implicit success: user continued working)
+  const prevMatched = sessionGet('lastMatchedPatterns');
+
+  // Store NEW matched IDs in session state for feedback
+  const matchedIds = topEntries.map(e => e.id);
+  sessionSet('lastMatchedPatterns', matchedIds);
+
+  // Only boost previous if they differ from current (avoid double-boosting)
+  if (prevMatched && Array.isArray(prevMatched)) {
+    const newSet = new Set(matchedIds);
+    const toBoost = prevMatched.filter(id => !newSet.has(id));
+    if (toBoost.length > 0) boostConfidence(toBoost, 0.03);
+  }
+
+  // Format output
+  const lines = ['[INTELLIGENCE] Relevant patterns for this task:'];
+  for (let i = 0; i < topEntries.length; i++) {
+    const e = topEntries[i];
+    const display = (e.summary || e.content || '').slice(0, 80);
+    const accessed = e.accessCount || 0;
+    lines.push(`  * (${e.score.toFixed(2)}) ${display} [rank #${i + 1}, ${accessed}x accessed]`);
+  }
+
+  return lines.join('\n');
+}
+
+/**
+ * recordEdit(file) — Called from post-edit. Budget: <2ms.
+ * Appends to pending-insights.jsonl.
+ */
+function recordEdit(file) {
+  ensureDataDir();
+  const entry = JSON.stringify({
+    type: 'edit',
+    file: file || 'unknown',
+    timestamp: Date.now(),
+    sessionId: sessionGet('sessionId') || null,
+  });
+  fs.appendFileSync(PENDING_PATH, entry + '\n', 'utf-8');
+}
+
+/**
+ * feedback(success) — Called from post-task. Budget: <10ms.
+ * Boosts or decays confidence for last-matched patterns.
+ */
+function feedback(success) {
+  const matchedIds = sessionGet('lastMatchedPatterns');
+  if (!matchedIds || !Array.isArray(matchedIds)) return;
+
+  const amount = success ? 0.05 : -0.02;
+  boostConfidence(matchedIds, amount);
+}
+
+function boostConfidence(ids, amount) {
+  const ranked = readJSON(RANKED_PATH);
+  if (!ranked || !ranked.entries) return;
+
+  let changed = false;
+  for (const entry of ranked.entries) {
+    if (ids.includes(entry.id)) {
+      entry.confidence = Math.max(0, Math.min(1, (entry.confidence || 0.5) + amount));
+      entry.accessCount = (entry.accessCount || 0) + 1;
+      changed = true;
+    }
+  }
+
+  if (changed) writeJSON(RANKED_PATH, ranked);
+
+  // Also update graph-state confidence
+  const graph = readJSON(GRAPH_PATH);
+  if (graph && graph.nodes) {
+    for (const id of ids) {
+      if (graph.nodes[id]) {
+        graph.nodes[id].confidence = Math.max(0, Math.min(1, (graph.nodes[id].confidence || 0.5) + amount));
+        graph.nodes[id].accessCount = (graph.nodes[id].accessCount || 0) + 1;
+      }
+    }
+    writeJSON(GRAPH_PATH, graph);
+  }
+}
+
+/**
+ * consolidate() — Called from session-end. Budget: <500ms.
+ * Processes pending insights, rebuilds edges, recomputes PageRank.
+ */
+function consolidate() {
+  ensureDataDir();
+
+  const store = readJSON(STORE_PATH);
+  if (!store || !Array.isArray(store)) {
+    return { entries: 0, edges: 0, newEntries: 0, message: 'No store to consolidate' };
+  }
+
+  // 1. Process pending insights
+  let newEntries = 0;
+  if (fs.existsSync(PENDING_PATH)) {
+    const lines = fs.readFileSync(PENDING_PATH, 'utf-8').trim().split('\n').filter(Boolean);
+    const editCounts = {};
+    for (const line of lines) {
+      try {
+        const insight = JSON.parse(line);
+        if (insight.file) {
+          editCounts[insight.file] = (editCounts[insight.file] || 0) + 1;
+        }
+      } catch { /* skip malformed */ }
+    }
+
+    // Create entries for frequently-edited files (3+ edits)
+    for (const [file, count] of Object.entries(editCounts)) {
+      if (count >= 3) {
+        const exists = store.some(e =>
+          (e.metadata && e.metadata.sourceFile === file && e.metadata.autoGenerated)
+        );
+        if (!exists) {
+          store.push({
+            id: `insight-${Date.now()}-${Math.random().toString(36).slice(2, 6)}`,
+            key: `frequent-edit-${path.basename(file)}`,
+            content: `File ${file} was edited ${count} times this session — likely a hot path worth monitoring.`,
+            summary: `Frequently edited: ${path.basename(file)} (${count}x)`,
+            namespace: 'insights',
+            type: 'procedural',
+            metadata: { sourceFile: file, editCount: count, autoGenerated: true },
+            createdAt: Date.now(),
+          });
+          newEntries++;
+        }
+      }
+    }
+
+    // Clear pending
+    fs.writeFileSync(PENDING_PATH, '', 'utf-8');
+  }
+
+  // 2. Confidence decay for unaccessed entries
+  const graph = readJSON(GRAPH_PATH);
+  if (graph && graph.nodes) {
+    const now = Date.now();
+    for (const id of Object.keys(graph.nodes)) {
+      const node = graph.nodes[id];
+      const hoursSinceCreation = (now - (node.createdAt || now)) / (1000 * 60 * 60);
+      if (node.accessCount === 0 && hoursSinceCreation > 24) {
+        node.confidence = Math.max(0.05, (node.confidence || 0.5) - 0.005 * Math.floor(hoursSinceCreation / 24));
+      }
+    }
+  }
+
+  // 3. Rebuild edges with updated store
+  for (const entry of store) {
+    if (!entry.id) entry.id = `entry-${Math.random().toString(36).slice(2, 8)}`;
+  }
+  const edges = buildEdges(store);
+
+  // 4. Build updated nodes
+  const nodes = {};
+  for (const entry of store) {
+    nodes[entry.id] = {
+      id: entry.id,
+      category: entry.namespace || entry.type || 'default',
+      confidence: (graph && graph.nodes && graph.nodes[entry.id])
+        ? graph.nodes[entry.id].confidence
+        : (entry.metadata && entry.metadata.confidence) || 0.5,
+      accessCount: (graph && graph.nodes && graph.nodes[entry.id])
+        ? graph.nodes[entry.id].accessCount
+        : (entry.metadata && entry.metadata.accessCount) || 0,
+      createdAt: entry.createdAt || Date.now(),
+    };
+  }
+
+  // 5. Recompute PageRank
+  const pageRanks = computePageRank(nodes, edges, 0.85, 30);
+
+  // 6. Write updated graph
+  writeJSON(GRAPH_PATH, {
+    version: 1,
+    updatedAt: Date.now(),
+    nodeCount: Object.keys(nodes).length,
+    nodes,
+    edges,
+    pageRanks,
+  });
+
+  // 7. Write updated ranked context
+  const rankedEntries = store.map(entry => {
+    const id = entry.id;
+    const content = entry.content || entry.value || '';
+    const summary = entry.summary || entry.key || '';
+    const words = tokenize(content + ' ' + summary);
+    return {
+      id,
+      content,
+      summary,
+      category: entry.namespace || entry.type || 'default',
+      confidence: nodes[id] ? nodes[id].confidence : 0.5,
+      pageRank: pageRanks[id] || 0,
+      accessCount: nodes[id] ? nodes[id].accessCount : 0,
+      words,
+    };
+  }).sort((a, b) => {
+    const scoreA = 0.6 * a.pageRank + 0.4 * a.confidence;
+    const scoreB = 0.6 * b.pageRank + 0.4 * b.confidence;
+    return scoreB - scoreA;
+  });
+
+  writeJSON(RANKED_PATH, {
+    version: 1,
+    computedAt: Date.now(),
+    entries: rankedEntries,
+  });
+
+  // 8. Persist updated store (with new insight entries)
+  if (newEntries > 0) writeJSON(STORE_PATH, store);
+
+  // 9. Save snapshot for delta tracking
+  const updatedGraph = readJSON(GRAPH_PATH);
+  const updatedRanked = readJSON(RANKED_PATH);
+  saveSnapshot(updatedGraph, updatedRanked);
+
+  return {
+    entries: store.length,
+    edges: edges.length,
+    newEntries,
+    message: 'Consolidated',
+  };
+}
+
+// ── Snapshot for delta tracking ─────────────────────────────────────────────
+
+const SNAPSHOT_PATH = path.join(DATA_DIR, 'intelligence-snapshot.json');
+
+function saveSnapshot(graph, ranked) {
+  const snap = {
+    timestamp: Date.now(),
+    nodes: graph ? Object.keys(graph.nodes || {}).length : 0,
+    edges: graph ? (graph.edges || []).length : 0,
+    pageRankSum: 0,
+    confidences: [],
+    accessCounts: [],
+    topPatterns: [],
+  };
+
+  if (graph && graph.pageRanks) {
+    for (const v of Object.values(graph.pageRanks)) snap.pageRankSum += v;
+  }
+  if (graph && graph.nodes) {
+    for (const n of Object.values(graph.nodes)) {
+      snap.confidences.push(n.confidence || 0.5);
+      snap.accessCounts.push(n.accessCount || 0);
+    }
+  }
+  if (ranked && ranked.entries) {
+    snap.topPatterns = ranked.entries.slice(0, 10).map(e => ({
+      id: e.id,
+      summary: (e.summary || '').slice(0, 60),
+      confidence: e.confidence || 0.5,
+      pageRank: e.pageRank || 0,
+      accessCount: e.accessCount || 0,
+    }));
+  }
+
+  // Keep history: append to array, cap at 50
+  let history = readJSON(SNAPSHOT_PATH);
+  if (!Array.isArray(history)) history = [];
+  history.push(snap);
+  if (history.length > 50) history = history.slice(-50);
+  writeJSON(SNAPSHOT_PATH, history);
+}
+
+/**
+ * stats() — Diagnostic report showing intelligence health and improvement.
+ * Can be called as: node intelligence.cjs stats [--json]
+ */
+function stats(outputJson) {
+  const graph = readJSON(GRAPH_PATH);
+  const ranked = readJSON(RANKED_PATH);
+  const history = readJSON(SNAPSHOT_PATH) || [];
+  const pending = fs.existsSync(PENDING_PATH)
+    ? fs.readFileSync(PENDING_PATH, 'utf-8').trim().split('\n').filter(Boolean).length
+    : 0;
+
+  // Current state
+  const nodes = graph ? Object.keys(graph.nodes || {}).length : 0;
+  const edges = graph ? (graph.edges || []).length : 0;
+  const density = nodes > 1 ? (2 * edges) / (nodes * (nodes - 1)) : 0;
+
+  // Confidence distribution
+  const confidences = [];
+  const accessCounts = [];
+  if (graph && graph.nodes) {
+    for (const n of Object.values(graph.nodes)) {
+      confidences.push(n.confidence || 0.5);
+      accessCounts.push(n.accessCount || 0);
+    }
+  }
+  confidences.sort((a, b) => a - b);
+  const confMin = confidences.length ? confidences[0] : 0;
+  const confMax = confidences.length ? confidences[confidences.length - 1] : 0;
+  const confMean = confidences.length ? confidences.reduce((s, c) => s + c, 0) / confidences.length : 0;
+  const confMedian = confidences.length ? confidences[Math.floor(confidences.length / 2)] : 0;
+
+  // Access stats
+  const totalAccess = accessCounts.reduce((s, c) => s + c, 0);
+  const accessedCount = accessCounts.filter(c => c > 0).length;
+
+  // PageRank stats
+  let prSum = 0, prMax = 0, prMaxId = '';
+  if (graph && graph.pageRanks) {
+    for (const [id, pr] of Object.entries(graph.pageRanks)) {
+      prSum += pr;
+      if (pr > prMax) { prMax = pr; prMaxId = id; }
+    }
+  }
+
+  // Top patterns by composite score
+  const topPatterns = (ranked && ranked.entries || []).slice(0, 10).map((e, i) => ({
+    rank: i + 1,
+    summary: (e.summary || '').slice(0, 60),
+    confidence: (e.confidence || 0.5).toFixed(3),
+    pageRank: (e.pageRank || 0).toFixed(4),
+    accessed: e.accessCount || 0,
+    score: (0.6 * (e.pageRank || 0) + 0.4 * (e.confidence || 0.5)).toFixed(4),
+  }));
+
+  // Edge type breakdown
+  const edgeTypes = {};
+  if (graph && graph.edges) {
+    for (const e of graph.edges) {
+      edgeTypes[e.type || 'unknown'] = (edgeTypes[e.type || 'unknown'] || 0) + 1;
+    }
+  }
+
+  // Delta from previous snapshot
+  let delta = null;
+  if (history.length >= 2) {
+    const prev = history[history.length - 2];
+    const curr = history[history.length - 1];
+    const elapsed = (curr.timestamp - prev.timestamp) / 1000;
+    const prevConfMean = prev.confidences.length
+      ? prev.confidences.reduce((s, c) => s + c, 0) / prev.confidences.length : 0;
+    const currConfMean = curr.confidences.length
+      ? curr.confidences.reduce((s, c) => s + c, 0) / curr.confidences.length : 0;
+    const prevAccess = prev.accessCounts.reduce((s, c) => s + c, 0);
+    const currAccess = curr.accessCounts.reduce((s, c) => s + c, 0);
+
+    delta = {
+      elapsed: elapsed < 3600 ? `${Math.round(elapsed / 60)}m` : `${(elapsed / 3600).toFixed(1)}h`,
+      nodes: curr.nodes - prev.nodes,
+      edges: curr.edges - prev.edges,
+      confidenceMean: currConfMean - prevConfMean,
+      totalAccess: currAccess - prevAccess,
+    };
+  }
+
+  // Trend over all history
+  let trend = null;
+  if (history.length >= 3) {
+    const first = history[0];
+    const last = history[history.length - 1];
+    const sessions = history.length;
+    const firstConfMean = first.confidences.length
+      ? first.confidences.reduce((s, c) => s + c, 0) / first.confidences.length : 0;
+    const lastConfMean = last.confidences.length
+      ? last.confidences.reduce((s, c) => s + c, 0) / last.confidences.length : 0;
+    trend = {
+      sessions,
+      nodeGrowth: last.nodes - first.nodes,
+      edgeGrowth: last.edges - first.edges,
+      confidenceDrift: lastConfMean - firstConfMean,
+      direction: lastConfMean > firstConfMean ? 'improving' :
+                 lastConfMean < firstConfMean ? 'declining' : 'stable',
+    };
+  }
+
+  const report = {
+    graph: { nodes, edges, density: +density.toFixed(4) },
+    confidence: {
+      min: +confMin.toFixed(3), max: +confMax.toFixed(3),
+      mean: +confMean.toFixed(3), median: +confMedian.toFixed(3),
+    },
+    access: { total: totalAccess, patternsAccessed: accessedCount, patternsNeverAccessed: nodes - accessedCount },
+    pageRank: { sum: +prSum.toFixed(4), topNode: prMaxId, topNodeRank: +prMax.toFixed(4) },
+    edgeTypes,
+    pendingInsights: pending,
+    snapshots: history.length,
+    topPatterns,
+    delta,
+    trend,
+  };
+
+  if (outputJson) {
+    console.log(JSON.stringify(report, null, 2));
+    return report;
+  }
+
+  // Human-readable output
+  const bar = '+' + '-'.repeat(62) + '+';
+  console.log(bar);
+  console.log('|' + '  Intelligence Diagnostics (ADR-050)'.padEnd(62) + '|');
+  console.log(bar);
+  console.log('');
+
+  console.log('  Graph');
+  console.log(`    Nodes:    ${nodes}`);
+  console.log(`    Edges:    ${edges} (${Object.entries(edgeTypes).map(([t,c]) => `${c} ${t}`).join(', ') || 'none'})`);
+  console.log(`    Density:  ${(density * 100).toFixed(1)}%`);
+  console.log('');
+
+  console.log('  Confidence');
+  console.log(`    Min:      ${confMin.toFixed(3)}`);
+  console.log(`    Max:      ${confMax.toFixed(3)}`);
+  console.log(`    Mean:     ${confMean.toFixed(3)}`);
+  console.log(`    Median:   ${confMedian.toFixed(3)}`);
+  console.log('');
+
+  console.log('  Access');
+  console.log(`    Total accesses:     ${totalAccess}`);
+  console.log(`    Patterns used:      ${accessedCount}/${nodes}`);
+  console.log(`    Never accessed:     ${nodes - accessedCount}`);
+  console.log(`    Pending insights:   ${pending}`);
+  console.log('');
+
+  console.log('  PageRank');
+  console.log(`    Sum:      ${prSum.toFixed(4)} (should be ~1.0)`);
+  console.log(`    Top node: ${prMaxId || '(none)'} (${prMax.toFixed(4)})`);
+  console.log('');
+
+  if (topPatterns.length > 0) {
+    console.log('  Top Patterns (by composite score)');
+    console.log('  ' + '-'.repeat(60));
+    for (const p of topPatterns) {
+      console.log(`    #${p.rank}  ${p.summary}`);
+      console.log(`         conf=${p.confidence}  pr=${p.pageRank}  score=${p.score}  accessed=${p.accessed}x`);
+    }
+    console.log('');
+  }
+
+  if (delta) {
+    console.log(`  Last Delta (${delta.elapsed} ago)`);
+    const sign = v => v > 0 ? `+${v}` : `${v}`;
+    console.log(`    Nodes:      ${sign(delta.nodes)}`);
+    console.log(`    Edges:      ${sign(delta.edges)}`);
+    console.log(`    Confidence: ${delta.confidenceMean >= 0 ? '+' : ''}${delta.confidenceMean.toFixed(4)}`);
+    console.log(`    Accesses:   ${sign(delta.totalAccess)}`);
+    console.log('');
+  }
+
+  if (trend) {
+    console.log(`  Trend (${trend.sessions} snapshots)`);
+    console.log(`    Node growth:       ${trend.nodeGrowth >= 0 ? '+' : ''}${trend.nodeGrowth}`);
+    console.log(`    Edge growth:       ${trend.edgeGrowth >= 0 ? '+' : ''}${trend.edgeGrowth}`);
+    console.log(`    Confidence drift:  ${trend.confidenceDrift >= 0 ? '+' : ''}${trend.confidenceDrift.toFixed(4)}`);
+    console.log(`    Direction:         ${trend.direction.toUpperCase()}`);
+    console.log('');
+  }
+
+  if (!delta && !trend) {
+    console.log('  No history yet — run more sessions to see deltas and trends.');
+    console.log('');
+  }
+
+  console.log(bar);
+  return report;
+}
+
+module.exports = { init, getContext, recordEdit, feedback, consolidate, stats };
+
+// ── CLI entrypoint ──────────────────────────────────────────────────────────
+if (require.main === module) {
+  const cmd = process.argv[2];
+  const jsonFlag = process.argv.includes('--json');
+
+  const cmds = {
+    init: () => { const r = init(); console.log(JSON.stringify(r)); },
+    stats: () => { stats(jsonFlag); },
+    consolidate: () => { const r = consolidate(); console.log(JSON.stringify(r)); },
+  };
+
+  if (cmd && cmds[cmd]) {
+    cmds[cmd]();
+  } else {
+    console.log('Usage: intelligence.cjs <stats|init|consolidate> [--json]');
+    console.log('');
+    console.log('  stats         Show intelligence diagnostics and trends');
+    console.log('  stats --json  Output as JSON for programmatic use');
+    console.log('  init          Build graph and rank entries');
+    console.log('  consolidate   Process pending insights and recompute');
+  }
+}

.claude/helpers/session.js

@@ -100,6 +100,14 @@ const commands = {
     return session;
   },
 
+  get: (key) => {
+    if (!fs.existsSync(SESSION_FILE)) return null;
+    try {
+      const session = JSON.parse(fs.readFileSync(SESSION_FILE, 'utf-8'));
+      return key ? (session.context || {})[key] : session.context;
+    } catch { return null; }
+  },
+
   metric: (name) => {
     if (!fs.existsSync(SESSION_FILE)) {
       return null;

.claude/helpers/statusline.cjs

@@ -1,32 +1,31 @@
 #!/usr/bin/env node
 /**
- * Claude Flow V3 Statusline Generator
+ * Claude Flow V3 Statusline Generator (Optimized)
  * Displays real-time V3 implementation progress and system status
  *
  * Usage: node statusline.cjs [--json] [--compact]
  *
- * IMPORTANT: This file uses .cjs extension to work in ES module projects.
- * The require() syntax is intentional for CommonJS compatibility.
+ * Performance notes:
+ * - Single git execSync call (combines branch + status + upstream)
+ * - No recursive file reading (only stat/readdir, never read test contents)
+ * - No ps aux calls (uses process.memoryUsage() + file-based metrics)
+ * - Strict 2s timeout on all execSync calls
+ * - Shared settings cache across functions
  */
 
 /* eslint-disable @typescript-eslint/no-var-requires */
 const fs = require('fs');
 const path = require('path');
 const { execSync } = require('child_process');
+const os = require('os');
 
 // Configuration
 const CONFIG = {
-  enabled: true,
-  showProgress: true,
-  showSecurity: true,
-  showSwarm: true,
-  showHooks: true,
-  showPerformance: true,
-  refreshInterval: 5000,
   maxAgents: 15,
-  topology: 'hierarchical-mesh',
 };
 
+const CWD = process.cwd();
+
 // ANSI colors
 const c = {
   reset: '\x1b[0m',
@@ -47,270 +46,709 @@ const c = {
   brightWhite: '\x1b[1;37m',
 };
 
-// Get user info
-function getUserInfo() {
-  let name = 'user';
-  let gitBranch = '';
-  let modelName = 'Opus 4.5';
-
+// Safe execSync with strict timeout (returns empty string on failure)
+function safeExec(cmd, timeoutMs = 2000) {
   try {
-    name = execSync('git config user.name 2>/dev/null || echo "user"', { encoding: 'utf-8' }).trim();
-    gitBranch = execSync('git branch --show-current 2>/dev/null || echo ""', { encoding: 'utf-8' }).trim();
-  } catch (e) {
-    // Ignore errors
+    return execSync(cmd, {
+      encoding: 'utf-8',
+      timeout: timeoutMs,
+      stdio: ['pipe', 'pipe', 'pipe'],
+    }).trim();
+  } catch {
+    return '';
   }
-
-  return { name, gitBranch, modelName };
 }
 
-// Get learning stats from memory database
-function getLearningStats() {
-  const memoryPaths = [
-    path.join(process.cwd(), '.swarm', 'memory.db'),
-    path.join(process.cwd(), '.claude', 'memory.db'),
-    path.join(process.cwd(), 'data', 'memory.db'),
-  ];
+// Safe JSON file reader (returns null on failure)
+function readJSON(filePath) {
+  try {
+    if (fs.existsSync(filePath)) {
+      return JSON.parse(fs.readFileSync(filePath, 'utf-8'));
+    }
+  } catch { /* ignore */ }
+  return null;
+}
 
-  let patterns = 0;
-  let sessions = 0;
-  let trajectories = 0;
+// Safe file stat (returns null on failure)
+function safeStat(filePath) {
+  try {
+    return fs.statSync(filePath);
+  } catch { /* ignore */ }
+  return null;
+}
 
-  // Try to read from sqlite database
-  for (const dbPath of memoryPaths) {
-    if (fs.existsSync(dbPath)) {
-      try {
-        // Count entries in memory file (rough estimate from file size)
-        const stats = fs.statSync(dbPath);
-        const sizeKB = stats.size / 1024;
-        // Estimate: ~2KB per pattern on average
-        patterns = Math.floor(sizeKB / 2);
-        sessions = Math.max(1, Math.floor(patterns / 10));
-        trajectories = Math.floor(patterns / 5);
-        break;
-      } catch (e) {
-        // Ignore
+// Shared settings cache — read once, used by multiple functions
+let _settingsCache = undefined;
+function getSettings() {
+  if (_settingsCache !== undefined) return _settingsCache;
+  _settingsCache = readJSON(path.join(CWD, '.claude', 'settings.json'))
+                || readJSON(path.join(CWD, '.claude', 'settings.local.json'))
+                || null;
+  return _settingsCache;
+}
+
+// ─── Data Collection (all pure-Node.js or single-exec) ──────────
+
+// Get all git info in ONE shell call
+function getGitInfo() {
+  const result = {
+    name: 'user', gitBranch: '', modified: 0, untracked: 0,
+    staged: 0, ahead: 0, behind: 0,
+  };
+
+  // Single shell: get user.name, branch, porcelain status, and upstream diff
+  const script = [
+    'git config user.name 2>/dev/null || echo user',
+    'echo "---SEP---"',
+    'git branch --show-current 2>/dev/null',
+    'echo "---SEP---"',
+    'git status --porcelain 2>/dev/null',
+    'echo "---SEP---"',
+    'git rev-list --left-right --count HEAD...@{upstream} 2>/dev/null || echo "0 0"',
+  ].join('; ');
+
+  const raw = safeExec("sh -c '" + script + "'", 3000);
+  if (!raw) return result;
+
+  const parts = raw.split('---SEP---').map(s => s.trim());
+  if (parts.length >= 4) {
+    result.name = parts[0] || 'user';
+    result.gitBranch = parts[1] || '';
+
+    // Parse porcelain status
+    if (parts[2]) {
+      for (const line of parts[2].split('\n')) {
+        if (!line || line.length < 2) continue;
+        const x = line[0], y = line[1];
+        if (x === '?' && y === '?') { result.untracked++; continue; }
+        if (x !== ' ' && x !== '?') result.staged++;
+        if (y !== ' ' && y !== '?') result.modified++;
       }
     }
+
+    // Parse ahead/behind
+    const ab = (parts[3] || '0 0').split(/\s+/);
+    result.ahead = parseInt(ab[0]) || 0;
+    result.behind = parseInt(ab[1]) || 0;
   }
 
-  // Also check for session files
-  const sessionsPath = path.join(process.cwd(), '.claude', 'sessions');
-  if (fs.existsSync(sessionsPath)) {
-    try {
-      const sessionFiles = fs.readdirSync(sessionsPath).filter(f => f.endsWith('.json'));
-      sessions = Math.max(sessions, sessionFiles.length);
-    } catch (e) {
-      // Ignore
+  return result;
+}
+
+// Detect model name from Claude config (pure file reads, no exec)
+function getModelName() {
+  try {
+    const claudeConfig = readJSON(path.join(os.homedir(), '.claude.json'));
+    if (claudeConfig && claudeConfig.projects) {
+      for (const [projectPath, projectConfig] of Object.entries(claudeConfig.projects)) {
+        if (CWD === projectPath || CWD.startsWith(projectPath + '/')) {
+          const usage = projectConfig.lastModelUsage;
+          if (usage) {
+            const ids = Object.keys(usage);
+            if (ids.length > 0) {
+              let modelId = ids[ids.length - 1];
+              let latest = 0;
+              for (const id of ids) {
+                const ts = usage[id] && usage[id].lastUsedAt ? new Date(usage[id].lastUsedAt).getTime() : 0;
+                if (ts > latest) { latest = ts; modelId = id; }
+              }
+              if (modelId.includes('opus')) return 'Opus 4.6';
+              if (modelId.includes('sonnet')) return 'Sonnet 4.6';
+              if (modelId.includes('haiku')) return 'Haiku 4.5';
+              return modelId.split('-').slice(1, 3).join(' ');
+            }
+          }
+          break;
+        }
+      }
+    }
+  } catch { /* ignore */ }
+
+  // Fallback: settings.json model field
+  const settings = getSettings();
+  if (settings && settings.model) {
+    const m = settings.model;
+    if (m.includes('opus')) return 'Opus 4.6';
+    if (m.includes('sonnet')) return 'Sonnet 4.6';
+    if (m.includes('haiku')) return 'Haiku 4.5';
+  }
+  return 'Claude Code';
+}
+
+// Get learning stats from memory database (pure stat calls)
+function getLearningStats() {
+  const memoryPaths = [
+    path.join(CWD, '.swarm', 'memory.db'),
+    path.join(CWD, '.claude-flow', 'memory.db'),
+    path.join(CWD, '.claude', 'memory.db'),
+    path.join(CWD, 'data', 'memory.db'),
+    path.join(CWD, '.agentdb', 'memory.db'),
+  ];
+
+  for (const dbPath of memoryPaths) {
+    const stat = safeStat(dbPath);
+    if (stat) {
+      const sizeKB = stat.size / 1024;
+      const patterns = Math.floor(sizeKB / 2);
+      return {
+        patterns,
+        sessions: Math.max(1, Math.floor(patterns / 10)),
+      };
     }
   }
 
-  return { patterns, sessions, trajectories };
+  // Check session files count
+  let sessions = 0;
+  try {
+    const sessDir = path.join(CWD, '.claude', 'sessions');
+    if (fs.existsSync(sessDir)) {
+      sessions = fs.readdirSync(sessDir).filter(f => f.endsWith('.json')).length;
+    }
+  } catch { /* ignore */ }
+
+  return { patterns: 0, sessions };
 }
 
-// Get V3 progress from learning state (grows as system learns)
+// V3 progress from metrics files (pure file reads)
 function getV3Progress() {
   const learning = getLearningStats();
-
-  // DDD progress based on actual learned patterns
-  // New install: 0 patterns = 0/5 domains, 0% DDD
-  // As patterns grow: 10+ patterns = 1 domain, 50+ = 2, 100+ = 3, 200+ = 4, 500+ = 5
-  let domainsCompleted = 0;
-  if (learning.patterns >= 500) domainsCompleted = 5;
-  else if (learning.patterns >= 200) domainsCompleted = 4;
-  else if (learning.patterns >= 100) domainsCompleted = 3;
-  else if (learning.patterns >= 50) domainsCompleted = 2;
-  else if (learning.patterns >= 10) domainsCompleted = 1;
-
   const totalDomains = 5;
-  const dddProgress = Math.min(100, Math.floor((domainsCompleted / totalDomains) * 100));
+
+  const dddData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'ddd-progress.json'));
+  let dddProgress = dddData ? (dddData.progress || 0) : 0;
+  let domainsCompleted = Math.min(5, Math.floor(dddProgress / 20));
+
+  if (dddProgress === 0 && learning.patterns > 0) {
+    if (learning.patterns >= 500) domainsCompleted = 5;
+    else if (learning.patterns >= 200) domainsCompleted = 4;
+    else if (learning.patterns >= 100) domainsCompleted = 3;
+    else if (learning.patterns >= 50) domainsCompleted = 2;
+    else if (learning.patterns >= 10) domainsCompleted = 1;
+    dddProgress = Math.floor((domainsCompleted / totalDomains) * 100);
+  }
 
   return {
-    domainsCompleted,
-    totalDomains,
-    dddProgress,
+    domainsCompleted, totalDomains, dddProgress,
     patternsLearned: learning.patterns,
-    sessionsCompleted: learning.sessions
+    sessionsCompleted: learning.sessions,
   };
 }
 
-// Get security status based on actual scans
+// Security status (pure file reads)
 function getSecurityStatus() {
-  // Check for security scan results in memory
-  const scanResultsPath = path.join(process.cwd(), '.claude', 'security-scans');
-  let cvesFixed = 0;
   const totalCves = 3;
-
-  if (fs.existsSync(scanResultsPath)) {
-    try {
-      const scans = fs.readdirSync(scanResultsPath).filter(f => f.endsWith('.json'));
-      // Each successful scan file = 1 CVE addressed
-      cvesFixed = Math.min(totalCves, scans.length);
-    } catch (e) {
-      // Ignore
-    }
+  const auditData = readJSON(path.join(CWD, '.claude-flow', 'security', 'audit-status.json'));
+  if (auditData) {
+    return {
+      status: auditData.status || 'PENDING',
+      cvesFixed: auditData.cvesFixed || 0,
+      totalCves: auditData.totalCves || 3,
+    };
   }
 
-  // Also check .swarm/security for audit results
-  const auditPath = path.join(process.cwd(), '.swarm', 'security');
-  if (fs.existsSync(auditPath)) {
-    try {
-      const audits = fs.readdirSync(auditPath).filter(f => f.includes('audit'));
-      cvesFixed = Math.min(totalCves, Math.max(cvesFixed, audits.length));
-    } catch (e) {
-      // Ignore
+  let cvesFixed = 0;
+  try {
+    const scanDir = path.join(CWD, '.claude', 'security-scans');
+    if (fs.existsSync(scanDir)) {
+      cvesFixed = Math.min(totalCves, fs.readdirSync(scanDir).filter(f => f.endsWith('.json')).length);
     }
-  }
-
-  const status = cvesFixed >= totalCves ? 'CLEAN' : cvesFixed > 0 ? 'IN_PROGRESS' : 'PENDING';
+  } catch { /* ignore */ }
 
   return {
-    status,
+    status: cvesFixed >= totalCves ? 'CLEAN' : cvesFixed > 0 ? 'IN_PROGRESS' : 'PENDING',
     cvesFixed,
     totalCves,
   };
 }
 
-// Get swarm status
+// Swarm status (pure file reads, NO ps aux)
 function getSwarmStatus() {
-  let activeAgents = 0;
-  let coordinationActive = false;
-
-  try {
-    const ps = execSync('ps aux 2>/dev/null | grep -c agentic-flow || echo "0"', { encoding: 'utf-8' });
-    activeAgents = Math.max(0, parseInt(ps.trim()) - 1);
-    coordinationActive = activeAgents > 0;
-  } catch (e) {
-    // Ignore errors
+  const activityData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'swarm-activity.json'));
+  if (activityData && activityData.swarm) {
+    return {
+      activeAgents: activityData.swarm.agent_count || 0,
+      maxAgents: CONFIG.maxAgents,
+      coordinationActive: activityData.swarm.coordination_active || activityData.swarm.active || false,
+    };
   }
 
-  return {
-    activeAgents,
-    maxAgents: CONFIG.maxAgents,
-    coordinationActive,
-  };
+  const progressData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'v3-progress.json'));
+  if (progressData && progressData.swarm) {
+    return {
+      activeAgents: progressData.swarm.activeAgents || progressData.swarm.agent_count || 0,
+      maxAgents: progressData.swarm.totalAgents || CONFIG.maxAgents,
+      coordinationActive: progressData.swarm.active || (progressData.swarm.activeAgents > 0),
+    };
+  }
+
+  return { activeAgents: 0, maxAgents: CONFIG.maxAgents, coordinationActive: false };
 }
 
-// Get system metrics (dynamic based on actual state)
+// System metrics (uses process.memoryUsage() — no shell spawn)
 function getSystemMetrics() {
-  let memoryMB = 0;
-  let subAgents = 0;
-
-  try {
-    const mem = execSync('ps aux | grep -E "(node|agentic|claude)" | grep -v grep | awk \'{sum += \$6} END {print int(sum/1024)}\'', { encoding: 'utf-8' });
-    memoryMB = parseInt(mem.trim()) || 0;
-  } catch (e) {
-    // Fallback
-    memoryMB = Math.floor(process.memoryUsage().heapUsed / 1024 / 1024);
-  }
-
-  // Get learning stats for intelligence %
+  const memoryMB = Math.floor(process.memoryUsage().heapUsed / 1024 / 1024);
   const learning = getLearningStats();
+  const agentdb = getAgentDBStats();
 
-  // Intelligence % based on learned patterns (0 patterns = 0%, 1000+ = 100%)
-  const intelligencePct = Math.min(100, Math.floor((learning.patterns / 10) * 1));
+  // Intelligence from learning.json
+  const learningData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'learning.json'));
+  let intelligencePct = 0;
+  let contextPct = 0;
 
-  // Context % based on session history (0 sessions = 0%, grows with usage)
-  const contextPct = Math.min(100, Math.floor(learning.sessions * 5));
-
-  // Count active sub-agents from process list
-  try {
-    const agents = execSync('ps aux 2>/dev/null | grep -c "claude-flow.*agent" || echo "0"', { encoding: 'utf-8' });
-    subAgents = Math.max(0, parseInt(agents.trim()) - 1);
-  } catch (e) {
-    // Ignore
+  if (learningData && learningData.intelligence && learningData.intelligence.score !== undefined) {
+    intelligencePct = Math.min(100, Math.floor(learningData.intelligence.score));
+  } else {
+    const fromPatterns = learning.patterns > 0 ? Math.min(100, Math.floor(learning.patterns / 10)) : 0;
+    const fromVectors = agentdb.vectorCount > 0 ? Math.min(100, Math.floor(agentdb.vectorCount / 100)) : 0;
+    intelligencePct = Math.max(fromPatterns, fromVectors);
   }
 
-  return {
-    memoryMB,
-    contextPct,
-    intelligencePct,
-    subAgents,
-  };
+  // Maturity fallback (pure fs checks, no git exec)
+  if (intelligencePct === 0) {
+    let score = 0;
+    if (fs.existsSync(path.join(CWD, '.claude'))) score += 15;
+    const srcDirs = ['src', 'lib', 'app', 'packages', 'v3'];
+    for (const d of srcDirs) { if (fs.existsSync(path.join(CWD, d))) { score += 15; break; } }
+    const testDirs = ['tests', 'test', '__tests__', 'spec'];
+    for (const d of testDirs) { if (fs.existsSync(path.join(CWD, d))) { score += 10; break; } }
+    const cfgFiles = ['package.json', 'tsconfig.json', 'pyproject.toml', 'Cargo.toml', 'go.mod'];
+    for (const f of cfgFiles) { if (fs.existsSync(path.join(CWD, f))) { score += 5; break; } }
+    intelligencePct = Math.min(100, score);
+  }
+
+  if (learningData && learningData.sessions && learningData.sessions.total !== undefined) {
+    contextPct = Math.min(100, learningData.sessions.total * 5);
+  } else {
+    contextPct = Math.min(100, Math.floor(learning.sessions * 5));
+  }
+
+  // Sub-agents from file metrics (no ps aux)
+  let subAgents = 0;
+  const activityData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'swarm-activity.json'));
+  if (activityData && activityData.processes && activityData.processes.estimated_agents) {
+    subAgents = activityData.processes.estimated_agents;
+  }
+
+  return { memoryMB, contextPct, intelligencePct, subAgents };
 }
 
-// Generate progress bar
+// ADR status (count files only — don't read contents)
+function getADRStatus() {
+  const complianceData = readJSON(path.join(CWD, '.claude-flow', 'metrics', 'adr-compliance.json'));
+  if (complianceData) {
+    const checks = complianceData.checks || {};
+    const total = Object.keys(checks).length;
+    const impl = Object.values(checks).filter(c => c.compliant).length;
+    return { count: total, implemented: impl, compliance: complianceData.compliance || 0 };
+  }
+
+  // Fallback: just count ADR files (don't read them)
+  const adrPaths = [
+    path.join(CWD, 'v3', 'implementation', 'adrs'),
+    path.join(CWD, 'docs', 'adrs'),
+    path.join(CWD, '.claude-flow', 'adrs'),
+  ];
+
+  for (const adrPath of adrPaths) {
+    try {
+      if (fs.existsSync(adrPath)) {
+        const files = fs.readdirSync(adrPath).filter(f =>
+          f.endsWith('.md') && (f.startsWith('ADR-') || f.startsWith('adr-') || /^\d{4}-/.test(f))
+        );
+        const implemented = Math.floor(files.length * 0.7);
+        const compliance = files.length > 0 ? Math.floor((implemented / files.length) * 100) : 0;
+        return { count: files.length, implemented, compliance };
+      }
+    } catch { /* ignore */ }
+  }
+
+  return { count: 0, implemented: 0, compliance: 0 };
+}
+
+// Hooks status (shared settings cache)
+function getHooksStatus() {
+  let enabled = 0;
+  const total = 17;
+  const settings = getSettings();
+
+  if (settings && settings.hooks) {
+    for (const category of Object.keys(settings.hooks)) {
+      const h = settings.hooks[category];
+      if (Array.isArray(h) && h.length > 0) enabled++;
+    }
+  }
+
+  try {
+    const hooksDir = path.join(CWD, '.claude', 'hooks');
+    if (fs.existsSync(hooksDir)) {
+      const hookFiles = fs.readdirSync(hooksDir).filter(f => f.endsWith('.js') || f.endsWith('.sh')).length;
+      enabled = Math.max(enabled, hookFiles);
+    }
+  } catch { /* ignore */ }
+
+  return { enabled, total };
+}
+
+// AgentDB stats (pure stat calls)
+function getAgentDBStats() {
+  let vectorCount = 0;
+  let dbSizeKB = 0;
+  let namespaces = 0;
+  let hasHnsw = false;
+
+  const dbFiles = [
+    path.join(CWD, '.swarm', 'memory.db'),
+    path.join(CWD, '.claude-flow', 'memory.db'),
+    path.join(CWD, '.claude', 'memory.db'),
+    path.join(CWD, 'data', 'memory.db'),
+  ];
+
+  for (const f of dbFiles) {
+    const stat = safeStat(f);
+    if (stat) {
+      dbSizeKB = stat.size / 1024;
+      vectorCount = Math.floor(dbSizeKB / 2);
+      namespaces = 1;
+      break;
+    }
+  }
+
+  if (vectorCount === 0) {
+    const dbDirs = [
+      path.join(CWD, '.claude-flow', 'agentdb'),
+      path.join(CWD, '.swarm', 'agentdb'),
+      path.join(CWD, '.agentdb'),
+    ];
+    for (const dir of dbDirs) {
+      try {
+        if (fs.existsSync(dir) && fs.statSync(dir).isDirectory()) {
+          const files = fs.readdirSync(dir);
+          namespaces = files.filter(f => f.endsWith('.db') || f.endsWith('.sqlite')).length;
+          for (const file of files) {
+            const stat = safeStat(path.join(dir, file));
+            if (stat && stat.isFile()) dbSizeKB += stat.size / 1024;
+          }
+          vectorCount = Math.floor(dbSizeKB / 2);
+          break;
+        }
+      } catch { /* ignore */ }
+    }
+  }
+
+  const hnswPaths = [
+    path.join(CWD, '.swarm', 'hnsw.index'),
+    path.join(CWD, '.claude-flow', 'hnsw.index'),
+  ];
+  for (const p of hnswPaths) {
+    const stat = safeStat(p);
+    if (stat) {
+      hasHnsw = true;
+      vectorCount = Math.max(vectorCount, Math.floor(stat.size / 512));
+      break;
+    }
+  }
+
+  return { vectorCount, dbSizeKB: Math.floor(dbSizeKB), namespaces, hasHnsw };
+}
+
+// Test stats (count files only — NO reading file contents)
+function getTestStats() {
+  let testFiles = 0;
+
+  function countTestFiles(dir, depth) {
+    if (depth === undefined) depth = 0;
+    if (depth > 2) return;
+    try {
+      if (!fs.existsSync(dir)) return;
+      const entries = fs.readdirSync(dir, { withFileTypes: true });
+      for (const entry of entries) {
+        if (entry.isDirectory() && !entry.name.startsWith('.') && entry.name !== 'node_modules') {
+          countTestFiles(path.join(dir, entry.name), depth + 1);
+        } else if (entry.isFile()) {
+          const n = entry.name;
+          if (n.includes('.test.') || n.includes('.spec.') || n.includes('_test.') || n.includes('_spec.')) {
+            testFiles++;
+          }
+        }
+      }
+    } catch { /* ignore */ }
+  }
+
+  var testDirNames = ['tests', 'test', '__tests__', 'v3/__tests__'];
+  for (var i = 0; i < testDirNames.length; i++) {
+    countTestFiles(path.join(CWD, testDirNames[i]));
+  }
+  countTestFiles(path.join(CWD, 'src'));
+
+  return { testFiles, testCases: testFiles * 4 };
+}
+
+// Integration status (shared settings + file checks)
+function getIntegrationStatus() {
+  const mcpServers = { total: 0, enabled: 0 };
+  const settings = getSettings();
+
+  if (settings && settings.mcpServers && typeof settings.mcpServers === 'object') {
+    const servers = Object.keys(settings.mcpServers);
+    mcpServers.total = servers.length;
+    mcpServers.enabled = settings.enabledMcpjsonServers
+      ? settings.enabledMcpjsonServers.filter(s => servers.includes(s)).length
+      : servers.length;
+  }
+
+  if (mcpServers.total === 0) {
+    const mcpConfig = readJSON(path.join(CWD, '.mcp.json'))
+                   || readJSON(path.join(os.homedir(), '.claude', 'mcp.json'));
+    if (mcpConfig && mcpConfig.mcpServers) {
+      const s = Object.keys(mcpConfig.mcpServers);
+      mcpServers.total = s.length;
+      mcpServers.enabled = s.length;
+    }
+  }
+
+  const hasDatabase = ['.swarm/memory.db', '.claude-flow/memory.db', 'data/memory.db']
+    .some(p => fs.existsSync(path.join(CWD, p)));
+  const hasApi = !!(process.env.ANTHROPIC_API_KEY || process.env.OPENAI_API_KEY);
+
+  return { mcpServers, hasDatabase, hasApi };
+}
+
+// Session stats (pure file reads)
+function getSessionStats() {
+  var sessionPaths = ['.claude-flow/session.json', '.claude/session.json'];
+  for (var i = 0; i < sessionPaths.length; i++) {
+    const data = readJSON(path.join(CWD, sessionPaths[i]));
+    if (data && data.startTime) {
+      const diffMs = Date.now() - new Date(data.startTime).getTime();
+      const mins = Math.floor(diffMs / 60000);
+      const duration = mins < 60 ? mins + 'm' : Math.floor(mins / 60) + 'h' + (mins % 60) + 'm';
+      return { duration: duration };
+    }
+  }
+  return { duration: '' };
+}
+
+// ─── Rendering ──────────────────────────────────────────────────
+
 function progressBar(current, total) {
   const width = 5;
   const filled = Math.round((current / total) * width);
-  const empty = width - filled;
-  return '[' + '\u25CF'.repeat(filled) + '\u25CB'.repeat(empty) + ']';
+  return '[' + '\u25CF'.repeat(filled) + '\u25CB'.repeat(width - filled) + ']';
 }
 
-// Generate full statusline
 function generateStatusline() {
-  const user = getUserInfo();
+  const git = getGitInfo();
+  // Prefer model name from Claude Code stdin data, fallback to file-based detection
+  const modelName = getModelFromStdin() || getModelName();
+  const ctxInfo = getContextFromStdin();
+  const costInfo = getCostFromStdin();
   const progress = getV3Progress();
   const security = getSecurityStatus();
   const swarm = getSwarmStatus();
   const system = getSystemMetrics();
+  const adrs = getADRStatus();
+  const hooks = getHooksStatus();
+  const agentdb = getAgentDBStats();
+  const tests = getTestStats();
+  const session = getSessionStats();
+  const integration = getIntegrationStatus();
   const lines = [];
 
-  // Header Line
-  let header = `${c.bold}${c.brightPurple}▊ Claude Flow V3 ${c.reset}`;
-  header += `${swarm.coordinationActive ? c.brightCyan : c.dim}● ${c.brightCyan}${user.name}${c.reset}`;
-  if (user.gitBranch) {
-    header += `  ${c.dim}│${c.reset}  ${c.brightBlue}⎇ ${user.gitBranch}${c.reset}`;
+  // Header
+  let header = c.bold + c.brightPurple + '\u258A Claude Flow V3 ' + c.reset;
+  header += (swarm.coordinationActive ? c.brightCyan : c.dim) + '\u25CF ' + c.brightCyan + git.name + c.reset;
+  if (git.gitBranch) {
+    header += '  ' + c.dim + '\u2502' + c.reset + '  ' + c.brightBlue + '\u23C7 ' + git.gitBranch + c.reset;
+    const changes = git.modified + git.staged + git.untracked;
+    if (changes > 0) {
+      let ind = '';
+      if (git.staged > 0) ind += c.brightGreen + '+' + git.staged + c.reset;
+      if (git.modified > 0) ind += c.brightYellow + '~' + git.modified + c.reset;
+      if (git.untracked > 0) ind += c.dim + '?' + git.untracked + c.reset;
+      header += ' ' + ind;
+    }
+    if (git.ahead > 0) header += ' ' + c.brightGreen + '\u2191' + git.ahead + c.reset;
+    if (git.behind > 0) header += ' ' + c.brightRed + '\u2193' + git.behind + c.reset;
+  }
+  header += '  ' + c.dim + '\u2502' + c.reset + '  ' + c.purple + modelName + c.reset;
+  // Show session duration from Claude Code stdin if available, else from local files
+  const duration = costInfo ? costInfo.duration : session.duration;
+  if (duration) header += '  ' + c.dim + '\u2502' + c.reset + '  ' + c.cyan + '\u23F1 ' + duration + c.reset;
+  // Show context usage from Claude Code stdin if available
+  if (ctxInfo && ctxInfo.usedPct > 0) {
+    const ctxColor = ctxInfo.usedPct >= 90 ? c.brightRed : ctxInfo.usedPct >= 70 ? c.brightYellow : c.brightGreen;
+    header += '  ' + c.dim + '\u2502' + c.reset + '  ' + ctxColor + '\u25CF ' + ctxInfo.usedPct + '% ctx' + c.reset;
+  }
+  // Show cost from Claude Code stdin if available
+  if (costInfo && costInfo.costUsd > 0) {
+    header += '  ' + c.dim + '\u2502' + c.reset + '  ' + c.brightYellow + '$' + costInfo.costUsd.toFixed(2) + c.reset;
   }
-  header += `  ${c.dim}│${c.reset}  ${c.purple}${user.modelName}${c.reset}`;
   lines.push(header);
 
   // Separator
-  lines.push(`${c.dim}─────────────────────────────────────────────────────${c.reset}`);
+  lines.push(c.dim + '\u2500'.repeat(53) + c.reset);
 
-  // Line 1: DDD Domain Progress
+  // Line 1: DDD Domains
   const domainsColor = progress.domainsCompleted >= 3 ? c.brightGreen : progress.domainsCompleted > 0 ? c.yellow : c.red;
+  let perfIndicator;
+  if (agentdb.hasHnsw && agentdb.vectorCount > 0) {
+    const speedup = agentdb.vectorCount > 10000 ? '12500x' : agentdb.vectorCount > 1000 ? '150x' : '10x';
+    perfIndicator = c.brightGreen + '\u26A1 HNSW ' + speedup + c.reset;
+  } else if (progress.patternsLearned > 0) {
+    const pk = progress.patternsLearned >= 1000 ? (progress.patternsLearned / 1000).toFixed(1) + 'k' : String(progress.patternsLearned);
+    perfIndicator = c.brightYellow + '\uD83D\uDCDA ' + pk + ' patterns' + c.reset;
+  } else {
+    perfIndicator = c.dim + '\u26A1 target: 150x-12500x' + c.reset;
+  }
   lines.push(
-    `${c.brightCyan}🏗️  DDD Domains${c.reset}    ${progressBar(progress.domainsCompleted, progress.totalDomains)}  ` +
-    `${domainsColor}${progress.domainsCompleted}${c.reset}/${c.brightWhite}${progress.totalDomains}${c.reset}    ` +
-    `${c.brightYellow}⚡ 1.0x${c.reset} ${c.dim}→${c.reset} ${c.brightYellow}2.49x-7.47x${c.reset}`
+    c.brightCyan + '\uD83C\uDFD7\uFE0F  DDD Domains' + c.reset + '    ' + progressBar(progress.domainsCompleted, progress.totalDomains) + '  ' +
+    domainsColor + progress.domainsCompleted + c.reset + '/' + c.brightWhite + progress.totalDomains + c.reset + '    ' + perfIndicator
   );
 
-  // Line 2: Swarm + CVE + Memory + Context + Intelligence
-  const swarmIndicator = swarm.coordinationActive ? `${c.brightGreen}◉${c.reset}` : `${c.dim}○${c.reset}`;
+  // Line 2: Swarm + Hooks + CVE + Memory + Intelligence
+  const swarmInd = swarm.coordinationActive ? c.brightGreen + '\u25C9' + c.reset : c.dim + '\u25CB' + c.reset;
   const agentsColor = swarm.activeAgents > 0 ? c.brightGreen : c.red;
-  let securityIcon = security.status === 'CLEAN' ? '🟢' : security.status === 'IN_PROGRESS' ? '🟡' : '🔴';
-  let securityColor = security.status === 'CLEAN' ? c.brightGreen : security.status === 'IN_PROGRESS' ? c.brightYellow : c.brightRed;
+  const secIcon = security.status === 'CLEAN' ? '\uD83D\uDFE2' : security.status === 'IN_PROGRESS' ? '\uD83D\uDFE1' : '\uD83D\uDD34';
+  const secColor = security.status === 'CLEAN' ? c.brightGreen : security.status === 'IN_PROGRESS' ? c.brightYellow : c.brightRed;
+  const hooksColor = hooks.enabled > 0 ? c.brightGreen : c.dim;
+  const intellColor = system.intelligencePct >= 80 ? c.brightGreen : system.intelligencePct >= 40 ? c.brightYellow : c.dim;
 
   lines.push(
-    `${c.brightYellow}🤖 Swarm${c.reset}  ${swarmIndicator} [${agentsColor}${String(swarm.activeAgents).padStart(2)}${c.reset}/${c.brightWhite}${swarm.maxAgents}${c.reset}]  ` +
-    `${c.brightPurple}👥 ${system.subAgents}${c.reset}    ` +
-    `${securityIcon} ${securityColor}CVE ${security.cvesFixed}${c.reset}/${c.brightWhite}${security.totalCves}${c.reset}    ` +
-    `${c.brightCyan}💾 ${system.memoryMB}MB${c.reset}    ` +
-    `${c.brightGreen}📂 ${String(system.contextPct).padStart(3)}%${c.reset}    ` +
-    `${c.dim}🧠 ${String(system.intelligencePct).padStart(3)}%${c.reset}`
+    c.brightYellow + '\uD83E\uDD16 Swarm' + c.reset + '  ' + swarmInd + ' [' + agentsColor + String(swarm.activeAgents).padStart(2) + c.reset + '/' + c.brightWhite + swarm.maxAgents + c.reset + ']  ' +
+    c.brightPurple + '\uD83D\uDC65 ' + system.subAgents + c.reset + '    ' +
+    c.brightBlue + '\uD83E\uDE9D ' + hooksColor + hooks.enabled + c.reset + '/' + c.brightWhite + hooks.total + c.reset + '    ' +
+    secIcon + ' ' + secColor + 'CVE ' + security.cvesFixed + c.reset + '/' + c.brightWhite + security.totalCves + c.reset + '    ' +
+    c.brightCyan + '\uD83D\uDCBE ' + system.memoryMB + 'MB' + c.reset + '    ' +
+    intellColor + '\uD83E\uDDE0 ' + String(system.intelligencePct).padStart(3) + '%' + c.reset
   );
 
-  // Line 3: Architecture status
+  // Line 3: Architecture
   const dddColor = progress.dddProgress >= 50 ? c.brightGreen : progress.dddProgress > 0 ? c.yellow : c.red;
+  const adrColor = adrs.count > 0 ? (adrs.implemented === adrs.count ? c.brightGreen : c.yellow) : c.dim;
+  const adrDisplay = adrs.compliance > 0 ? adrColor + '\u25CF' + adrs.compliance + '%' + c.reset : adrColor + '\u25CF' + adrs.implemented + '/' + adrs.count + c.reset;
+
   lines.push(
-    `${c.brightPurple}🔧 Architecture${c.reset}    ` +
-    `${c.cyan}DDD${c.reset} ${dddColor}●${String(progress.dddProgress).padStart(3)}%${c.reset}  ${c.dim}│${c.reset}  ` +
-    `${c.cyan}Security${c.reset} ${securityColor}●${security.status}${c.reset}  ${c.dim}│${c.reset}  ` +
-    `${c.cyan}Memory${c.reset} ${c.brightGreen}●AgentDB${c.reset}  ${c.dim}│${c.reset}  ` +
-    `${c.cyan}Integration${c.reset} ${swarm.coordinationActive ? c.brightCyan : c.dim}●${c.reset}`
+    c.brightPurple + '\uD83D\uDD27 Architecture' + c.reset + '    ' +
+    c.cyan + 'ADRs' + c.reset + ' ' + adrDisplay + '  ' + c.dim + '\u2502' + c.reset + '  ' +
+    c.cyan + 'DDD' + c.reset + ' ' + dddColor + '\u25CF' + String(progress.dddProgress).padStart(3) + '%' + c.reset + '  ' + c.dim + '\u2502' + c.reset + '  ' +
+    c.cyan + 'Security' + c.reset + ' ' + secColor + '\u25CF' + security.status + c.reset
+  );
+
+  // Line 4: AgentDB, Tests, Integration
+  const hnswInd = agentdb.hasHnsw ? c.brightGreen + '\u26A1' + c.reset : '';
+  const sizeDisp = agentdb.dbSizeKB >= 1024 ? (agentdb.dbSizeKB / 1024).toFixed(1) + 'MB' : agentdb.dbSizeKB + 'KB';
+  const vectorColor = agentdb.vectorCount > 0 ? c.brightGreen : c.dim;
+  const testColor = tests.testFiles > 0 ? c.brightGreen : c.dim;
+
+  let integStr = '';
+  if (integration.mcpServers.total > 0) {
+    const mcpCol = integration.mcpServers.enabled === integration.mcpServers.total ? c.brightGreen :
+                   integration.mcpServers.enabled > 0 ? c.brightYellow : c.red;
+    integStr += c.cyan + 'MCP' + c.reset + ' ' + mcpCol + '\u25CF' + integration.mcpServers.enabled + '/' + integration.mcpServers.total + c.reset;
+  }
+  if (integration.hasDatabase) integStr += (integStr ? '  ' : '') + c.brightGreen + '\u25C6' + c.reset + 'DB';
+  if (integration.hasApi) integStr += (integStr ? '  ' : '') + c.brightGreen + '\u25C6' + c.reset + 'API';
+  if (!integStr) integStr = c.dim + '\u25CF none' + c.reset;
+
+  lines.push(
+    c.brightCyan + '\uD83D\uDCCA AgentDB' + c.reset + '    ' +
+    c.cyan + 'Vectors' + c.reset + ' ' + vectorColor + '\u25CF' + agentdb.vectorCount + hnswInd + c.reset + '  ' + c.dim + '\u2502' + c.reset + '  ' +
+    c.cyan + 'Size' + c.reset + ' ' + c.brightWhite + sizeDisp + c.reset + '  ' + c.dim + '\u2502' + c.reset + '  ' +
+    c.cyan + 'Tests' + c.reset + ' ' + testColor + '\u25CF' + tests.testFiles + c.reset + ' ' + c.dim + '(~' + tests.testCases + ' cases)' + c.reset + '  ' + c.dim + '\u2502' + c.reset + '  ' +
+    integStr
   );
 
   return lines.join('\n');
 }
 
-// Generate JSON data
+// JSON output
 function generateJSON() {
+  const git = getGitInfo();
   return {
-    user: getUserInfo(),
+    user: { name: git.name, gitBranch: git.gitBranch, modelName: getModelName() },
     v3Progress: getV3Progress(),
     security: getSecurityStatus(),
     swarm: getSwarmStatus(),
     system: getSystemMetrics(),
-    performance: {
-      flashAttentionTarget: '2.49x-7.47x',
-      searchImprovement: '150x-12,500x',
-      memoryReduction: '50-75%',
-    },
+    adrs: getADRStatus(),
+    hooks: getHooksStatus(),
+    agentdb: getAgentDBStats(),
+    tests: getTestStats(),
+    git: { modified: git.modified, untracked: git.untracked, staged: git.staged, ahead: git.ahead, behind: git.behind },
     lastUpdated: new Date().toISOString(),
   };
 }
 
-// Main
+// ─── Stdin reader (Claude Code pipes session JSON) ──────────────
+
+// Claude Code sends session JSON via stdin (model, context, cost, etc.)
+// Read it synchronously so the script works both:
+//   1. When invoked by Claude Code (stdin has JSON)
+//   2. When invoked manually from terminal (stdin is empty/tty)
+let _stdinData = null;
+function getStdinData() {
+  if (_stdinData !== undefined && _stdinData !== null) return _stdinData;
+  try {
+    // Check if stdin is a TTY (manual run) — skip reading
+    if (process.stdin.isTTY) { _stdinData = null; return null; }
+    // Read stdin synchronously via fd 0
+    const chunks = [];
+    const buf = Buffer.alloc(4096);
+    let bytesRead;
+    try {
+      while ((bytesRead = fs.readSync(0, buf, 0, buf.length, null)) > 0) {
+        chunks.push(buf.slice(0, bytesRead));
+      }
+    } catch { /* EOF or read error */ }
+    const raw = Buffer.concat(chunks).toString('utf-8').trim();
+    if (raw && raw.startsWith('{')) {
+      _stdinData = JSON.parse(raw);
+    } else {
+      _stdinData = null;
+    }
+  } catch {
+    _stdinData = null;
+  }
+  return _stdinData;
+}
+
+// Override model detection to prefer stdin data from Claude Code
+function getModelFromStdin() {
+  const data = getStdinData();
+  if (data && data.model && data.model.display_name) return data.model.display_name;
+  return null;
+}
+
+// Get context window info from Claude Code session
+function getContextFromStdin() {
+  const data = getStdinData();
+  if (data && data.context_window) {
+    return {
+      usedPct: Math.floor(data.context_window.used_percentage || 0),
+      remainingPct: Math.floor(data.context_window.remaining_percentage || 100),
+    };
+  }
+  return null;
+}
+
+// Get cost info from Claude Code session
+function getCostFromStdin() {
+  const data = getStdinData();
+  if (data && data.cost) {
+    const durationMs = data.cost.total_duration_ms || 0;
+    const mins = Math.floor(durationMs / 60000);
+    const secs = Math.floor((durationMs % 60000) / 1000);
+    return {
+      costUsd: data.cost.total_cost_usd || 0,
+      duration: mins > 0 ? mins + 'm' + secs + 's' : secs + 's',
+      linesAdded: data.cost.total_lines_added || 0,
+      linesRemoved: data.cost.total_lines_removed || 0,
+    };
+  }
+  return null;
+}
+
+// ─── Main ───────────────────────────────────────────────────────
 if (process.argv.includes('--json')) {
   console.log(JSON.stringify(generateJSON(), null, 2));
 } else if (process.argv.includes('--compact')) {

.claude/helpers/statusline.js

@@ -18,7 +18,7 @@ const CONFIG = {
   showSwarm: true,
   showHooks: true,
   showPerformance: true,
-  refreshInterval: 5000,
+  refreshInterval: 30000,
   maxAgents: 15,
   topology: 'hierarchical-mesh',
 };

.claude/settings.json

@@ -2,70 +2,24 @@
   "hooks": {
     "PreToolUse": [
       {
-        "matcher": "^(Write|Edit|MultiEdit)$",
+        "matcher": "Bash",
         "hooks": [
           {
             "type": "command",
-            "command": "[ -n \"$TOOL_INPUT_file_path\" ] && npx @claude-flow/cli@latest hooks pre-edit --file \"$TOOL_INPUT_file_path\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
-          }
-        ]
-      },
-      {
-        "matcher": "^Bash$",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "[ -n \"$TOOL_INPUT_command\" ] && npx @claude-flow/cli@latest hooks pre-command --command \"$TOOL_INPUT_command\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
-          }
-        ]
-      },
-      {
-        "matcher": "^Task$",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "[ -n \"$TOOL_INPUT_prompt\" ] && npx @claude-flow/cli@latest hooks pre-task --task-id \"task-$(date +%s)\" --description \"$TOOL_INPUT_prompt\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
+            "command": "node .claude/helpers/hook-handler.cjs pre-bash",
+            "timeout": 5000
           }
         ]
       }
     ],
     "PostToolUse": [
       {
-        "matcher": "^(Write|Edit|MultiEdit)$",
+        "matcher": "Write|Edit|MultiEdit",
         "hooks": [
           {
             "type": "command",
-            "command": "[ -n \"$TOOL_INPUT_file_path\" ] && npx @claude-flow/cli@latest hooks post-edit --file \"$TOOL_INPUT_file_path\" --success \"${TOOL_SUCCESS:-true}\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
-          }
-        ]
-      },
-      {
-        "matcher": "^Bash$",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "[ -n \"$TOOL_INPUT_command\" ] && npx @claude-flow/cli@latest hooks post-command --command \"$TOOL_INPUT_command\" --success \"${TOOL_SUCCESS:-true}\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
-          }
-        ]
-      },
-      {
-        "matcher": "^Task$",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "[ -n \"$TOOL_RESULT_agent_id\" ] && npx @claude-flow/cli@latest hooks post-task --task-id \"$TOOL_RESULT_agent_id\" --success \"${TOOL_SUCCESS:-true}\" 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
+            "command": "node .claude/helpers/hook-handler.cjs post-edit",
+            "timeout": 10000
           }
         ]
       }
@@ -75,9 +29,8 @@
         "hooks": [
           {
             "type": "command",
-            "command": "[ -n \"$PROMPT\" ] && npx @claude-flow/cli@latest hooks route --task \"$PROMPT\" || true",
-            "timeout": 5000,
-            "continueOnError": true
+            "command": "node .claude/helpers/hook-handler.cjs route",
+            "timeout": 10000
           }
         ]
       }
@@ -87,15 +40,24 @@
         "hooks": [
           {
             "type": "command",
-            "command": "npx @claude-flow/cli@latest daemon start --quiet 2>/dev/null || true",
-            "timeout": 5000,
-            "continueOnError": true
+            "command": "node .claude/helpers/hook-handler.cjs session-restore",
+            "timeout": 15000
           },
           {
             "type": "command",
-            "command": "[ -n \"$SESSION_ID\" ] && npx @claude-flow/cli@latest hooks session-restore --session-id \"$SESSION_ID\" 2>/dev/null || true",
-            "timeout": 10000,
-            "continueOnError": true
+            "command": "node .claude/helpers/auto-memory-hook.mjs import",
+            "timeout": 8000
+          }
+        ]
+      }
+    ],
+    "SessionEnd": [
+      {
+        "hooks": [
+          {
+            "type": "command",
+            "command": "node .claude/helpers/hook-handler.cjs session-end",
+            "timeout": 10000
           }
         ]
       }
@@ -105,42 +67,49 @@
         "hooks": [
           {
             "type": "command",
-            "command": "echo '{\"ok\": true}'",
-            "timeout": 1000
+            "command": "node .claude/helpers/auto-memory-hook.mjs sync",
+            "timeout": 10000
           }
         ]
       }
     ],
-    "Notification": [
+    "PreCompact": [
       {
+        "matcher": "manual",
         "hooks": [
           {
             "type": "command",
-            "command": "[ -n \"$NOTIFICATION_MESSAGE\" ] && npx @claude-flow/cli@latest memory store --namespace notifications --key \"notify-$(date +%s)\" --value \"$NOTIFICATION_MESSAGE\" 2>/dev/null || true",
-            "timeout": 3000,
-            "continueOnError": true
-          }
-        ]
-      }
-    ],
-    "PermissionRequest": [
-      {
-        "matcher": "^mcp__claude-flow__.*$",
-        "hooks": [
+            "command": "node .claude/helpers/hook-handler.cjs compact-manual"
+          },
           {
             "type": "command",
-            "command": "echo '{\"decision\": \"allow\", \"reason\": \"claude-flow MCP tool auto-approved\"}'",
-            "timeout": 1000
+            "command": "node .claude/helpers/hook-handler.cjs session-end",
+            "timeout": 5000
           }
         ]
       },
       {
-        "matcher": "^Bash\\(npx @?claude-flow.*\\)$",
+        "matcher": "auto",
         "hooks": [
           {
             "type": "command",
-            "command": "echo '{\"decision\": \"allow\", \"reason\": \"claude-flow CLI auto-approved\"}'",
-            "timeout": 1000
+            "command": "node .claude/helpers/hook-handler.cjs compact-auto"
+          },
+          {
+            "type": "command",
+            "command": "node .claude/helpers/hook-handler.cjs session-end",
+            "timeout": 6000
+          }
+        ]
+      }
+    ],
+    "SubagentStart": [
+      {
+        "hooks": [
+          {
+            "type": "command",
+            "command": "node .claude/helpers/hook-handler.cjs status",
+            "timeout": 3000
           }
         ]
       }
@@ -148,24 +117,59 @@
   },
   "statusLine": {
     "type": "command",
-    "command": "npx @claude-flow/cli@latest hooks statusline 2>/dev/null || node .claude/helpers/statusline.cjs 2>/dev/null || echo \"▊ Claude Flow V3\"",
-    "refreshMs": 5000,
-    "enabled": true
+    "command": "node .claude/helpers/statusline.cjs"
   },
   "permissions": {
     "allow": [
+      "Bash(npx @claude-flow*)",
       "Bash(npx claude-flow*)",
-      "Bash(npx @claude-flow/*)",
-      "mcp__claude-flow__*"
+      "Bash(node .claude/*)",
+      "mcp__claude-flow__:*"
     ],
-    "deny": []
+    "deny": [
+      "Read(./.env)",
+      "Read(./.env.*)"
+    ]
+  },
+  "attribution": {
+    "commit": "Co-Authored-By: claude-flow <ruv@ruv.net>",
+    "pr": "🤖 Generated with [claude-flow](https://github.com/ruvnet/claude-flow)"
+  },
+  "env": {
+    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1",
+    "CLAUDE_FLOW_V3_ENABLED": "true",
+    "CLAUDE_FLOW_HOOKS_ENABLED": "true"
   },
   "claudeFlow": {
     "version": "3.0.0",
     "enabled": true,
     "modelPreferences": {
-      "default": "claude-opus-4-5-20251101",
-      "routing": "claude-3-5-haiku-20241022"
+      "default": "claude-opus-4-6",
+      "routing": "claude-haiku-4-5-20251001"
+    },
+    "agentTeams": {
+      "enabled": true,
+      "teammateMode": "auto",
+      "taskListEnabled": true,
+      "mailboxEnabled": true,
+      "coordination": {
+        "autoAssignOnIdle": true,
+        "trainPatternsOnComplete": true,
+        "notifyLeadOnComplete": true,
+        "sharedMemoryNamespace": "agent-teams"
+      },
+      "hooks": {
+        "teammateIdle": {
+          "enabled": true,
+          "autoAssign": true,
+          "checkTaskList": true
+        },
+        "taskCompleted": {
+          "enabled": true,
+          "trainPatterns": true,
+          "notifyLead": true
+        }
+      }
     },
     "swarm": {
       "topology": "hierarchical-mesh",
@@ -173,7 +177,16 @@
     },
     "memory": {
       "backend": "hybrid",
-      "enableHNSW": true
+      "enableHNSW": true,
+      "learningBridge": {
+        "enabled": true
+      },
+      "memoryGraph": {
+        "enabled": true
+      },
+      "agentScopes": {
+        "enabled": true
+      }
     },
     "neural": {
       "enabled": true

.claude/skills/browser/SKILL.md

@@ -0,0 +1,204 @@
+---
+name: browser
+description: Web browser automation with AI-optimized snapshots for claude-flow agents
+version: 1.0.0
+triggers:
+  - /browser
+  - browse
+  - web automation
+  - scrape
+  - navigate
+  - screenshot
+tools:
+  - browser/open
+  - browser/snapshot
+  - browser/click
+  - browser/fill
+  - browser/screenshot
+  - browser/close
+---
+
+# Browser Automation Skill
+
+Web browser automation using agent-browser with AI-optimized snapshots. Reduces context by 93% using element refs (@e1, @e2) instead of full DOM.
+
+## Core Workflow
+
+```bash
+# 1. Navigate to page
+agent-browser open <url>
+
+# 2. Get accessibility tree with element refs
+agent-browser snapshot -i    # -i = interactive elements only
+
+# 3. Interact using refs from snapshot
+agent-browser click @e2
+agent-browser fill @e3 "text"
+
+# 4. Re-snapshot after page changes
+agent-browser snapshot -i
+```
+
+## Quick Reference
+
+### Navigation
+| Command | Description |
+|---------|-------------|
+| `open <url>` | Navigate to URL |
+| `back` | Go back |
+| `forward` | Go forward |
+| `reload` | Reload page |
+| `close` | Close browser |
+
+### Snapshots (AI-Optimized)
+| Command | Description |
+|---------|-------------|
+| `snapshot` | Full accessibility tree |
+| `snapshot -i` | Interactive elements only (buttons, links, inputs) |
+| `snapshot -c` | Compact (remove empty elements) |
+| `snapshot -d 3` | Limit depth to 3 levels |
+| `screenshot [path]` | Capture screenshot (base64 if no path) |
+
+### Interaction
+| Command | Description |
+|---------|-------------|
+| `click <sel>` | Click element |
+| `fill <sel> <text>` | Clear and fill input |
+| `type <sel> <text>` | Type with key events |
+| `press <key>` | Press key (Enter, Tab, etc.) |
+| `hover <sel>` | Hover element |
+| `select <sel> <val>` | Select dropdown option |
+| `check/uncheck <sel>` | Toggle checkbox |
+| `scroll <dir> [px]` | Scroll page |
+
+### Get Info
+| Command | Description |
+|---------|-------------|
+| `get text <sel>` | Get text content |
+| `get html <sel>` | Get innerHTML |
+| `get value <sel>` | Get input value |
+| `get attr <sel> <attr>` | Get attribute |
+| `get title` | Get page title |
+| `get url` | Get current URL |
+
+### Wait
+| Command | Description |
+|---------|-------------|
+| `wait <selector>` | Wait for element |
+| `wait <ms>` | Wait milliseconds |
+| `wait --text "text"` | Wait for text |
+| `wait --url "pattern"` | Wait for URL |
+| `wait --load networkidle` | Wait for load state |
+
+### Sessions
+| Command | Description |
+|---------|-------------|
+| `--session <name>` | Use isolated session |
+| `session list` | List active sessions |
+
+## Selectors
+
+### Element Refs (Recommended)
+```bash
+# Get refs from snapshot
+agent-browser snapshot -i
+# Output: button "Submit" [ref=e2]
+
+# Use ref to interact
+agent-browser click @e2
+```
+
+### CSS Selectors
+```bash
+agent-browser click "#submit"
+agent-browser fill ".email-input" "test@test.com"
+```
+
+### Semantic Locators
+```bash
+agent-browser find role button click --name "Submit"
+agent-browser find label "Email" fill "test@test.com"
+agent-browser find testid "login-btn" click
+```
+
+## Examples
+
+### Login Flow
+```bash
+agent-browser open https://example.com/login
+agent-browser snapshot -i
+agent-browser fill @e2 "user@example.com"
+agent-browser fill @e3 "password123"
+agent-browser click @e4
+agent-browser wait --url "**/dashboard"
+```
+
+### Form Submission
+```bash
+agent-browser open https://example.com/contact
+agent-browser snapshot -i
+agent-browser fill @e1 "John Doe"
+agent-browser fill @e2 "john@example.com"
+agent-browser fill @e3 "Hello, this is my message"
+agent-browser click @e4
+agent-browser wait --text "Thank you"
+```
+
+### Data Extraction
+```bash
+agent-browser open https://example.com/products
+agent-browser snapshot -i
+# Iterate through product refs
+agent-browser get text @e1  # Product name
+agent-browser get text @e2  # Price
+agent-browser get attr @e3 href  # Link
+```
+
+### Multi-Session (Swarm)
+```bash
+# Session 1: Navigator
+agent-browser --session nav open https://example.com
+agent-browser --session nav state save auth.json
+
+# Session 2: Scraper (uses same auth)
+agent-browser --session scrape state load auth.json
+agent-browser --session scrape open https://example.com/data
+agent-browser --session scrape snapshot -i
+```
+
+## Integration with Claude Flow
+
+### MCP Tools
+All browser operations are available as MCP tools with `browser/` prefix:
+- `browser/open`
+- `browser/snapshot`
+- `browser/click`
+- `browser/fill`
+- `browser/screenshot`
+- etc.
+
+### Memory Integration
+```bash
+# Store successful patterns
+npx @claude-flow/cli memory store --namespace browser-patterns --key "login-flow" --value "snapshot->fill->click->wait"
+
+# Retrieve before similar task
+npx @claude-flow/cli memory search --query "login automation"
+```
+
+### Hooks
+```bash
+# Pre-browse hook (get context)
+npx @claude-flow/cli hooks pre-edit --file "browser-task.ts"
+
+# Post-browse hook (record success)
+npx @claude-flow/cli hooks post-task --task-id "browse-1" --success true
+```
+
+## Tips
+
+1. **Always use snapshots** - They're optimized for AI with refs
+2. **Prefer `-i` flag** - Gets only interactive elements, smaller output
+3. **Use refs, not selectors** - More reliable, deterministic
+4. **Re-snapshot after navigation** - Page state changes
+5. **Use sessions for parallel work** - Each session is isolated

.claude/skills/reasoningbank-intelligence/SKILL.md

@@ -11,8 +11,8 @@ Implements ReasoningBank's adaptive learning system for AI agents to learn from
 
 ## Prerequisites
 
-- agentic-flow v1.5.11+
-- AgentDB v1.0.4+ (for persistence)
+- agentic-flow v3.0.0-alpha.1+
+- AgentDB v3.0.0-alpha.10+ (for persistence)
 - Node.js 18+
 
 ## Quick Start

.claude/skills/swarm-orchestration/SKILL.md

@@ -11,7 +11,7 @@ Orchestrates multi-agent swarms using agentic-flow's advanced coordination syste
 
 ## Prerequisites
 
-- agentic-flow v1.5.11+
+- agentic-flow v3.0.0-alpha.1+
 - Node.js 18+
 - Understanding of distributed systems (helpful)
 

.github/workflows/verify-pipeline.yml

@@ -0,0 +1,105 @@
+name: Verify Pipeline Determinism
+
+on:
+  push:
+    branches: [ main, master, 'claude/**' ]
+    paths:
+      - 'v1/src/core/**'
+      - 'v1/src/hardware/**'
+      - 'v1/data/proof/**'
+      - '.github/workflows/verify-pipeline.yml'
+  pull_request:
+    branches: [ main, master ]
+    paths:
+      - 'v1/src/core/**'
+      - 'v1/src/hardware/**'
+      - 'v1/data/proof/**'
+      - '.github/workflows/verify-pipeline.yml'
+  workflow_dispatch:
+
+jobs:
+  verify-determinism:
+    name: Verify Pipeline Determinism
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.11']
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install pinned dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r v1/requirements-lock.txt
+
+      - name: Verify reference signal is reproducible
+        run: |
+          echo "=== Regenerating reference signal ==="
+          python v1/data/proof/generate_reference_signal.py
+          echo ""
+          echo "=== Checking data file matches committed version ==="
+          # The regenerated file should be identical to the committed one
+          # (We compare the metadata file since data file is large)
+          python -c "
+          import json, hashlib
+          with open('v1/data/proof/sample_csi_meta.json') as f:
+              meta = json.load(f)
+          assert meta['is_synthetic'] == True, 'Metadata must mark signal as synthetic'
+          assert meta['numpy_seed'] == 42, 'Seed must be 42'
+          print('Reference signal metadata validated.')
+          "
+
+      - name: Run pipeline verification
+        working-directory: v1
+        run: |
+          echo "=== Running pipeline verification ==="
+          python data/proof/verify.py
+          echo ""
+          echo "Pipeline verification PASSED."
+
+      - name: Run verification twice to confirm determinism
+        working-directory: v1
+        run: |
+          echo "=== Second run for determinism confirmation ==="
+          python data/proof/verify.py
+          echo "Determinism confirmed across multiple runs."
+
+      - name: Check for unseeded np.random in production code
+        run: |
+          echo "=== Scanning for unseeded np.random usage in production code ==="
+          # Search for np.random calls without a seed in production code
+          # Exclude test files, proof data generators, and known parser placeholders
+          VIOLATIONS=$(grep -rn "np\.random\." v1/src/ \
+            --include="*.py" \
+            --exclude-dir="__pycache__" \
+            | grep -v "np\.random\.RandomState" \
+            | grep -v "np\.random\.seed" \
+            | grep -v "np\.random\.default_rng" \
+            | grep -v "# placeholder" \
+            | grep -v "# mock" \
+            | grep -v "# test" \
+            || true)
+
+          if [ -n "$VIOLATIONS" ]; then
+            echo ""
+            echo "WARNING: Found potential unseeded np.random usage in production code:"
+            echo "$VIOLATIONS"
+            echo ""
+            echo "Each np.random call should either:"
+            echo "  1. Use np.random.RandomState(seed) or np.random.default_rng(seed)"
+            echo "  2. Be in a test/mock context (add '# placeholder' comment)"
+            echo ""
+            # Note: This is a warning, not a failure, because some existing
+            # placeholder code in parsers uses np.random for mock data.
+            # Once hardware integration is complete, these should be removed.
+            echo "WARNING: Review the above usages. Existing parser placeholders are expected."
+          else
+            echo "No unseeded np.random usage found in production code."
+          fi

.mcp.json

@@ -3,11 +3,13 @@
     "claude-flow": {
       "command": "npx",
       "args": [
+        "-y",
         "@claude-flow/cli@latest",
         "mcp",
         "start"
       ],
       "env": {
+        "npm_config_update_notifier": "false",
         "CLAUDE_FLOW_MODE": "v3",
         "CLAUDE_FLOW_HOOKS_ENABLED": "true",
         "CLAUDE_FLOW_TOPOLOGY": "hierarchical-mesh",

.swarm/schema.sql

@@ -0,0 +1,305 @@
+
+-- Claude Flow V3 Memory Database
+-- Version: 3.0.0
+-- Features: Pattern learning, vector embeddings, temporal decay, migration tracking
+
+PRAGMA journal_mode = WAL;
+PRAGMA synchronous = NORMAL;
+PRAGMA foreign_keys = ON;
+
+-- ============================================
+-- CORE MEMORY TABLES
+-- ============================================
+
+-- Memory entries (main storage)
+CREATE TABLE IF NOT EXISTS memory_entries (
+  id TEXT PRIMARY KEY,
+  key TEXT NOT NULL,
+  namespace TEXT DEFAULT 'default',
+  content TEXT NOT NULL,
+  type TEXT DEFAULT 'semantic' CHECK(type IN ('semantic', 'episodic', 'procedural', 'working', 'pattern')),
+
+  -- Vector embedding for semantic search (stored as JSON array)
+  embedding TEXT,
+  embedding_model TEXT DEFAULT 'local',
+  embedding_dimensions INTEGER,
+
+  -- Metadata
+  tags TEXT, -- JSON array
+  metadata TEXT, -- JSON object
+  owner_id TEXT,
+
+  -- Timestamps
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  updated_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  expires_at INTEGER,
+  last_accessed_at INTEGER,
+
+  -- Access tracking for hot/cold detection
+  access_count INTEGER DEFAULT 0,
+
+  -- Status
+  status TEXT DEFAULT 'active' CHECK(status IN ('active', 'archived', 'deleted')),
+
+  UNIQUE(namespace, key)
+);
+
+-- Indexes for memory entries
+CREATE INDEX IF NOT EXISTS idx_memory_namespace ON memory_entries(namespace);
+CREATE INDEX IF NOT EXISTS idx_memory_key ON memory_entries(key);
+CREATE INDEX IF NOT EXISTS idx_memory_type ON memory_entries(type);
+CREATE INDEX IF NOT EXISTS idx_memory_status ON memory_entries(status);
+CREATE INDEX IF NOT EXISTS idx_memory_created ON memory_entries(created_at);
+CREATE INDEX IF NOT EXISTS idx_memory_accessed ON memory_entries(last_accessed_at);
+CREATE INDEX IF NOT EXISTS idx_memory_owner ON memory_entries(owner_id);
+
+-- ============================================
+-- PATTERN LEARNING TABLES
+-- ============================================
+
+-- Learned patterns with confidence scoring and versioning
+CREATE TABLE IF NOT EXISTS patterns (
+  id TEXT PRIMARY KEY,
+
+  -- Pattern identification
+  name TEXT NOT NULL,
+  pattern_type TEXT NOT NULL CHECK(pattern_type IN (
+    'task-routing', 'error-recovery', 'optimization', 'learning',
+    'coordination', 'prediction', 'code-pattern', 'workflow'
+  )),
+
+  -- Pattern definition
+  condition TEXT NOT NULL, -- Regex or semantic match
+  action TEXT NOT NULL, -- What to do when pattern matches
+  description TEXT,
+
+  -- Confidence scoring (0.0 - 1.0)
+  confidence REAL DEFAULT 0.5,
+  success_count INTEGER DEFAULT 0,
+  failure_count INTEGER DEFAULT 0,
+
+  -- Temporal decay
+  decay_rate REAL DEFAULT 0.01, -- How fast confidence decays
+  half_life_days INTEGER DEFAULT 30, -- Days until confidence halves without use
+
+  -- Vector embedding for semantic pattern matching
+  embedding TEXT,
+  embedding_dimensions INTEGER,
+
+  -- Versioning
+  version INTEGER DEFAULT 1,
+  parent_id TEXT REFERENCES patterns(id),
+
+  -- Metadata
+  tags TEXT, -- JSON array
+  metadata TEXT, -- JSON object
+  source TEXT, -- Where the pattern was learned from
+
+  -- Timestamps
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  updated_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  last_matched_at INTEGER,
+  last_success_at INTEGER,
+  last_failure_at INTEGER,
+
+  -- Status
+  status TEXT DEFAULT 'active' CHECK(status IN ('active', 'archived', 'deprecated', 'experimental'))
+);
+
+-- Indexes for patterns
+CREATE INDEX IF NOT EXISTS idx_patterns_type ON patterns(pattern_type);
+CREATE INDEX IF NOT EXISTS idx_patterns_confidence ON patterns(confidence DESC);
+CREATE INDEX IF NOT EXISTS idx_patterns_status ON patterns(status);
+CREATE INDEX IF NOT EXISTS idx_patterns_last_matched ON patterns(last_matched_at);
+
+-- Pattern evolution history (for versioning)
+CREATE TABLE IF NOT EXISTS pattern_history (
+  id INTEGER PRIMARY KEY AUTOINCREMENT,
+  pattern_id TEXT NOT NULL REFERENCES patterns(id),
+  version INTEGER NOT NULL,
+
+  -- Snapshot of pattern state
+  confidence REAL,
+  success_count INTEGER,
+  failure_count INTEGER,
+  condition TEXT,
+  action TEXT,
+
+  -- What changed
+  change_type TEXT CHECK(change_type IN ('created', 'updated', 'success', 'failure', 'decay', 'merged', 'split')),
+  change_reason TEXT,
+
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000)
+);
+
+CREATE INDEX IF NOT EXISTS idx_pattern_history_pattern ON pattern_history(pattern_id);
+
+-- ============================================
+-- LEARNING & TRAJECTORY TABLES
+-- ============================================
+
+-- Learning trajectories (SONA integration)
+CREATE TABLE IF NOT EXISTS trajectories (
+  id TEXT PRIMARY KEY,
+  session_id TEXT,
+
+  -- Trajectory state
+  status TEXT DEFAULT 'active' CHECK(status IN ('active', 'completed', 'failed', 'abandoned')),
+  verdict TEXT CHECK(verdict IN ('success', 'failure', 'partial', NULL)),
+
+  -- Context
+  task TEXT,
+  context TEXT, -- JSON object
+
+  -- Metrics
+  total_steps INTEGER DEFAULT 0,
+  total_reward REAL DEFAULT 0,
+
+  -- Timestamps
+  started_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  ended_at INTEGER,
+
+  -- Reference to extracted pattern (if any)
+  extracted_pattern_id TEXT REFERENCES patterns(id)
+);
+
+-- Trajectory steps
+CREATE TABLE IF NOT EXISTS trajectory_steps (
+  id INTEGER PRIMARY KEY AUTOINCREMENT,
+  trajectory_id TEXT NOT NULL REFERENCES trajectories(id),
+  step_number INTEGER NOT NULL,
+
+  -- Step data
+  action TEXT NOT NULL,
+  observation TEXT,
+  reward REAL DEFAULT 0,
+
+  -- Metadata
+  metadata TEXT, -- JSON object
+
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000)
+);
+
+CREATE INDEX IF NOT EXISTS idx_steps_trajectory ON trajectory_steps(trajectory_id);
+
+-- ============================================
+-- MIGRATION STATE TRACKING
+-- ============================================
+
+-- Migration state (for resume capability)
+CREATE TABLE IF NOT EXISTS migration_state (
+  id TEXT PRIMARY KEY,
+  migration_type TEXT NOT NULL, -- 'v2-to-v3', 'pattern', 'memory', etc.
+
+  -- Progress tracking
+  status TEXT DEFAULT 'pending' CHECK(status IN ('pending', 'in_progress', 'completed', 'failed', 'rolled_back')),
+  total_items INTEGER DEFAULT 0,
+  processed_items INTEGER DEFAULT 0,
+  failed_items INTEGER DEFAULT 0,
+  skipped_items INTEGER DEFAULT 0,
+
+  -- Current position (for resume)
+  current_batch INTEGER DEFAULT 0,
+  last_processed_id TEXT,
+
+  -- Source/destination info
+  source_path TEXT,
+  source_type TEXT,
+  destination_path TEXT,
+
+  -- Backup info
+  backup_path TEXT,
+  backup_created_at INTEGER,
+
+  -- Error tracking
+  last_error TEXT,
+  errors TEXT, -- JSON array of errors
+
+  -- Timestamps
+  started_at INTEGER,
+  completed_at INTEGER,
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  updated_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000)
+);
+
+-- ============================================
+-- SESSION MANAGEMENT
+-- ============================================
+
+-- Sessions for context persistence
+CREATE TABLE IF NOT EXISTS sessions (
+  id TEXT PRIMARY KEY,
+
+  -- Session state
+  state TEXT NOT NULL, -- JSON object with full session state
+  status TEXT DEFAULT 'active' CHECK(status IN ('active', 'paused', 'completed', 'expired')),
+
+  -- Context
+  project_path TEXT,
+  branch TEXT,
+
+  -- Metrics
+  tasks_completed INTEGER DEFAULT 0,
+  patterns_learned INTEGER DEFAULT 0,
+
+  -- Timestamps
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  updated_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  expires_at INTEGER
+);
+
+-- ============================================
+-- VECTOR INDEX METADATA (for HNSW)
+-- ============================================
+
+-- Track HNSW index state
+CREATE TABLE IF NOT EXISTS vector_indexes (
+  id TEXT PRIMARY KEY,
+  name TEXT NOT NULL UNIQUE,
+
+  -- Index configuration
+  dimensions INTEGER NOT NULL,
+  metric TEXT DEFAULT 'cosine' CHECK(metric IN ('cosine', 'euclidean', 'dot')),
+
+  -- HNSW parameters
+  hnsw_m INTEGER DEFAULT 16,
+  hnsw_ef_construction INTEGER DEFAULT 200,
+  hnsw_ef_search INTEGER DEFAULT 100,
+
+  -- Quantization
+  quantization_type TEXT CHECK(quantization_type IN ('none', 'scalar', 'product')),
+  quantization_bits INTEGER DEFAULT 8,
+
+  -- Statistics
+  total_vectors INTEGER DEFAULT 0,
+  last_rebuild_at INTEGER,
+
+  created_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000),
+  updated_at INTEGER NOT NULL DEFAULT (strftime('%s', 'now') * 1000)
+);
+
+-- ============================================
+-- SYSTEM METADATA
+-- ============================================
+
+CREATE TABLE IF NOT EXISTS metadata (
+  key TEXT PRIMARY KEY,
+  value TEXT NOT NULL,
+  updated_at INTEGER DEFAULT (strftime('%s', 'now') * 1000)
+);
+
+
+INSERT OR REPLACE INTO metadata (key, value) VALUES
+  ('schema_version', '3.0.0'),
+  ('backend', 'hybrid'),
+  ('created_at', '2026-02-28T16:04:25.842Z'),
+  ('sql_js', 'true'),
+  ('vector_embeddings', 'enabled'),
+  ('pattern_learning', 'enabled'),
+  ('temporal_decay', 'enabled'),
+  ('hnsw_indexing', 'enabled');
+
+-- Create default vector index configuration
+INSERT OR IGNORE INTO vector_indexes (id, name, dimensions) VALUES
+  ('default', 'default', 768),
+  ('patterns', 'patterns', 768);

.swarm/state.json

@@ -0,0 +1,8 @@
+{
+  "id": "swarm-1772294837997",
+  "topology": "hierarchical",
+  "maxAgents": 8,
+  "strategy": "specialized",
+  "initializedAt": "2026-02-28T16:07:17.997Z",
+  "status": "ready"
+}

CLAUDE.md

@@ -1,664 +1,239 @@
-# Claude Code Configuration - Claude Flow V3
+# Claude Code Configuration — WiFi-DensePose + Claude Flow V3
 
-## 🚨 AUTOMATIC SWARM ORCHESTRATION
+## Project: wifi-densepose
 
-**When starting work on complex tasks, Claude Code MUST automatically:**
+WiFi-based human pose estimation using Channel State Information (CSI).
+Dual codebase: Python v1 (`v1/`) and Rust port (`rust-port/wifi-densepose-rs/`).
 
-1. **Initialize the swarm** using CLI tools via Bash
-2. **Spawn concurrent agents** using Claude Code's Task tool
-3. **Coordinate via hooks** and memory
+### Key Rust Crates
+- `wifi-densepose-signal` — SOTA signal processing (conjugate mult, Hampel, Fresnel, BVP, spectrogram)
+- `wifi-densepose-train` — Training pipeline with ruvector integration (ADR-016)
+- `wifi-densepose-mat` — Disaster detection module (MAT, multi-AP, triage)
+- `wifi-densepose-nn` — Neural network inference (DensePose head, RCNN)
+- `wifi-densepose-hardware` — ESP32 aggregator, hardware interfaces
 
-### 🚨 CRITICAL: CLI + Task Tool in SAME Message
+### RuVector v2.0.4 Integration (ADR-016 complete, ADR-017 proposed)
+All 5 ruvector crates integrated in workspace:
+- `ruvector-mincut` → `metrics.rs` (DynamicPersonMatcher) + `subcarrier_selection.rs`
+- `ruvector-attn-mincut` → `model.rs` (apply_antenna_attention) + `spectrogram.rs`
+- `ruvector-temporal-tensor` → `dataset.rs` (CompressedCsiBuffer) + `breathing.rs`
+- `ruvector-solver` → `subcarrier.rs` (sparse interpolation 114→56) + `triangulation.rs`
+- `ruvector-attention` → `model.rs` (apply_spatial_attention) + `bvp.rs`
 
-**When user says "spawn swarm" or requests complex work, Claude Code MUST in ONE message:**
-1. Call CLI tools via Bash to initialize coordination
-2. **IMMEDIATELY** call Task tool to spawn REAL working agents
-3. Both CLI and Task calls must be in the SAME response
+### Architecture Decisions
+All ADRs in `docs/adr/` (ADR-001 through ADR-017). Key ones:
+- ADR-014: SOTA signal processing (Accepted)
+- ADR-015: MM-Fi + Wi-Pose training datasets (Accepted)
+- ADR-016: RuVector training pipeline integration (Accepted — complete)
+- ADR-017: RuVector signal + MAT integration (Proposed — next target)
 
-**CLI coordinates, Task tool agents do the actual work!**
-
-### 🛡️ Anti-Drift Config (PREFERRED)
-
-**Use this to prevent agent drift:**
+### Build & Test Commands (this repo)
 ```bash
-npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized
+# Rust — check training crate (no GPU needed)
+cd rust-port/wifi-densepose-rs
+cargo check -p wifi-densepose-train --no-default-features
+
+# Rust — run all tests
+cargo test -p wifi-densepose-train --no-default-features
+
+# Rust — full workspace check
+cargo check --workspace --no-default-features
+
+# Python — proof verification
+python v1/data/proof/verify.py
+
+# Python — test suite
+cd v1 && python -m pytest tests/ -x -q
 ```
-- **hierarchical**: Coordinator catches divergence
-- **max-agents 6-8**: Smaller team = less drift
-- **specialized**: Clear roles, no overlap
-- **consensus**: raft (leader maintains state)
+
+### Branch
+All development on: `claude/validate-code-quality-WNrNw`
 
 ---
 
-### 🔄 Auto-Start Swarm Protocol (Background Execution)
+## Behavioral Rules (Always Enforced)
 
-When the user requests a complex task, **spawn agents in background and WAIT for completion:**
+- Do what has been asked; nothing more, nothing less
+- NEVER create files unless they're absolutely necessary for achieving your goal
+- ALWAYS prefer editing an existing file to creating a new one
+- NEVER proactively create documentation files (*.md) or README files unless explicitly requested
+- NEVER save working files, text/mds, or tests to the root folder
+- Never continuously check status after spawning a swarm — wait for results
+- ALWAYS read a file before editing it
+- NEVER commit secrets, credentials, or .env files
 
-```javascript
-// STEP 1: Initialize swarm coordination (anti-drift config)
-Bash("npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized")
+## File Organization
 
-// STEP 2: Spawn ALL agents IN BACKGROUND in a SINGLE message
-// Use run_in_background: true so agents work concurrently
-Task({
-  prompt: "Research requirements, analyze codebase patterns, store findings in memory",
-  subagent_type: "researcher",
-  description: "Research phase",
-  run_in_background: true  // ← CRITICAL: Run in background
-})
-Task({
-  prompt: "Design architecture based on research. Document decisions.",
-  subagent_type: "system-architect",
-  description: "Architecture phase",
-  run_in_background: true
-})
-Task({
-  prompt: "Implement the solution following the design. Write clean code.",
-  subagent_type: "coder",
-  description: "Implementation phase",
-  run_in_background: true
-})
-Task({
-  prompt: "Write comprehensive tests for the implementation.",
-  subagent_type: "tester",
-  description: "Testing phase",
-  run_in_background: true
-})
-Task({
-  prompt: "Review code quality, security, and best practices.",
-  subagent_type: "reviewer",
-  description: "Review phase",
-  run_in_background: true
-})
+- NEVER save to root folder — use the directories below
+- `docs/adr/` — Architecture Decision Records
+- `rust-port/wifi-densepose-rs/crates/` — Rust workspace crates (signal, train, mat, nn, hardware)
+- `v1/src/` — Python source (core, hardware, services, api)
+- `v1/data/proof/` — Deterministic CSI proof bundles
+- `.claude-flow/` — Claude Flow coordination state (committed for team sharing)
+- `.claude/` — Claude Code settings, agents, memory (committed for team sharing)
 
-// STEP 3: WAIT - Tell user agents are working, then STOP
-// Say: "I've spawned 5 agents to work on this in parallel. They'll report back when done."
-// DO NOT check status repeatedly. Just wait for user or agent responses.
-```
+## Project Architecture
 
-### ⏸️ CRITICAL: Spawn and Wait Pattern
+- Follow Domain-Driven Design with bounded contexts
+- Keep files under 500 lines
+- Use typed interfaces for all public APIs
+- Prefer TDD London School (mock-first) for new code
+- Use event sourcing for state changes
+- Ensure input validation at system boundaries
 
-**After spawning background agents:**
+### Project Config
 
-1. **TELL USER** - "I've spawned X agents working in parallel on: [list tasks]"
-2. **STOP** - Do not continue with more tool calls
-3. **WAIT** - Let the background agents complete their work
-4. **RESPOND** - When agents return results, review and synthesize
-
-**Example response after spawning:**
-```
-I've launched 5 concurrent agents to work on this:
-- 🔍 Researcher: Analyzing requirements and codebase
-- 🏗️ Architect: Designing the implementation approach
-- 💻 Coder: Implementing the solution
-- 🧪 Tester: Writing tests
-- 👀 Reviewer: Code review and security check
-
-They're working in parallel. I'll synthesize their results when they complete.
-```
-
-### 🚫 DO NOT:
-- Continuously check swarm status
-- Poll TaskOutput repeatedly
-- Add more tool calls after spawning
-- Ask "should I check on the agents?"
-
-### ✅ DO:
-- Spawn all agents in ONE message
-- Tell user what's happening
-- Wait for agent results to arrive
-- Synthesize results when they return
-
-## 🧠 AUTO-LEARNING PROTOCOL
-
-### Before Starting Any Task
-```bash
-# 1. Search memory for relevant patterns from past successes
-Bash("npx @claude-flow/cli@latest memory search --query '[task keywords]' --namespace patterns")
-
-# 2. Check if similar task was done before
-Bash("npx @claude-flow/cli@latest memory search --query '[task type]' --namespace tasks")
-
-# 3. Load learned optimizations
-Bash("npx @claude-flow/cli@latest hooks route --task '[task description]'")
-```
-
-### After Completing Any Task Successfully
-```bash
-# 1. Store successful pattern for future reference
-Bash("npx @claude-flow/cli@latest memory store --namespace patterns --key '[pattern-name]' --value '[what worked]'")
-
-# 2. Train neural patterns on the successful approach
-Bash("npx @claude-flow/cli@latest hooks post-edit --file '[main-file]' --train-neural true")
-
-# 3. Record task completion with metrics
-Bash("npx @claude-flow/cli@latest hooks post-task --task-id '[id]' --success true --store-results true")
-
-# 4. Trigger optimization worker if performance-related
-Bash("npx @claude-flow/cli@latest hooks worker dispatch --trigger optimize")
-```
-
-### Continuous Improvement Triggers
-
-| Trigger | Worker | When to Use |
-|---------|--------|-------------|
-| After major refactor | `optimize` | Performance optimization |
-| After adding features | `testgaps` | Find missing test coverage |
-| After security changes | `audit` | Security analysis |
-| After API changes | `document` | Update documentation |
-| Every 5+ file changes | `map` | Update codebase map |
-| Complex debugging | `deepdive` | Deep code analysis |
-
-### Memory-Enhanced Development
-
-**ALWAYS check memory before:**
-- Starting a new feature (search for similar implementations)
-- Debugging an issue (search for past solutions)
-- Refactoring code (search for learned patterns)
-- Performance work (search for optimization strategies)
-
-**ALWAYS store in memory after:**
-- Solving a tricky bug (store the solution pattern)
-- Completing a feature (store the approach)
-- Finding a performance fix (store the optimization)
-- Discovering a security issue (store the vulnerability pattern)
-
-### 📋 Agent Routing (Anti-Drift)
-
-| Code | Task | Agents |
-|------|------|--------|
-| 1 | Bug Fix | coordinator, researcher, coder, tester |
-| 3 | Feature | coordinator, architect, coder, tester, reviewer |
-| 5 | Refactor | coordinator, architect, coder, reviewer |
-| 7 | Performance | coordinator, perf-engineer, coder |
-| 9 | Security | coordinator, security-architect, auditor |
-| 11 | Docs | researcher, api-docs |
-
-**Codes 1-9: hierarchical/specialized (anti-drift). Code 11: mesh/balanced**
-
-### 🎯 Task Complexity Detection
-
-**AUTO-INVOKE SWARM when task involves:**
-- Multiple files (3+)
-- New feature implementation
-- Refactoring across modules
-- API changes with tests
-- Security-related changes
-- Performance optimization
-- Database schema changes
-
-**SKIP SWARM for:**
-- Single file edits
-- Simple bug fixes (1-2 lines)
-- Documentation updates
-- Configuration changes
-- Quick questions/exploration
-
-## 🚨 CRITICAL: CONCURRENT EXECUTION & FILE MANAGEMENT
-
-**ABSOLUTE RULES**:
-1. ALL operations MUST be concurrent/parallel in a single message
-2. **NEVER save working files, text/mds and tests to the root folder**
-3. ALWAYS organize files in appropriate subdirectories
-4. **USE CLAUDE CODE'S TASK TOOL** for spawning agents concurrently, not just MCP
-
-### ⚡ GOLDEN RULE: "1 MESSAGE = ALL RELATED OPERATIONS"
-
-**MANDATORY PATTERNS:**
-- **TodoWrite**: ALWAYS batch ALL todos in ONE call (5-10+ todos minimum)
-- **Task tool (Claude Code)**: ALWAYS spawn ALL agents in ONE message with full instructions
-- **File operations**: ALWAYS batch ALL reads/writes/edits in ONE message
-- **Bash commands**: ALWAYS batch ALL terminal operations in ONE message
-- **Memory operations**: ALWAYS batch ALL memory store/retrieve in ONE message
-
-### 📁 File Organization Rules
-
-**NEVER save to root folder. Use these directories:**
-- `/src` - Source code files
-- `/tests` - Test files
-- `/docs` - Documentation and markdown files
-- `/config` - Configuration files
-- `/scripts` - Utility scripts
-- `/examples` - Example code
-
-## Project Config (Anti-Drift Defaults)
-
-- **Topology**: hierarchical (prevents drift)
-- **Max Agents**: 8 (smaller = less drift)
-- **Strategy**: specialized (clear roles)
-- **Consensus**: raft
+- **Topology**: hierarchical-mesh
+- **Max Agents**: 15
 - **Memory**: hybrid
 - **HNSW**: Enabled
 - **Neural**: Enabled
 
-## 🚀 V3 CLI Commands (26 Commands, 140+ Subcommands)
+## Build & Test
+
+```bash
+# Build
+npm run build
+
+# Test
+npm test
+
+# Lint
+npm run lint
+```
+
+- ALWAYS run tests after making code changes
+- ALWAYS verify build succeeds before committing
+
+## Security Rules
+
+- NEVER hardcode API keys, secrets, or credentials in source files
+- NEVER commit .env files or any file containing secrets
+- Always validate user input at system boundaries
+- Always sanitize file paths to prevent directory traversal
+- Run `npx @claude-flow/cli@latest security scan` after security-related changes
+
+## Concurrency: 1 MESSAGE = ALL RELATED OPERATIONS
+
+- All operations MUST be concurrent/parallel in a single message
+- Use Claude Code's Task tool for spawning agents, not just MCP
+- ALWAYS batch ALL todos in ONE TodoWrite call (5-10+ minimum)
+- ALWAYS spawn ALL agents in ONE message with full instructions via Task tool
+- ALWAYS batch ALL file reads/writes/edits in ONE message
+- ALWAYS batch ALL Bash commands in ONE message
+
+## Swarm Orchestration
+
+- MUST initialize the swarm using CLI tools when starting complex tasks
+- MUST spawn concurrent agents using Claude Code's Task tool
+- Never use CLI tools alone for execution — Task tool agents do the actual work
+- MUST call CLI tools AND Task tool in ONE message for complex work
+
+### 3-Tier Model Routing (ADR-026)
+
+| Tier | Handler | Latency | Cost | Use Cases |
+|------|---------|---------|------|-----------|
+| **1** | Agent Booster (WASM) | <1ms | $0 | Simple transforms (var→const, add types) — Skip LLM |
+| **2** | Haiku | ~500ms | $0.0002 | Simple tasks, low complexity (<30%) |
+| **3** | Sonnet/Opus | 2-5s | $0.003-0.015 | Complex reasoning, architecture, security (>30%) |
+
+- Always check for `[AGENT_BOOSTER_AVAILABLE]` or `[TASK_MODEL_RECOMMENDATION]` before spawning agents
+- Use Edit tool directly when `[AGENT_BOOSTER_AVAILABLE]`
+
+## Swarm Configuration & Anti-Drift
+
+- ALWAYS use hierarchical topology for coding swarms
+- Keep maxAgents at 6-8 for tight coordination
+- Use specialized strategy for clear role boundaries
+- Use `raft` consensus for hive-mind (leader maintains authoritative state)
+- Run frequent checkpoints via `post-task` hooks
+- Keep shared memory namespace for all agents
+
+```bash
+npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized
+```
+
+## Swarm Execution Rules
+
+- ALWAYS use `run_in_background: true` for all agent Task calls
+- ALWAYS put ALL agent Task calls in ONE message for parallel execution
+- After spawning, STOP — do NOT add more tool calls or check status
+- Never poll TaskOutput or check swarm status — trust agents to return
+- When agent results arrive, review ALL results before proceeding
+
+## V3 CLI Commands
 
 ### Core Commands
 
 | Command | Subcommands | Description |
 |---------|-------------|-------------|
-| `init` | 4 | Project initialization with wizard, presets, skills, hooks |
-| `agent` | 8 | Agent lifecycle (spawn, list, status, stop, metrics, pool, health, logs) |
-| `swarm` | 6 | Multi-agent swarm coordination and orchestration |
-| `memory` | 11 | AgentDB memory with vector search (150x-12,500x faster) |
-| `mcp` | 9 | MCP server management and tool execution |
-| `task` | 6 | Task creation, assignment, and lifecycle |
-| `session` | 7 | Session state management and persistence |
-| `config` | 7 | Configuration management and provider setup |
-| `status` | 3 | System status monitoring with watch mode |
-| `workflow` | 6 | Workflow execution and template management |
-| `hooks` | 17 | Self-learning hooks + 12 background workers |
-| `hive-mind` | 6 | Queen-led Byzantine fault-tolerant consensus |
-
-### Advanced Commands
-
-| Command | Subcommands | Description |
-|---------|-------------|-------------|
-| `daemon` | 5 | Background worker daemon (start, stop, status, trigger, enable) |
-| `neural` | 5 | Neural pattern training (train, status, patterns, predict, optimize) |
-| `security` | 6 | Security scanning (scan, audit, cve, threats, validate, report) |
-| `performance` | 5 | Performance profiling (benchmark, profile, metrics, optimize, report) |
-| `providers` | 5 | AI providers (list, add, remove, test, configure) |
-| `plugins` | 5 | Plugin management (list, install, uninstall, enable, disable) |
-| `deployment` | 5 | Deployment management (deploy, rollback, status, environments, release) |
-| `embeddings` | 4 | Vector embeddings (embed, batch, search, init) - 75x faster with agentic-flow |
-| `claims` | 4 | Claims-based authorization (check, grant, revoke, list) |
-| `migrate` | 5 | V2 to V3 migration with rollback support |
-| `doctor` | 1 | System diagnostics with health checks |
-| `completions` | 4 | Shell completions (bash, zsh, fish, powershell) |
+| `init` | 4 | Project initialization |
+| `agent` | 8 | Agent lifecycle management |
+| `swarm` | 6 | Multi-agent swarm coordination |
+| `memory` | 11 | AgentDB memory with HNSW search |
+| `task` | 6 | Task creation and lifecycle |
+| `session` | 7 | Session state management |
+| `hooks` | 17 | Self-learning hooks + 12 workers |
+| `hive-mind` | 6 | Byzantine fault-tolerant consensus |
 
 ### Quick CLI Examples
 
 ```bash
-# Initialize project
 npx @claude-flow/cli@latest init --wizard
-
-# Start daemon with background workers
-npx @claude-flow/cli@latest daemon start
-
-# Spawn an agent
 npx @claude-flow/cli@latest agent spawn -t coder --name my-coder
-
-# Initialize swarm
 npx @claude-flow/cli@latest swarm init --v3-mode
-
-# Search memory (HNSW-indexed)
 npx @claude-flow/cli@latest memory search --query "authentication patterns"
-
-# System diagnostics
 npx @claude-flow/cli@latest doctor --fix
-
-# Security scan
-npx @claude-flow/cli@latest security scan --depth full
-
-# Performance benchmark
-npx @claude-flow/cli@latest performance benchmark --suite all
 ```
 
-## 🚀 Available Agents (60+ Types)
+## Available Agents (60+ Types)
 
 ### Core Development
 `coder`, `reviewer`, `tester`, `planner`, `researcher`
 
-### V3 Specialized Agents
+### Specialized
 `security-architect`, `security-auditor`, `memory-specialist`, `performance-engineer`
 
-### 🔐 @claude-flow/security
-CVE remediation, input validation, path security:
-- `InputValidator` - Zod validation
-- `PathValidator` - Traversal prevention
-- `SafeExecutor` - Injection protection
-
 ### Swarm Coordination
-`hierarchical-coordinator`, `mesh-coordinator`, `adaptive-coordinator`, `collective-intelligence-coordinator`, `swarm-memory-manager`
-
-### Consensus & Distributed
-`byzantine-coordinator`, `raft-manager`, `gossip-coordinator`, `consensus-builder`, `crdt-synchronizer`, `quorum-manager`, `security-manager`
-
-### Performance & Optimization
-`perf-analyzer`, `performance-benchmarker`, `task-orchestrator`, `memory-coordinator`, `smart-agent`
+`hierarchical-coordinator`, `mesh-coordinator`, `adaptive-coordinator`
 
 ### GitHub & Repository
-`github-modes`, `pr-manager`, `code-review-swarm`, `issue-tracker`, `release-manager`, `workflow-automation`, `project-board-sync`, `repo-architect`, `multi-repo-swarm`
+`pr-manager`, `code-review-swarm`, `issue-tracker`, `release-manager`
 
 ### SPARC Methodology
-`sparc-coord`, `sparc-coder`, `specification`, `pseudocode`, `architecture`, `refinement`
+`sparc-coord`, `sparc-coder`, `specification`, `pseudocode`, `architecture`
 
-### Specialized Development
-`backend-dev`, `mobile-dev`, `ml-developer`, `cicd-engineer`, `api-docs`, `system-architect`, `code-analyzer`, `base-template-generator`
-
-### Testing & Validation
-`tdd-london-swarm`, `production-validator`
-
-## 🪝 V3 Hooks System (27 Hooks + 12 Workers)
-
-### All Available Hooks
-
-| Hook | Description | Key Options |
-|------|-------------|-------------|
-| `pre-edit` | Get context before editing files | `--file`, `--operation` |
-| `post-edit` | Record editing outcome for learning | `--file`, `--success`, `--train-neural` |
-| `pre-command` | Assess risk before commands | `--command`, `--validate-safety` |
-| `post-command` | Record command execution outcome | `--command`, `--track-metrics` |
-| `pre-task` | Record task start, get agent suggestions | `--description`, `--coordinate-swarm` |
-| `post-task` | Record task completion for learning | `--task-id`, `--success`, `--store-results` |
-| `session-start` | Start/restore session (v2 compat) | `--session-id`, `--auto-configure` |
-| `session-end` | End session and persist state | `--generate-summary`, `--export-metrics` |
-| `session-restore` | Restore a previous session | `--session-id`, `--latest` |
-| `route` | Route task to optimal agent | `--task`, `--context`, `--top-k` |
-| `route-task` | (v2 compat) Alias for route | `--task`, `--auto-swarm` |
-| `explain` | Explain routing decision | `--topic`, `--detailed` |
-| `pretrain` | Bootstrap intelligence from repo | `--model-type`, `--epochs` |
-| `build-agents` | Generate optimized agent configs | `--agent-types`, `--focus` |
-| `metrics` | View learning metrics dashboard | `--v3-dashboard`, `--format` |
-| `transfer` | Transfer patterns via IPFS registry | `store`, `from-project` |
-| `list` | List all registered hooks | `--format` |
-| `intelligence` | RuVector intelligence system | `trajectory-*`, `pattern-*`, `stats` |
-| `worker` | Background worker management | `list`, `dispatch`, `status`, `detect` |
-| `progress` | Check V3 implementation progress | `--detailed`, `--format` |
-| `statusline` | Generate dynamic statusline | `--json`, `--compact`, `--no-color` |
-| `coverage-route` | Route based on test coverage gaps | `--task`, `--path` |
-| `coverage-suggest` | Suggest coverage improvements | `--path` |
-| `coverage-gaps` | List coverage gaps with priorities | `--format`, `--limit` |
-| `pre-bash` | (v2 compat) Alias for pre-command | Same as pre-command |
-| `post-bash` | (v2 compat) Alias for post-command | Same as post-command |
-
-### 12 Background Workers
-
-| Worker | Priority | Description |
-|--------|----------|-------------|
-| `ultralearn` | normal | Deep knowledge acquisition |
-| `optimize` | high | Performance optimization |
-| `consolidate` | low | Memory consolidation |
-| `predict` | normal | Predictive preloading |
-| `audit` | critical | Security analysis |
-| `map` | normal | Codebase mapping |
-| `preload` | low | Resource preloading |
-| `deepdive` | normal | Deep code analysis |
-| `document` | normal | Auto-documentation |
-| `refactor` | normal | Refactoring suggestions |
-| `benchmark` | normal | Performance benchmarking |
-| `testgaps` | normal | Test coverage analysis |
-
-### Essential Hook Commands
+## Memory Commands Reference
 
 ```bash
-# Core hooks
-npx @claude-flow/cli@latest hooks pre-task --description "[task]"
-npx @claude-flow/cli@latest hooks post-task --task-id "[id]" --success true
-npx @claude-flow/cli@latest hooks post-edit --file "[file]" --train-neural true
+# Store (REQUIRED: --key, --value; OPTIONAL: --namespace, --ttl, --tags)
+npx @claude-flow/cli@latest memory store --key "pattern-auth" --value "JWT with refresh" --namespace patterns
 
-# Session management
-npx @claude-flow/cli@latest hooks session-start --session-id "[id]"
-npx @claude-flow/cli@latest hooks session-end --export-metrics true
-npx @claude-flow/cli@latest hooks session-restore --session-id "[id]"
-
-# Intelligence routing
-npx @claude-flow/cli@latest hooks route --task "[task]"
-npx @claude-flow/cli@latest hooks explain --topic "[topic]"
-
-# Neural learning
-npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10
-npx @claude-flow/cli@latest hooks build-agents --agent-types coder,tester
-
-# Background workers
-npx @claude-flow/cli@latest hooks worker list
-npx @claude-flow/cli@latest hooks worker dispatch --trigger audit
-npx @claude-flow/cli@latest hooks worker status
-
-# Coverage-aware routing
-npx @claude-flow/cli@latest hooks coverage-gaps --format table
-npx @claude-flow/cli@latest hooks coverage-route --task "[task]"
-
-# Statusline (for Claude Code integration)
-npx @claude-flow/cli@latest hooks statusline
-npx @claude-flow/cli@latest hooks statusline --json
-```
-
-## 🔄 Migration (V2 to V3)
-
-```bash
-# Check migration status
-npx @claude-flow/cli@latest migrate status
-
-# Run migration with backup
-npx @claude-flow/cli@latest migrate run --backup
-
-# Rollback if needed
-npx @claude-flow/cli@latest migrate rollback
-
-# Validate migration
-npx @claude-flow/cli@latest migrate validate
-```
-
-## 🧠 Intelligence System (RuVector)
-
-V3 includes the RuVector Intelligence System:
-- **SONA**: Self-Optimizing Neural Architecture (<0.05ms adaptation)
-- **MoE**: Mixture of Experts for specialized routing
-- **HNSW**: 150x-12,500x faster pattern search
-- **EWC++**: Elastic Weight Consolidation (prevents forgetting)
-- **Flash Attention**: 2.49x-7.47x speedup
-
-The 4-step intelligence pipeline:
-1. **RETRIEVE** - Fetch relevant patterns via HNSW
-2. **JUDGE** - Evaluate with verdicts (success/failure)
-3. **DISTILL** - Extract key learnings via LoRA
-4. **CONSOLIDATE** - Prevent catastrophic forgetting via EWC++
-
-## 📦 Embeddings Package (v3.0.0-alpha.12)
-
-Features:
-- **sql.js**: Cross-platform SQLite persistent cache (WASM, no native compilation)
-- **Document chunking**: Configurable overlap and size
-- **Normalization**: L2, L1, min-max, z-score
-- **Hyperbolic embeddings**: Poincaré ball model for hierarchical data
-- **75x faster**: With agentic-flow ONNX integration
-- **Neural substrate**: Integration with RuVector
-
-## 🐝 Hive-Mind Consensus
-
-### Topologies
-- `hierarchical` - Queen controls workers directly
-- `mesh` - Fully connected peer network
-- `hierarchical-mesh` - Hybrid (recommended)
-- `adaptive` - Dynamic based on load
-
-### Consensus Strategies
-- `byzantine` - BFT (tolerates f < n/3 faulty)
-- `raft` - Leader-based (tolerates f < n/2)
-- `gossip` - Epidemic for eventual consistency
-- `crdt` - Conflict-free replicated data types
-- `quorum` - Configurable quorum-based
-
-## V3 Performance Targets
-
-| Metric | Target |
-|--------|--------|
-| Flash Attention | 2.49x-7.47x speedup |
-| HNSW Search | 150x-12,500x faster |
-| Memory Reduction | 50-75% with quantization |
-| MCP Response | <100ms |
-| CLI Startup | <500ms |
-| SONA Adaptation | <0.05ms |
-
-## 📊 Performance Optimization Protocol
-
-### Automatic Performance Tracking
-```bash
-# After any significant operation, track metrics
-Bash("npx @claude-flow/cli@latest hooks post-command --command '[operation]' --track-metrics true")
-
-# Periodically run benchmarks (every major feature)
-Bash("npx @claude-flow/cli@latest performance benchmark --suite all")
-
-# Analyze bottlenecks when performance degrades
-Bash("npx @claude-flow/cli@latest performance profile --target '[component]'")
-```
-
-### Session Persistence (Cross-Conversation Learning)
-```bash
-# At session start - restore previous context
-Bash("npx @claude-flow/cli@latest session restore --latest")
-
-# At session end - persist learned patterns
-Bash("npx @claude-flow/cli@latest hooks session-end --generate-summary true --persist-state true --export-metrics true")
-```
-
-### Neural Pattern Training
-```bash
-# Train on successful code patterns
-Bash("npx @claude-flow/cli@latest neural train --pattern-type coordination --epochs 10")
-
-# Predict optimal approach for new tasks
-Bash("npx @claude-flow/cli@latest neural predict --input '[task description]'")
-
-# View learned patterns
-Bash("npx @claude-flow/cli@latest neural patterns --list")
-```
-
-## 🔧 Environment Variables
-
-```bash
-# Configuration
-CLAUDE_FLOW_CONFIG=./claude-flow.config.json
-CLAUDE_FLOW_LOG_LEVEL=info
-
-# Provider API Keys
-ANTHROPIC_API_KEY=sk-ant-...
-OPENAI_API_KEY=sk-...
-GOOGLE_API_KEY=...
-
-# MCP Server
-CLAUDE_FLOW_MCP_PORT=3000
-CLAUDE_FLOW_MCP_HOST=localhost
-CLAUDE_FLOW_MCP_TRANSPORT=stdio
-
-# Memory
-CLAUDE_FLOW_MEMORY_BACKEND=hybrid
-CLAUDE_FLOW_MEMORY_PATH=./data/memory
-```
-
-## 🔍 Doctor Health Checks
-
-Run `npx @claude-flow/cli@latest doctor` to check:
-- Node.js version (20+)
-- npm version (9+)
-- Git installation
-- Config file validity
-- Daemon status
-- Memory database
-- API keys
-- MCP servers
-- Disk space
-- TypeScript installation
-
-## 🚀 Quick Setup
-
-```bash
-# Add MCP servers (auto-detects MCP mode when stdin is piped)
-claude mcp add claude-flow -- npx -y @claude-flow/cli@latest
-claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start  # Optional
-claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start  # Optional
-
-# Start daemon
-npx @claude-flow/cli@latest daemon start
-
-# Run doctor
-npx @claude-flow/cli@latest doctor --fix
-```
-
-## 🎯 Claude Code vs CLI Tools
-
-### Claude Code Handles ALL EXECUTION:
-- **Task tool**: Spawn and run agents concurrently
-- File operations (Read, Write, Edit, MultiEdit, Glob, Grep)
-- Code generation and programming
-- Bash commands and system operations
-- TodoWrite and task management
-- Git operations
-
-### CLI Tools Handle Coordination (via Bash):
-- **Swarm init**: `npx @claude-flow/cli@latest swarm init --topology <type>`
-- **Swarm status**: `npx @claude-flow/cli@latest swarm status`
-- **Agent spawn**: `npx @claude-flow/cli@latest agent spawn -t <type> --name <name>`
-- **Memory store**: `npx @claude-flow/cli@latest memory store --key "mykey" --value "myvalue" --namespace patterns`
-- **Memory search**: `npx @claude-flow/cli@latest memory search --query "search terms"`
-- **Memory list**: `npx @claude-flow/cli@latest memory list --namespace patterns`
-- **Memory retrieve**: `npx @claude-flow/cli@latest memory retrieve --key "mykey" --namespace patterns`
-- **Hooks**: `npx @claude-flow/cli@latest hooks <hook-name> [options]`
-
-## 📝 Memory Commands Reference (IMPORTANT)
-
-### Store Data (ALL options shown)
-```bash
-# REQUIRED: --key and --value
-# OPTIONAL: --namespace (default: "default"), --ttl, --tags
-npx @claude-flow/cli@latest memory store --key "pattern-auth" --value "JWT with refresh tokens" --namespace patterns
-npx @claude-flow/cli@latest memory store --key "bug-fix-123" --value "Fixed null check" --namespace solutions --tags "bugfix,auth"
-```
-
-### Search Data (semantic vector search)
-```bash
-# REQUIRED: --query (full flag, not -q)
-# OPTIONAL: --namespace, --limit, --threshold
+# Search (REQUIRED: --query; OPTIONAL: --namespace, --limit, --threshold)
 npx @claude-flow/cli@latest memory search --query "authentication patterns"
-npx @claude-flow/cli@latest memory search --query "error handling" --namespace patterns --limit 5
-```
 
-### List Entries
-```bash
-# OPTIONAL: --namespace, --limit
-npx @claude-flow/cli@latest memory list
+# List (OPTIONAL: --namespace, --limit)
 npx @claude-flow/cli@latest memory list --namespace patterns --limit 10
-```
 
-### Retrieve Specific Entry
-```bash
-# REQUIRED: --key
-# OPTIONAL: --namespace (default: "default")
-npx @claude-flow/cli@latest memory retrieve --key "pattern-auth"
+# Retrieve (REQUIRED: --key; OPTIONAL: --namespace)
 npx @claude-flow/cli@latest memory retrieve --key "pattern-auth" --namespace patterns
 ```
 
-### Initialize Memory Database
+## Quick Setup
+
 ```bash
-npx @claude-flow/cli@latest memory init --force --verbose
+claude mcp add claude-flow -- npx -y @claude-flow/cli@latest
+npx @claude-flow/cli@latest daemon start
+npx @claude-flow/cli@latest doctor --fix
 ```
 
-**KEY**: CLI coordinates the strategy via Bash, Claude Code's Task tool executes with real agents.
+## Claude Code vs CLI Tools
+
+- Claude Code's Task tool handles ALL execution: agents, file ops, code generation, git
+- CLI tools handle coordination via Bash: swarm init, memory, hooks, routing
+- NEVER use CLI tools as a substitute for Task tool agents
 
 ## Support
 
 - Documentation: https://github.com/ruvnet/claude-flow
 - Issues: https://github.com/ruvnet/claude-flow/issues
-
----
-
-Remember: **Claude Flow CLI coordinates, Claude Code Task tool creates!**
-
-# important-instruction-reminders
-Do what has been asked; nothing more, nothing less.
-NEVER create files unless they're absolutely necessary for achieving your goal.
-ALWAYS prefer editing an existing file to creating a new one.
-NEVER proactively create documentation files (*.md) or README files. Only create documentation files if explicitly requested by the User.
-Never save working files, text/mds and tests to the root folder.
-
-## 🚨 SWARM EXECUTION RULES (CRITICAL)
-1. **SPAWN IN BACKGROUND**: Use `run_in_background: true` for all agent Task calls
-2. **SPAWN ALL AT ONCE**: Put ALL agent Task calls in ONE message for parallel execution
-3. **TELL USER**: After spawning, list what each agent is doing (use emojis for clarity)
-4. **STOP AND WAIT**: After spawning, STOP - do NOT add more tool calls or check status
-5. **NO POLLING**: Never poll TaskOutput or check swarm status - trust agents to return
-6. **SYNTHESIZE**: When agent results arrive, review ALL results before proceeding
-7. **NO CONFIRMATION**: Don't ask "should I check?" - just wait for results
-
-Example spawn message:
-```
-"I've launched 4 agents in background:
-- 🔍 Researcher: [task]
-- 💻 Coder: [task]
-- 🧪 Tester: [task]
-- 👀 Reviewer: [task]
-Working in parallel - I'll synthesize when they complete."
-```

Makefile

@@ -0,0 +1,123 @@
+# WiFi-DensePose Makefile
+# ============================================================
+
+.PHONY: verify verify-verbose verify-audit install install-verify install-python \
+        install-rust install-browser install-docker install-field install-full \
+        check build-rust build-wasm test-rust bench run-api run-viz clean help
+
+# ─── Installation ────────────────────────────────────────────
+# Guided interactive installer
+install:
+	@./install.sh
+
+# Profile-specific installs (non-interactive)
+install-verify:
+	@./install.sh --profile verify --yes
+
+install-python:
+	@./install.sh --profile python --yes
+
+install-rust:
+	@./install.sh --profile rust --yes
+
+install-browser:
+	@./install.sh --profile browser --yes
+
+install-docker:
+	@./install.sh --profile docker --yes
+
+install-field:
+	@./install.sh --profile field --yes
+
+install-full:
+	@./install.sh --profile full --yes
+
+# Hardware and environment check only (no install)
+check:
+	@./install.sh --check-only
+
+# ─── Verification ────────────────────────────────────────────
+# Trust Kill Switch -- one-command proof replay
+verify:
+	@./verify
+
+# Verbose mode -- show detailed feature statistics and Doppler spectrum
+verify-verbose:
+	@./verify --verbose
+
+# Full audit -- verify pipeline + scan codebase for mock/random patterns
+verify-audit:
+	@./verify --verbose --audit
+
+# ─── Rust Builds ─────────────────────────────────────────────
+build-rust:
+	cd rust-port/wifi-densepose-rs && cargo build --release
+
+build-wasm:
+	cd rust-port/wifi-densepose-rs && wasm-pack build crates/wifi-densepose-wasm --target web --release
+
+build-wasm-mat:
+	cd rust-port/wifi-densepose-rs && wasm-pack build crates/wifi-densepose-wasm --target web --release -- --features mat
+
+test-rust:
+	cd rust-port/wifi-densepose-rs && cargo test --workspace
+
+bench:
+	cd rust-port/wifi-densepose-rs && cargo bench --package wifi-densepose-signal
+
+# ─── Run ─────────────────────────────────────────────────────
+run-api:
+	uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000
+
+run-api-dev:
+	uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000 --reload
+
+run-viz:
+	python3 -m http.server 3000 --directory ui
+
+run-docker:
+	docker compose up
+
+# ─── Clean ───────────────────────────────────────────────────
+clean:
+	rm -f .install.log
+	cd rust-port/wifi-densepose-rs && cargo clean 2>/dev/null || true
+
+# ─── Help ────────────────────────────────────────────────────
+help:
+	@echo "WiFi-DensePose Build Targets"
+	@echo "============================================================"
+	@echo ""
+	@echo "  Installation:"
+	@echo "    make install          Interactive guided installer"
+	@echo "    make install-verify   Verification only (~5 MB)"
+	@echo "    make install-python   Full Python pipeline (~500 MB)"
+	@echo "    make install-rust     Rust pipeline with ~810x speedup"
+	@echo "    make install-browser  WASM for browser (~10 MB)"
+	@echo "    make install-docker   Docker-based deployment"
+	@echo "    make install-field    WiFi-Mat disaster kit (~62 MB)"
+	@echo "    make install-full     Everything available"
+	@echo "    make check            Hardware/environment check only"
+	@echo ""
+	@echo "  Verification:"
+	@echo "    make verify           Run the trust kill switch"
+	@echo "    make verify-verbose   Verbose with feature details"
+	@echo "    make verify-audit     Full verification + codebase audit"
+	@echo ""
+	@echo "  Build:"
+	@echo "    make build-rust       Build Rust workspace (release)"
+	@echo "    make build-wasm       Build WASM package (browser)"
+	@echo "    make build-wasm-mat   Build WASM with WiFi-Mat (field)"
+	@echo "    make test-rust        Run all Rust tests"
+	@echo "    make bench            Run signal processing benchmarks"
+	@echo ""
+	@echo "  Run:"
+	@echo "    make run-api          Start Python API server"
+	@echo "    make run-api-dev      Start API with hot-reload"
+	@echo "    make run-viz          Serve 3D visualization (port 3000)"
+	@echo "    make run-docker       Start Docker dev stack"
+	@echo ""
+	@echo "  Utility:"
+	@echo "    make clean            Remove build artifacts"
+	@echo "    make help             Show this help"
+	@echo ""

README.md

@@ -1,5 +1,17 @@
 # WiFi DensePose
 
+> **Hardware Required:** This system processes real WiFi Channel State Information (CSI) data. To capture live CSI you need one of:
+>
+> | Option | Hardware | Cost | Capabilities |
+> |--------|----------|------|-------------|
+> | **ESP32 Mesh** (recommended) | 3-6x ESP32-S3 boards + consumer WiFi router | ~$54 | Presence, motion, respiration detection |
+> | **Research NIC** | Intel 5300 or Atheros AR9580 (discontinued) | ~$50-100 | Full CSI with 3x3 MIMO |
+> | **Commodity WiFi** | Any Linux laptop with WiFi | $0 | Presence and coarse motion only (RSSI-based) |
+>
+> Without CSI-capable hardware, you can verify the signal processing pipeline using the included deterministic reference signal: `python v1/data/proof/verify.py`
+>
+> See [docs/adr/ADR-012-esp32-csi-sensor-mesh.md](docs/adr/ADR-012-esp32-csi-sensor-mesh.md) for the ESP32 setup guide and [docs/adr/ADR-013-feature-level-sensing-commodity-gear.md](docs/adr/ADR-013-feature-level-sensing-commodity-gear.md) for the zero-cost RSSI path.
+
 [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
 [![FastAPI](https://img.shields.io/badge/FastAPI-0.95+-green.svg)](https://fastapi.tiangolo.com/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
@@ -52,7 +64,7 @@ A high-performance Rust port is available in `/rust-port/wifi-densepose-rs/`:
 | Memory Usage | ~500MB | ~100MB |
 | WASM Support | ❌ | ✅ |
 | Binary Size | N/A | ~10MB |
-| Test Coverage | 100% | 107 tests |
+| Test Coverage | 100% | 313 tests |
 
 **Quick Start (Rust):**
 ```bash
@@ -71,6 +83,19 @@ Mathematical correctness validated:
 - ✅ Correlation: 1.0 for identical signals
 - ✅ Phase coherence: 1.0 for coherent signals
 
+### SOTA Signal Processing (ADR-014)
+
+Six research-grade algorithms implemented in the `wifi-densepose-signal` crate:
+
+| Algorithm | Purpose | Reference |
+|-----------|---------|-----------|
+| **Conjugate Multiplication** | Cancels CFO/SFO from raw CSI phase via antenna ratio | SpotFi (SIGCOMM 2015) |
+| **Hampel Filter** | Robust outlier removal using median/MAD (resists 50% contamination) | Hampel (1974) |
+| **Fresnel Zone Model** | Physics-based breathing detection from chest displacement | FarSense (MobiCom 2019) |
+| **CSI Spectrogram** | STFT time-frequency matrices for CNN-based activity recognition | Standard since 2018 |
+| **Subcarrier Selection** | Variance-ratio ranking to pick top-K motion-sensitive subcarriers | WiDance (MobiCom 2017) |
+| **Body Velocity Profile** | Domain-independent velocity x time representation from Doppler | Widar 3.0 (MobiSys 2019) |
+
 See [Rust Port Documentation](/rust-port/wifi-densepose-rs/docs/) for ADRs and DDD patterns.
 
 ## 🚨 WiFi-Mat: Disaster Response Module
@@ -140,8 +165,10 @@ cargo test --package wifi-densepose-mat
 - [WiFi-Mat Disaster Response](#-wifi-mat-disaster-response-module)
 - [System Architecture](#️-system-architecture)
 - [Installation](#-installation)
-  - [Using pip (Recommended)](#using-pip-recommended)
-  - [From Source](#from-source)
+  - [Guided Installer (Recommended)](#guided-installer-recommended)
+  - [Install Profiles](#install-profiles)
+  - [From Source (Rust)](#from-source-rust--primary)
+  - [From Source (Python)](#from-source-python)
   - [Using Docker](#using-docker)
   - [System Requirements](#system-requirements)
 - [Quick Start](#-quick-start)
@@ -177,7 +204,7 @@ cargo test --package wifi-densepose-mat
 - [Testing](#-testing)
   - [Running Tests](#running-tests)
   - [Test Categories](#test-categories)
-  - [Mock Testing](#mock-testing)
+  - [Testing Without Hardware](#testing-without-hardware)
   - [Continuous Integration](#continuous-integration)
 - [Deployment](#-deployment)
   - [Production Deployment](#production-deployment)
@@ -254,24 +281,73 @@ WiFi DensePose consists of several key components working together:
 
 ## 📦 Installation
 
-### Using pip (Recommended)
+### Guided Installer (Recommended)
 
-WiFi-DensePose is now available on PyPI for easy installation:
+The interactive installer detects your hardware, checks your environment, and builds the right profile automatically:
 
 ```bash
-# Install the latest stable version
-pip install wifi-densepose
-
-# Install with specific version
-pip install wifi-densepose==1.0.0
-
-# Install with optional dependencies
-pip install wifi-densepose[gpu]  # For GPU acceleration
-pip install wifi-densepose[dev]  # For development
-pip install wifi-densepose[all]  # All optional dependencies
+./install.sh
 ```
 
-### From Source
+It walks through 7 steps:
+1. **System detection** — OS, RAM, disk, GPU
+2. **Toolchain detection** — Python, Rust, Docker, Node.js, ESP-IDF
+3. **WiFi hardware detection** — interfaces, ESP32 USB, Intel CSI debug
+4. **Profile recommendation** — picks the best profile for your hardware
+5. **Dependency installation** — installs what's missing
+6. **Build** — compiles the selected profile
+7. **Summary** — shows next steps and verification commands
+
+#### Install Profiles
+
+| Profile | What it installs | Size | Requirements |
+|---------|-----------------|------|-------------|
+| `verify` | Pipeline verification only | ~5 MB | Python 3.8+ |
+| `python` | Full Python API server + sensing | ~500 MB | Python 3.8+ |
+| `rust` | Rust pipeline (~810x faster) | ~200 MB | Rust 1.70+ |
+| `browser` | WASM for in-browser execution | ~10 MB | Rust + wasm-pack |
+| `iot` | ESP32 sensor mesh + aggregator | varies | Rust + ESP-IDF |
+| `docker` | Docker-based deployment | ~1 GB | Docker |
+| `field` | WiFi-Mat disaster response kit | ~62 MB | Rust + wasm-pack |
+| `full` | Everything available | ~2 GB | All toolchains |
+
+#### Non-Interactive Install
+
+```bash
+# Install a specific profile without prompts
+./install.sh --profile rust --yes
+
+# Just run hardware detection (no install)
+./install.sh --check-only
+
+# Or use make targets
+make install              # Interactive
+make install-verify       # Verification only
+make install-python       # Python pipeline
+make install-rust         # Rust pipeline
+make install-browser      # WASM browser build
+make install-docker       # Docker deployment
+make install-field        # Disaster response kit
+make install-full         # Everything
+make check                # Hardware check only
+```
+
+### From Source (Rust — Primary)
+
+```bash
+git clone https://github.com/ruvnet/wifi-densepose.git
+cd wifi-densepose
+
+# Install Rust pipeline (810x faster than Python)
+./install.sh --profile rust --yes
+
+# Or manually:
+cd rust-port/wifi-densepose-rs
+cargo build --release
+cargo test --workspace
+```
+
+### From Source (Python)
 
 ```bash
 git clone https://github.com/ruvnet/wifi-densepose.git
@@ -280,6 +356,16 @@ pip install -r requirements.txt
 pip install -e .
 ```
 
+### Using pip (Python only)
+
+```bash
+pip install wifi-densepose
+
+# With optional dependencies
+pip install wifi-densepose[gpu]  # For GPU acceleration
+pip install wifi-densepose[all]  # All optional dependencies
+```
+
 ### Using Docker
 
 ```bash
@@ -289,19 +375,23 @@ docker run -p 8000:8000 ruvnet/wifi-densepose:latest
 
 ### System Requirements
 
-- **Python**: 3.8 or higher
+- **Rust**: 1.70+ (primary runtime — install via [rustup](https://rustup.rs/))
+- **Python**: 3.8+ (for verification and legacy v1 API)
 - **Operating System**: Linux (Ubuntu 18.04+), macOS (10.15+), Windows 10+
 - **Memory**: Minimum 4GB RAM, Recommended 8GB+
 - **Storage**: 2GB free space for models and data
-- **Network**: WiFi interface with CSI capability
-- **GPU**: Optional but recommended (NVIDIA GPU with CUDA support)
+- **Network**: WiFi interface with CSI capability (optional — installer detects what you have)
+- **GPU**: Optional (NVIDIA CUDA or Apple Metal)
 
 ## 🚀 Quick Start
 
 ### 1. Basic Setup
 
 ```bash
-# Install the package
+# Install the package (Rust — recommended)
+./install.sh --profile rust --yes
+
+# Or Python legacy
 pip install wifi-densepose
 
 # Copy example configuration
@@ -879,17 +969,16 @@ pytest tests/performance/  # Performance tests
 - Memory usage profiling
 - Stress testing
 
-### Mock Testing
+### Testing Without Hardware
 
-For development without hardware:
+For development without WiFi CSI hardware, use the deterministic reference signal:
 
 ```bash
-# Enable mock mode
-export MOCK_HARDWARE=true
-export MOCK_POSE_DATA=true
+# Verify the full signal processing pipeline (no hardware needed)
+./verify
 
-# Run tests with mocked hardware
-pytest tests/ --mock-hardware
+# Run Rust tests (all use real signal processing, no mocks)
+cd rust-port/wifi-densepose-rs && cargo test --workspace
 ```
 
 ### Continuous Integration
@@ -1290,6 +1379,34 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 ```
 
+## Changelog
+
+### v2.2.0 — 2026-02-28
+
+- **Guided installer** — `./install.sh` with 7-step hardware detection, WiFi interface discovery, toolchain checks, and environment-specific RVF builds (verify/python/rust/browser/iot/docker/field/full profiles)
+- **Make targets** — `make install`, `make check`, `make install-rust`, `make build-wasm`, `make bench`, and 15+ other targets
+- **Real-only inference** — `forward()` and hardware adapters return explicit errors without weights/hardware instead of silent empty data
+- **5.7x Doppler FFT speedup** — Phase cache ring buffer reduces full pipeline from 719us to 254us per frame
+- **Trust kill switch** — `./verify` with SHA-256 proof replay, `--audit` mode, and production code integrity scan
+- **Security hardening** — 10 vulnerabilities fixed (hardcoded creds, JWT bypass, NaN panics), 12 dead code instances removed
+- **SOTA research** — Comprehensive WiFi sensing + RuVector analysis with 30+ citations and 20-year projection (docs/research/)
+- **6 SOTA signal algorithms (ADR-014)** — Conjugate multiplication (SpotFi), Hampel filter, Fresnel zone breathing model, CSI spectrogram, subcarrier sensitivity selection, Body Velocity Profile (Widar 3.0) — 83 new tests
+- **WiFi-Mat disaster response** — Ensemble classifier with START triage, scan zone management, API endpoints (ADR-001) — 139 tests
+- **ESP32 CSI hardware parser** — Real binary frame parsing with I/Q extraction, amplitude/phase conversion, stream resync (ADR-012) — 28 tests
+- **313 total Rust tests** — All passing, zero mocks
+
+### v2.1.0 — 2026-02-28
+
+- **RuVector RVF integration** — Architecture Decision Records (ADR-002 through ADR-013) defining integration of RVF cognitive containers, HNSW vector search, SONA self-learning, GNN pattern recognition, post-quantum cryptography, distributed consensus, WASM edge runtime, and witness chains
+- **ESP32 CSI sensor mesh** — Firmware specification for $54 starter kit with 3-6 ESP32-S3 nodes, feature-level fusion aggregator, and UDP streaming (ADR-012)
+- **Commodity WiFi sensing** — Zero-cost presence/motion detection via RSSI from any Linux WiFi adapter using `/proc/net/wireless` and `iw` (ADR-013)
+- **Deterministic proof bundle** — One-command pipeline verification (`./verify`) with SHA-256 hash matching against a published reference signal
+- **Real Doppler extraction** — Temporal phase-difference FFT across CSI history frames for true Doppler spectrum computation
+- **Three.js visualization** — 3D body model with 24 DensePose body parts, signal visualization, environment rendering, and WebSocket streaming
+- **Commodity sensing module** — `RssiFeatureExtractor` with FFT spectral analysis, CUSUM change detection, and `PresenceClassifier` with rule-based logic
+- **CI verification pipeline** — GitHub Actions workflow that verifies pipeline determinism and scans for unseeded random calls in production code
+- **Rust hardware adapters** — ESP32, Intel 5300, Atheros, UDP, and PCAP adapters now return explicit errors when no hardware is connected instead of silent empty data
+
 ## 🙏 Acknowledgments
 
 - **Research Foundation**: Based on groundbreaking research in WiFi-based human sensing

assets/README.txt

@@ -0,0 +1,135 @@
+WiFi-Mat v3.2 - AI Thermal Monitor + WiFi CSI Sensing
+======================================================
+
+Embedded AI system combining thermal monitoring with WiFi-based
+presence detection, inspired by WiFi-DensePose technology.
+
+For Heltec ESP32-S3 with OLED Display
+
+CORE CAPABILITIES:
+------------------
+* Thermal Pattern Learning - Spiking Neural Network (LIF neurons)
+* WiFi CSI Sensing - Through-wall motion/presence detection
+* Breathing Detection - Respiratory rate from WiFi phase
+* Anomaly Detection - Ruvector-inspired attention weights
+* HNSW Indexing - Fast O(log n) pattern matching
+* Power Optimization - Adaptive sleep modes
+
+VISUAL INDICATORS:
+------------------
+* Animated motion figure when movement detected
+* Radar sweep with detection blips
+* Breathing wave visualization with BPM
+* Status bar: WiFi/Motion/Alert icons
+* Screen flash on anomaly or motion alerts
+* Dynamic confidence bars
+
+DISPLAY MODES (cycle with double-tap):
+--------------------------------------
+1. STATS  - Temperature, zone, patterns, attention level
+2. GRAPH  - Temperature history graph (40 samples)
+3. PTRNS  - Learned pattern list with scores
+4. ANOM   - Anomaly detection with trajectory view
+5. AI     - Power optimization metrics
+6. CSI    - WiFi CSI motion sensing with radar
+7. RF     - RF device presence detection
+8. INFO   - Device info, uptime, memory
+
+AI POWER OPTIMIZATION (AI mode):
+--------------------------------
+* Mode: ACTIVE/LIGHT/DEEP sleep states
+* Energy: Estimated power savings (0-95%)
+* Neurons: Active vs idle neuron ratio
+* HNSW: Hierarchical search efficiency
+* Spikes: Neural spike efficiency
+* Attn: Pattern attention weights
+
+WIFI CSI SENSING (CSI mode):
+----------------------------
+Uses WiFi Channel State Information for through-wall sensing:
+
+* MOTION/STILL - Real-time motion detection
+* Radar Animation - Sweep with confidence blips
+* Breathing Wave - Sine wave + BPM when detected
+* Confidence % - Detection confidence level
+* Detection Count - Cumulative motion events
+* Variance Metrics - Signal variance analysis
+
+Technology based on WiFi-DensePose concepts:
+- Phase unwrapping for movement detection
+- Amplitude variance for presence sensing
+- Frequency analysis for breathing rate
+- No cameras needed - works through walls
+
+BUTTON CONTROLS:
+----------------
+* TAP (quick)     - Learn current thermal pattern
+* DOUBLE-TAP      - Cycle display mode
+* HOLD 1 second   - Pause/Resume monitoring
+* HOLD 2 seconds  - Reset all learned patterns
+* HOLD 3+ seconds - Show device info
+
+INSTALLATION:
+-------------
+1. Connect Heltec ESP32-S3 via USB
+2. Run flash.bat (Windows) or flash.ps1 (PowerShell)
+3. Enter COM port when prompted (e.g., COM7)
+4. Wait for flash to complete (~60 seconds)
+5. Device auto-connects to configured WiFi
+
+REQUIREMENTS:
+-------------
+* espflash tool: cargo install espflash
+* Heltec WiFi LoRa 32 V3 (ESP32-S3)
+* USB-C cable
+* Windows 10/11
+
+WIFI CONFIGURATION:
+-------------------
+Default network: ruv.net
+
+To change WiFi credentials, edit source and rebuild:
+  C:\esp\src\main.rs (lines 43-44)
+
+HARDWARE PINOUT:
+----------------
+* OLED SDA: GPIO17
+* OLED SCL: GPIO18
+* OLED RST: GPIO21
+* OLED PWR: GPIO36 (Vext)
+* Button: GPIO0 (PRG)
+* Thermal: MLX90614 on I2C
+
+TECHNICAL SPECS:
+----------------
+* MCU: ESP32-S3 dual-core 240MHz
+* Flash: 8MB
+* RAM: 512KB SRAM + 8MB PSRAM
+* Display: 128x64 OLED (SSD1306)
+* WiFi: 802.11 b/g/n (2.4GHz)
+* Bluetooth: BLE 5.0
+
+NEURAL NETWORK:
+---------------
+* Architecture: Leaky Integrate-and-Fire (LIF)
+* Neurons: 16 configurable
+* Patterns: Up to 32 learned
+* Features: 6 sparse dimensions
+* Indexing: 3-layer HNSW hierarchy
+
+SOURCE CODE:
+------------
+Full Rust source: C:\esp\src\main.rs
+WiFi CSI module: C:\esp\src\wifi_csi.rs
+Build script: C:\esp\build.ps1
+
+BASED ON:
+---------
+* Ruvector - Vector database with HNSW indexing
+* WiFi-DensePose - WiFi CSI for pose estimation
+* esp-rs - Rust on ESP32
+
+LICENSE:
+--------
+Created with Claude Code
+https://github.com/ruvnet/wifi-densepose

docs/adr/ADR-002-ruvector-rvf-integration-strategy.md

@@ -0,0 +1,217 @@
+# ADR-002: RuVector RVF Integration Strategy
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Current System Limitations
+
+The WiFi-DensePose system processes Channel State Information (CSI) from WiFi signals to estimate human body poses. The current architecture (Python v1 + Rust port) has several areas where intelligence and performance could be significantly improved:
+
+1. **No persistent vector storage**: CSI feature vectors are processed transiently. Historical patterns, fingerprints, and learned representations are not persisted in a searchable vector database.
+
+2. **Static inference models**: The modality translation network (`ModalityTranslationNetwork`) and DensePose head use fixed weights loaded at startup. There is no online learning, adaptation, or self-optimization.
+
+3. **Naive pattern matching**: Human detection in `CSIProcessor` uses simple threshold-based confidence scoring (`amplitude_indicator`, `phase_indicator`, `motion_indicator` with fixed weights 0.4, 0.3, 0.3). No similarity search against known patterns.
+
+4. **No cryptographic audit trail**: Life-critical disaster detection (wifi-densepose-mat) lacks tamper-evident logging for survivor detections and triage classifications.
+
+5. **Limited edge deployment**: The WASM crate (`wifi-densepose-wasm`) provides basic bindings but lacks a self-contained runtime capable of offline operation with embedded models.
+
+6. **Single-node architecture**: Multi-AP deployments for disaster scenarios require distributed coordination, but no consensus mechanism exists for cross-node state management.
+
+### RuVector Capabilities
+
+RuVector (github.com/ruvnet/ruvector) provides a comprehensive cognitive computing platform:
+
+- **RVF (Cognitive Containers)**: Self-contained files with 25 segment types (VEC, INDEX, KERNEL, EBPF, WASM, COW_MAP, WITNESS, CRYPTO) that package vectors, models, and runtime into a single deployable artifact
+- **HNSW Vector Search**: Hierarchical Navigable Small World indexing with SIMD acceleration and Hyperbolic extensions for hierarchy-aware search
+- **SONA**: Self-Optimizing Neural Architecture providing <1ms adaptation via LoRA fine-tuning with EWC++ memory preservation
+- **GNN Learning Layer**: Graph Neural Networks that learn from every query through message passing, attention weighting, and representation updates
+- **46 Attention Mechanisms**: Including Flash Attention, Linear Attention, Graph Attention, Hyperbolic Attention, Mincut-gated Attention
+- **Post-Quantum Cryptography**: ML-DSA-65, Ed25519, SLH-DSA-128s signatures with SHAKE-256 hashing
+- **Witness Chains**: Tamper-evident cryptographic hash-linked audit trails
+- **Raft Consensus**: Distributed coordination with multi-master replication and vector clocks
+- **WASM Runtime**: 5.5 KB runtime bootable in 125ms, deployable on servers, browsers, phones, IoT
+- **Git-like Branching**: Copy-on-write structure (1M vectors + 100 edits ≈ 2.5 MB branch)
+
+## Decision
+
+We will integrate RuVector's RVF format and intelligence capabilities into the WiFi-DensePose system through a phased, modular approach across 9 integration domains, each detailed in subsequent ADRs (ADR-003 through ADR-010).
+
+### Integration Architecture Overview
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                        WiFi-DensePose + RuVector                            │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
+│  │   CSI Input   │  │  RVF Store   │  │    SONA      │  │   GNN Layer  │   │
+│  │   Pipeline    │──▶│  (Vectors,  │──▶│  Self-Learn  │──▶│  Pattern     │   │
+│  │              │  │   Indices)   │  │              │  │  Enhancement │   │
+│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘   │
+│         │                 │                 │                 │            │
+│         ▼                 ▼                 ▼                 ▼            │
+│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐   │
+│  │  Feature     │  │   HNSW       │  │  Adaptive    │  │   Pose       │   │
+│  │  Extraction  │  │   Search     │  │  Weights     │  │  Estimation  │   │
+│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘   │
+│         │                 │                 │                 │            │
+│         └─────────────────┴─────────────────┴─────────────────┘            │
+│                                     │                                      │
+│                          ┌──────────▼──────────┐                          │
+│                          │    Output Layer      │                          │
+│                          │  • Pose Keypoints    │                          │
+│                          │  • Body Segments     │                          │
+│                          │  • UV Coordinates    │                          │
+│                          │  • Confidence Maps   │                          │
+│                          └──────────┬──────────┘                          │
+│                                     │                                      │
+│         ┌───────────────────────────┼───────────────────────────┐          │
+│         ▼                           ▼                           ▼          │
+│  ┌──────────────┐           ┌──────────────┐           ┌──────────────┐   │
+│  │  Witness     │           │    Raft       │           │   WASM       │   │
+│  │  Chains      │           │  Consensus    │           │   Edge       │   │
+│  │  (Audit)     │           │  (Multi-AP)   │           │  Runtime     │   │
+│  └──────────────┘           └──────────────┘           └──────────────┘   │
+│                                                                             │
+│  ┌─────────────────────────────────────────────────────────────────────┐   │
+│  │                  Post-Quantum Crypto Layer                          │   │
+│  │          ML-DSA-65 │ Ed25519 │ SLH-DSA-128s │ SHAKE-256           │   │
+│  └─────────────────────────────────────────────────────────────────────┘   │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+
+### New Crate: `wifi-densepose-rvf`
+
+A new workspace member crate will serve as the integration layer:
+
+```
+crates/wifi-densepose-rvf/
+├── Cargo.toml
+├── src/
+│   ├── lib.rs                 # Public API surface
+│   ├── container.rs           # RVF cognitive container management
+│   ├── vector_store.rs        # HNSW-backed CSI vector storage
+│   ├── search.rs              # Similarity search for fingerprinting
+│   ├── learning.rs            # SONA integration for online learning
+│   ├── gnn.rs                 # GNN pattern enhancement layer
+│   ├── attention.rs           # Attention mechanism selection
+│   ├── witness.rs             # Witness chain audit trails
+│   ├── consensus.rs           # Raft consensus for multi-AP
+│   ├── crypto.rs              # Post-quantum crypto wrappers
+│   ├── edge.rs                # WASM edge runtime integration
+│   └── adapters/
+│       ├── mod.rs
+│       ├── signal_adapter.rs  # Bridges wifi-densepose-signal
+│       ├── nn_adapter.rs      # Bridges wifi-densepose-nn
+│       └── mat_adapter.rs     # Bridges wifi-densepose-mat
+```
+
+### Phased Rollout
+
+| Phase | Timeline | ADR | Capability | Priority |
+|-------|----------|-----|------------|----------|
+| 1 | Weeks 1-3 | ADR-003 | RVF Cognitive Containers for CSI Data | Critical |
+| 2 | Weeks 2-4 | ADR-004 | HNSW Vector Search for Signal Fingerprinting | Critical |
+| 3 | Weeks 4-6 | ADR-005 | SONA Self-Learning for Pose Estimation | High |
+| 4 | Weeks 5-7 | ADR-006 | GNN-Enhanced CSI Pattern Recognition | High |
+| 5 | Weeks 6-8 | ADR-007 | Post-Quantum Cryptography for Secure Sensing | Medium |
+| 6 | Weeks 7-9 | ADR-008 | Distributed Consensus for Multi-AP | Medium |
+| 7 | Weeks 8-10 | ADR-009 | RVF WASM Runtime for Edge Deployment | Medium |
+| 8 | Weeks 9-11 | ADR-010 | Witness Chains for Audit Trail Integrity | High (MAT) |
+
+### Dependency Strategy
+
+**Verified published crates** (crates.io, all at v2.0.4 as of 2026-02-28):
+
+```toml
+# In Cargo.toml workspace dependencies
+[workspace.dependencies]
+ruvector-mincut = "2.0.4"           # Dynamic min-cut, O(n^1.5 log n) graph partitioning
+ruvector-attn-mincut = "2.0.4"     # Attention + mincut gating in one pass
+ruvector-temporal-tensor = "2.0.4"  # Tiered temporal compression (50-75% memory reduction)
+ruvector-solver = "2.0.4"           # NeumannSolver — O(√n) Neumann series convergence
+ruvector-attention = "2.0.4"        # ScaledDotProductAttention
+```
+
+> **Note (ADR-017 correction):** Earlier versions of this ADR specified
+> `ruvector-core`, `ruvector-data-framework`, `ruvector-consensus`, and
+> `ruvector-wasm` at version `"0.1"`. These crates do not exist at crates.io.
+> The five crates above are the verified published API surface at v2.0.4.
+> Capabilities such as RVF cognitive containers (ADR-003), HNSW search (ADR-004),
+> SONA (ADR-005), GNN patterns (ADR-006), post-quantum crypto (ADR-007),
+> Raft consensus (ADR-008), and WASM runtime (ADR-009) are internal capabilities
+> accessible through these five crates or remain as forward-looking architecture.
+> See ADR-017 for the corrected integration map.
+
+Feature flags control which ruvector capabilities are compiled in:
+
+```toml
+[features]
+default = ["mincut-matching", "solver-interpolation"]
+mincut-matching = ["ruvector-mincut"]
+attn-mincut = ["ruvector-attn-mincut"]
+temporal-compress = ["ruvector-temporal-tensor"]
+solver-interpolation = ["ruvector-solver"]
+attention = ["ruvector-attention"]
+full = ["mincut-matching", "attn-mincut", "temporal-compress", "solver-interpolation", "attention"]
+```
+
+## Consequences
+
+### Positive
+
+- **10-100x faster pattern lookup**: HNSW replaces linear scan for CSI fingerprint matching
+- **Continuous improvement**: SONA enables online adaptation without full retraining
+- **Self-contained deployment**: RVF containers package everything needed for field operation
+- **Tamper-evident records**: Witness chains provide cryptographic proof for disaster response auditing
+- **Future-proof security**: Post-quantum signatures resist quantum computing attacks
+- **Distributed operation**: Raft consensus enables coordinated multi-AP sensing
+- **Ultra-light edge**: 5.5 KB WASM runtime enables browser and IoT deployment
+- **Git-like versioning**: COW branching enables experimental model variations with minimal storage
+
+### Negative
+
+- **Increased binary size**: Full feature set adds significant dependencies (~15-30 MB)
+- **Complexity**: 9 integration domains require careful coordination
+- **Learning curve**: Team must understand RuVector's cognitive container paradigm
+- **API stability risk**: RuVector is pre-1.0; APIs may change
+- **Testing surface**: Each integration point requires dedicated test suites
+
+### Risks and Mitigations
+
+| Risk | Severity | Mitigation |
+|------|----------|------------|
+| RuVector API breaking changes | High | Pin versions, adapter pattern isolates impact |
+| Performance regression from abstraction layers | Medium | Benchmark each integration point, zero-cost abstractions |
+| Feature flag combinatorial complexity | Medium | CI matrix testing for key feature combinations |
+| Over-engineering for current use cases | Medium | Phased rollout, each phase independently valuable |
+| Binary size bloat for edge targets | Low | Feature flags ensure only needed capabilities compile |
+
+## Related ADRs
+
+- **ADR-001**: WiFi-Mat Disaster Detection Architecture (existing)
+- **ADR-003**: RVF Cognitive Containers for CSI Data
+- **ADR-004**: HNSW Vector Search for Signal Fingerprinting
+- **ADR-005**: SONA Self-Learning for Pose Estimation
+- **ADR-006**: GNN-Enhanced CSI Pattern Recognition
+- **ADR-007**: Post-Quantum Cryptography for Secure Sensing
+- **ADR-008**: Distributed Consensus for Multi-AP Coordination
+- **ADR-009**: RVF WASM Runtime for Edge Deployment
+- **ADR-010**: Witness Chains for Audit Trail Integrity
+
+## References
+
+- [RuVector Repository](https://github.com/ruvnet/ruvector)
+- [HNSW Algorithm](https://arxiv.org/abs/1603.09320)
+- [LoRA: Low-Rank Adaptation](https://arxiv.org/abs/2106.09685)
+- [Elastic Weight Consolidation](https://arxiv.org/abs/1612.00796)
+- [Raft Consensus](https://raft.github.io/raft.pdf)
+- [ML-DSA (FIPS 204)](https://csrc.nist.gov/pubs/fips/204/final)
+- [WiFi-DensePose Rust ADR-001: Workspace Structure](../rust-port/wifi-densepose-rs/docs/adr/ADR-001-workspace-structure.md)

docs/adr/ADR-003-rvf-cognitive-containers-csi.md

@@ -0,0 +1,251 @@
+# ADR-003: RVF Cognitive Containers for CSI Data
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Problem
+
+WiFi-DensePose processes CSI (Channel State Information) data through a multi-stage pipeline: raw capture → preprocessing → feature extraction → neural inference → pose output. Each stage produces intermediate data that is currently ephemeral:
+
+1. **Raw CSI measurements** (`CsiData`): Amplitude matrices (num_antennas x num_subcarriers), phase arrays, SNR values, metadata. Stored only in a bounded `VecDeque` (max 500 entries in Python, similar in Rust).
+
+2. **Extracted features** (`CsiFeatures`): Amplitude mean/variance, phase differences, correlation matrices, Doppler shifts, power spectral density. Discarded after single-pass inference.
+
+3. **Trained model weights**: Static ONNX/PyTorch files loaded from disk. No mechanism to persist adapted weights or experimental variations.
+
+4. **Detection results** (`HumanDetectionResult`): Confidence scores, motion scores, detection booleans. Logged but not indexed for pattern retrieval.
+
+5. **Environment fingerprints**: Each physical space has a unique CSI signature affected by room geometry, furniture, building materials. No persistent fingerprint database exists.
+
+### Opportunity
+
+RuVector's RVF (Cognitive Container) format provides a single-file packaging solution with 25 segment types that can encapsulate the entire WiFi-DensePose operational state:
+
+```
+RVF Cognitive Container Structure:
+┌─────────────────────────────────────────────┐
+│ HEADER    │ Magic, version, segment count   │
+├───────────┼─────────────────────────────────┤
+│ VEC       │ CSI feature vectors             │
+│ INDEX     │ HNSW index over vectors         │
+│ WASM      │ Inference runtime               │
+│ COW_MAP   │ Copy-on-write branch state      │
+│ WITNESS   │ Audit chain entries             │
+│ CRYPTO    │ Signature keys, attestations    │
+│ KERNEL    │ Bootable runtime (optional)     │
+│ EBPF      │ Hardware-accelerated filters    │
+│ ...       │ (25 total segment types)        │
+└─────────────────────────────────────────────┘
+```
+
+## Decision
+
+We will adopt the RVF Cognitive Container format as the primary persistence and deployment unit for WiFi-DensePose operational data, implementing the following container types:
+
+### 1. CSI Fingerprint Container (`.rvf.csi`)
+
+Packages environment-specific CSI signatures for location recognition:
+
+```rust
+/// CSI Fingerprint container storing environment signatures
+pub struct CsiFingerprintContainer {
+    /// Container metadata
+    metadata: ContainerMetadata,
+
+    /// VEC segment: Normalized CSI feature vectors
+    /// Each vector = [amplitude_mean(N) | amplitude_var(N) | phase_diff(N-1) | doppler(10) | psd(128)]
+    /// Typical dimensionality: 64 subcarriers → 64+64+63+10+128 = 329 dimensions
+    fingerprint_vectors: VecSegment,
+
+    /// INDEX segment: HNSW index for O(log n) nearest-neighbor lookup
+    hnsw_index: IndexSegment,
+
+    /// COW_MAP: Branches for different times-of-day, occupancy levels
+    branches: CowMapSegment,
+
+    /// Metadata per vector: room_id, timestamp, occupancy_count, furniture_hash
+    annotations: AnnotationSegment,
+}
+```
+
+**Vector encoding**: Each CSI snapshot is encoded as a fixed-dimension vector:
+```
+CSI Feature Vector (329-dim for 64 subcarriers):
+┌──────────────────┬──────────────────┬─────────────────┬──────────┬─────────┐
+│ amplitude_mean   │ amplitude_var    │ phase_diff      │ doppler  │ psd     │
+│ [f32; 64]        │ [f32; 64]        │ [f32; 63]       │ [f32; 10]│ [f32;128│
+└──────────────────┴──────────────────┴─────────────────┴──────────┴─────────┘
+```
+
+### 2. Model Container (`.rvf.model`)
+
+Packages neural network weights with versioning:
+
+```rust
+/// Model container with version tracking and A/B comparison
+pub struct ModelContainer {
+    /// Container metadata with model version history
+    metadata: ContainerMetadata,
+
+    /// Primary model weights (ONNX serialized)
+    primary_weights: BlobSegment,
+
+    /// SONA adaptation deltas (LoRA low-rank matrices)
+    adaptation_deltas: VecSegment,
+
+    /// COW branches for model experiments
+    /// e.g., "baseline", "adapted-office-env", "adapted-warehouse"
+    branches: CowMapSegment,
+
+    /// Performance metrics per branch
+    metrics: AnnotationSegment,
+
+    /// Witness chain: every weight update recorded
+    audit_trail: WitnessSegment,
+}
+```
+
+### 3. Session Container (`.rvf.session`)
+
+Captures a complete sensing session for replay and analysis:
+
+```rust
+/// Session container for recording and replaying sensing sessions
+pub struct SessionContainer {
+    /// Session metadata (start time, duration, hardware config)
+    metadata: ContainerMetadata,
+
+    /// Time-series CSI vectors at capture rate
+    csi_timeseries: VecSegment,
+
+    /// Detection results aligned to CSI timestamps
+    detections: AnnotationSegment,
+
+    /// Pose estimation outputs
+    poses: VecSegment,
+
+    /// Index for temporal range queries
+    temporal_index: IndexSegment,
+
+    /// Cryptographic integrity proof
+    witness_chain: WitnessSegment,
+}
+```
+
+### Container Lifecycle
+
+```
+  ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
+  │  Create   │───▶│  Ingest  │───▶│  Query   │───▶│  Branch  │
+  │ Container │    │ Vectors  │    │  (HNSW)  │    │  (COW)   │
+  └──────────┘    └──────────┘    └──────────┘    └──────────┘
+       │                                                │
+       │         ┌──────────┐    ┌──────────┐          │
+       │         │  Merge   │◀───│  Compare │◀─────────┘
+       │         │ Branches │    │ Results  │
+       │         └────┬─────┘    └──────────┘
+       │              │
+       ▼              ▼
+  ┌──────────┐   ┌──────────┐
+  │  Export   │   │  Deploy  │
+  │  (.rvf)  │   │  (Edge)  │
+  └──────────┘   └──────────┘
+```
+
+### Integration with Existing Crates
+
+The container system integrates through adapter traits:
+
+```rust
+/// Trait for types that can be vectorized into RVF containers
+pub trait RvfVectorizable {
+    /// Encode self as a fixed-dimension f32 vector
+    fn to_rvf_vector(&self) -> Vec<f32>;
+
+    /// Reconstruct from an RVF vector
+    fn from_rvf_vector(vec: &[f32]) -> Result<Self, RvfError> where Self: Sized;
+
+    /// Vector dimensionality
+    fn vector_dim() -> usize;
+}
+
+// Implementation for existing types
+impl RvfVectorizable for CsiFeatures {
+    fn to_rvf_vector(&self) -> Vec<f32> {
+        let mut vec = Vec::with_capacity(Self::vector_dim());
+        vec.extend(self.amplitude_mean.iter().map(|&x| x as f32));
+        vec.extend(self.amplitude_variance.iter().map(|&x| x as f32));
+        vec.extend(self.phase_difference.iter().map(|&x| x as f32));
+        vec.extend(self.doppler_shift.iter().map(|&x| x as f32));
+        vec.extend(self.power_spectral_density.iter().map(|&x| x as f32));
+        vec
+    }
+
+    fn vector_dim() -> usize {
+        // 64 + 64 + 63 + 10 + 128 = 329 (for 64 subcarriers)
+        329
+    }
+    // ...
+}
+```
+
+### Storage Characteristics
+
+| Container Type | Typical Size | Vector Count | Use Case |
+|----------------|-------------|-------------|----------|
+| Fingerprint | 5-50 MB | 10K-100K | Room/building fingerprint DB |
+| Model | 50-500 MB | N/A (blob) | Neural network deployment |
+| Session | 10-200 MB | 50K-500K | 1-hour recording at 100 Hz |
+
+### COW Branching for Environment Adaptation
+
+The copy-on-write mechanism enables zero-overhead experimentation:
+
+```
+main (office baseline: 50K vectors)
+  ├── branch/morning (delta: 500 vectors, ~15 KB)
+  ├── branch/afternoon (delta: 800 vectors, ~24 KB)
+  ├── branch/occupied-10 (delta: 2K vectors, ~60 KB)
+  └── branch/furniture-moved (delta: 5K vectors, ~150 KB)
+```
+
+Total overhead for 4 branches on a 50K-vector container: ~250 KB additional (0.5%).
+
+## Consequences
+
+### Positive
+- **Single-file deployment**: Move a fingerprint database between sites by copying one `.rvf` file
+- **Versioned models**: A/B test model variants without duplicating full weight sets
+- **Session replay**: Reproduce detection results from recorded CSI data
+- **Atomic operations**: Container writes are transactional; no partial state corruption
+- **Cross-platform**: Same container format works on server, WASM, and embedded
+- **Storage efficient**: COW branching avoids duplicating unchanged data
+
+### Negative
+- **Format lock-in**: RVF is not yet a widely-adopted standard
+- **Serialization overhead**: Converting between native types and RVF vectors adds latency (~0.1-0.5 ms per vector)
+- **Learning curve**: Team must understand segment types and container lifecycle
+- **File size for sessions**: High-rate CSI capture (1000 Hz) generates large session containers
+
+### Performance Targets
+
+| Operation | Target Latency | Notes |
+|-----------|---------------|-------|
+| Container open | <10 ms | Memory-mapped I/O |
+| Vector insert | <0.1 ms | Append to VEC segment |
+| HNSW query (100K vectors) | <1 ms | See ADR-004 |
+| Branch create | <1 ms | COW metadata only |
+| Branch merge | <100 ms | Delta application |
+| Container export | ~1 ms/MB | Sequential write |
+
+## References
+
+- [RuVector Cognitive Container Specification](https://github.com/ruvnet/ruvector)
+- [Memory-Mapped I/O in Rust](https://docs.rs/memmap2)
+- [Copy-on-Write Data Structures](https://en.wikipedia.org/wiki/Copy-on-write)
+- ADR-002: RuVector RVF Integration Strategy

docs/adr/ADR-004-hnsw-vector-search-fingerprinting.md

@@ -0,0 +1,270 @@
+# ADR-004: HNSW Vector Search for Signal Fingerprinting
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Current Signal Matching Limitations
+
+The WiFi-DensePose system needs to match incoming CSI patterns against known signatures for:
+
+1. **Environment recognition**: Identifying which room/area the device is in based on CSI characteristics
+2. **Activity classification**: Matching current CSI patterns to known human activities (walking, sitting, falling)
+3. **Anomaly detection**: Determining whether current readings deviate significantly from baseline
+4. **Survivor re-identification** (MAT module): Tracking individual survivors across scan sessions
+
+Current approach in `CSIProcessor._calculate_detection_confidence()`:
+```python
+# Fixed thresholds, no similarity search
+amplitude_indicator = np.mean(features.amplitude_mean) > 0.1
+phase_indicator = np.std(features.phase_difference) > 0.05
+motion_indicator = motion_score > 0.3
+confidence = (0.4 * amplitude_indicator + 0.3 * phase_indicator + 0.3 * motion_indicator)
+```
+
+This is a **O(1) fixed-threshold check** that:
+- Cannot learn from past observations
+- Has no concept of "similar patterns seen before"
+- Requires manual threshold tuning per environment
+- Produces binary indicators (above/below threshold) losing gradient information
+
+### What HNSW Provides
+
+Hierarchical Navigable Small World (HNSW) graphs enable approximate nearest-neighbor search in high-dimensional vector spaces with:
+
+- **O(log n) query time** vs O(n) brute-force
+- **High recall**: >95% recall at 10x speed of exact search
+- **Dynamic insertion**: New vectors added without full rebuild
+- **SIMD acceleration**: RuVector's implementation uses AVX2/NEON for distance calculations
+
+RuVector extends standard HNSW with:
+- **Hyperbolic HNSW**: Search in Poincaré ball space for hierarchy-aware results (e.g., "walking" is closer to "running" than to "sitting" in activity hierarchy)
+- **GNN enhancement**: Graph neural networks refine neighbor connections after queries
+- **Tiered compression**: 2-32x memory reduction through adaptive quantization
+
+## Decision
+
+We will integrate RuVector's HNSW implementation as the primary similarity search engine for all CSI pattern matching operations, replacing fixed-threshold detection with similarity-based retrieval.
+
+### Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                    HNSW Search Pipeline                          │
+├─────────────────────────────────────────────────────────────────┤
+│                                                                  │
+│  CSI Input     Feature           Vector         HNSW            │
+│  ────────▶    Extraction   ────▶ Encode   ────▶ Search          │
+│              (existing)        (new)          (new)             │
+│                                                  │              │
+│                                    ┌─────────────┤              │
+│                                    ▼             ▼              │
+│                              Top-K Results    Confidence        │
+│                              [vec_id, dist,   Score from        │
+│                               metadata]       Distance Dist.   │
+│                                    │                            │
+│                                    ▼                            │
+│                              ┌────────────┐                     │
+│                              │ Decision   │                     │
+│                              │ Fusion     │                     │
+│                              └────────────┘                     │
+│                               Combines HNSW similarity with     │
+│                               existing threshold-based logic    │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### Index Configuration
+
+```rust
+/// HNSW configuration tuned for CSI vector characteristics
+pub struct CsiHnswConfig {
+    /// Vector dimensionality (matches CsiFeatures encoding)
+    dim: usize,  // 329 for 64 subcarriers
+
+    /// Maximum number of connections per node per layer
+    /// Higher M = better recall, more memory
+    /// CSI vectors are moderately dimensional; M=16 balances well
+    m: usize,  // 16
+
+    /// Size of dynamic candidate list during construction
+    /// ef_construction = 200 gives >99% recall for 329-dim vectors
+    ef_construction: usize,  // 200
+
+    /// Size of dynamic candidate list during search
+    /// ef_search = 64 gives >95% recall with <1ms latency at 100K vectors
+    ef_search: usize,  // 64
+
+    /// Distance metric
+    /// Cosine similarity works best for normalized CSI features
+    metric: DistanceMetric,  // Cosine
+
+    /// Maximum elements (pre-allocated for performance)
+    max_elements: usize,  // 1_000_000
+
+    /// Enable SIMD acceleration
+    simd: bool,  // true
+
+    /// Quantization level for memory reduction
+    quantization: Quantization,  // PQ8 (product quantization, 8-bit)
+}
+```
+
+### Multiple Index Strategy
+
+Different use cases require different index configurations:
+
+| Index Name | Vectors | Dim | Distance | Use Case |
+|-----------|---------|-----|----------|----------|
+| `env_fingerprint` | 10K-1M | 329 | Cosine | Environment/room identification |
+| `activity_pattern` | 1K-50K | 329 | Euclidean | Activity classification |
+| `temporal_pattern` | 10K-500K | 329 | Cosine | Temporal anomaly detection |
+| `survivor_track` | 100-10K | 329 | Cosine | MAT survivor re-identification |
+
+### Similarity-Based Detection Enhancement
+
+Replace fixed thresholds with distance-based confidence:
+
+```rust
+/// Enhanced detection using HNSW similarity search
+pub struct SimilarityDetector {
+    /// HNSW index of known human-present CSI patterns
+    human_patterns: HnswIndex,
+
+    /// HNSW index of known empty-room CSI patterns
+    empty_patterns: HnswIndex,
+
+    /// Fusion weight between similarity and threshold methods
+    fusion_alpha: f64,  // 0.7 = 70% similarity, 30% threshold
+}
+
+impl SimilarityDetector {
+    /// Detect human presence using similarity search + threshold fusion
+    pub fn detect(&self, features: &CsiFeatures) -> DetectionResult {
+        let query_vec = features.to_rvf_vector();
+
+        // Search both indices
+        let human_neighbors = self.human_patterns.search(&query_vec, k=5);
+        let empty_neighbors = self.empty_patterns.search(&query_vec, k=5);
+
+        // Distance-based confidence
+        let avg_human_dist = human_neighbors.mean_distance();
+        let avg_empty_dist = empty_neighbors.mean_distance();
+
+        // Similarity confidence: how much closer to human patterns vs empty
+        let similarity_confidence = avg_empty_dist / (avg_human_dist + avg_empty_dist);
+
+        // Fuse with traditional threshold-based confidence
+        let threshold_confidence = self.traditional_threshold_detect(features);
+        let fused_confidence = self.fusion_alpha * similarity_confidence
+                             + (1.0 - self.fusion_alpha) * threshold_confidence;
+
+        DetectionResult {
+            human_detected: fused_confidence > 0.5,
+            confidence: fused_confidence,
+            similarity_confidence,
+            threshold_confidence,
+            nearest_human_pattern: human_neighbors[0].metadata.clone(),
+            nearest_empty_pattern: empty_neighbors[0].metadata.clone(),
+        }
+    }
+}
+```
+
+### Incremental Learning Loop
+
+Every confirmed detection enriches the index:
+
+```
+1. CSI captured → features extracted → vector encoded
+2. HNSW search returns top-K neighbors + distances
+3. Detection decision made (similarity + threshold fusion)
+4. If confirmed (by temporal consistency or ground truth):
+   a. Insert vector into appropriate index (human/empty)
+   b. GNN layer updates neighbor relationships (ADR-006)
+   c. SONA adapts fusion weights (ADR-005)
+5. Periodically: prune stale vectors, rebuild index layers
+```
+
+### Performance Analysis
+
+**Memory requirements** (PQ8 quantization):
+
+| Vector Count | Raw Size | PQ8 Compressed | HNSW Overhead | Total |
+|-------------|----------|----------------|---------------|-------|
+| 10,000 | 12.9 MB | 1.6 MB | 2.5 MB | 4.1 MB |
+| 100,000 | 129 MB | 16 MB | 25 MB | 41 MB |
+| 1,000,000 | 1.29 GB | 160 MB | 250 MB | 410 MB |
+
+**Latency expectations** (329-dim vectors, ef_search=64):
+
+| Vector Count | Brute Force | HNSW | Speedup |
+|-------------|-------------|------|---------|
+| 10,000 | 3.2 ms | 0.08 ms | 40x |
+| 100,000 | 32 ms | 0.3 ms | 107x |
+| 1,000,000 | 320 ms | 0.9 ms | 356x |
+
+### Hyperbolic Extension for Activity Hierarchy
+
+WiFi-sensed activities have natural hierarchy:
+
+```
+                    motion
+                   /      \
+              locomotion   stationary
+              /    \         /    \
+         walking  running  sitting  lying
+         /    \
+      normal  shuffling
+```
+
+Hyperbolic HNSW in Poincaré ball space preserves this hierarchy during search, so a query for "shuffling" returns "walking" before "sitting" even if Euclidean distances are similar.
+
+```rust
+/// Hyperbolic HNSW for hierarchy-aware activity matching
+pub struct HyperbolicActivityIndex {
+    index: HnswIndex,
+    curvature: f64,  // -1.0 for unit Poincaré ball
+}
+
+impl HyperbolicActivityIndex {
+    pub fn search(&self, query: &[f32], k: usize) -> Vec<SearchResult> {
+        // Uses Poincaré distance: d(u,v) = arcosh(1 + 2||u-v||²/((1-||u||²)(1-||v||²)))
+        self.index.search_hyperbolic(query, k, self.curvature)
+    }
+}
+```
+
+## Consequences
+
+### Positive
+- **Adaptive detection**: System improves with more data; no manual threshold tuning
+- **Sub-millisecond search**: HNSW provides <1ms queries even at 1M vectors
+- **Memory efficient**: PQ8 reduces storage 8x with <5% recall loss
+- **Hierarchy-aware**: Hyperbolic mode respects activity relationships
+- **Incremental**: New patterns added without full index rebuild
+- **Explainable**: "This detection matched pattern X from room Y at time Z"
+
+### Negative
+- **Cold-start problem**: Need initial fingerprint data before similarity search is useful
+- **Index maintenance**: Periodic pruning and layer rebalancing needed
+- **Approximation**: HNSW is approximate; may miss exact nearest neighbor (mitigated by high ef_search)
+- **Memory for indices**: HNSW graph structure adds 2.5x overhead on top of vectors
+
+### Migration Strategy
+
+1. **Phase 1**: Run HNSW search in parallel with existing threshold detection, log both results
+2. **Phase 2**: A/B test fusion weights (alpha parameter) on labeled data
+3. **Phase 3**: Gradually increase fusion_alpha from 0.0 (pure threshold) to 0.7 (primarily similarity)
+4. **Phase 4**: Threshold detection becomes fallback for cold-start/empty-index scenarios
+
+## References
+
+- [HNSW: Efficient and Robust Approximate Nearest Neighbor](https://arxiv.org/abs/1603.09320)
+- [Product Quantization for Nearest Neighbor Search](https://hal.inria.fr/inria-00514462)
+- [Poincaré Embeddings for Learning Hierarchical Representations](https://arxiv.org/abs/1705.08039)
+- [RuVector HNSW Implementation](https://github.com/ruvnet/ruvector)
+- ADR-003: RVF Cognitive Containers for CSI Data

docs/adr/ADR-005-sona-self-learning-pose-estimation.md

@@ -0,0 +1,253 @@
+# ADR-005: SONA Self-Learning for Pose Estimation
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Static Model Problem
+
+The WiFi-DensePose modality translation network (`ModalityTranslationNetwork` in Python, `ModalityTranslator` in Rust) converts CSI features into visual-like feature maps that feed the DensePose head for body segmentation and UV coordinate estimation. These models are trained offline and deployed with frozen weights.
+
+**Critical limitations of static models**:
+
+1. **Environment drift**: CSI characteristics change when furniture moves, new objects are introduced, or building occupancy changes. A model trained in Lab A degrades in Lab B without retraining.
+
+2. **Hardware variance**: Different WiFi chipsets (Intel AX200 vs Broadcom BCM4375 vs Qualcomm WCN6855) produce subtly different CSI patterns. Static models overfit to training hardware.
+
+3. **Temporal drift**: Even in the same environment, CSI patterns shift with temperature, humidity, and electromagnetic interference changes throughout the day.
+
+4. **Population bias**: Models trained on one demographic may underperform on body types, heights, or movement patterns not represented in training data.
+
+Current mitigation: manual retraining with new data, which requires:
+- Collecting labeled data in the new environment
+- GPU-intensive training (hours to days)
+- Model export/deployment cycle
+- Downtime during switchover
+
+### SONA Opportunity
+
+RuVector's Self-Optimizing Neural Architecture (SONA) provides <1ms online adaptation through:
+
+- **LoRA (Low-Rank Adaptation)**: Instead of updating all weights (millions of parameters), LoRA injects small trainable rank decomposition matrices into frozen model layers. For a weight matrix W ∈ R^(d×k), LoRA learns A ∈ R^(d×r) and B ∈ R^(r×k) where r << min(d,k), so the adapted weight is W + AB.
+
+- **EWC++ (Elastic Weight Consolidation)**: Prevents catastrophic forgetting by penalizing changes to parameters important for previously learned tasks. Each parameter has a Fisher information-weighted importance score.
+
+- **Online gradient accumulation**: Small batches of live data (as few as 1-10 samples) contribute to adaptation without full backward passes.
+
+## Decision
+
+We will integrate SONA as the online learning engine for both the modality translation network and the DensePose head, enabling continuous environment-specific adaptation without offline retraining.
+
+### Adaptation Architecture
+
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│                    SONA Adaptation Pipeline                          │
+├──────────────────────────────────────────────────────────────────────┤
+│                                                                      │
+│  Frozen Base Model                    LoRA Adaptation Matrices       │
+│  ┌─────────────────┐                  ┌──────────────────────┐      │
+│  │ Conv2d(64,128)  │ ◀── W_frozen ──▶ │ A(64,r) × B(r,128) │      │
+│  │ Conv2d(128,256) │ ◀── W_frozen ──▶ │ A(128,r) × B(r,256)│      │
+│  │ Conv2d(256,512) │ ◀── W_frozen ──▶ │ A(256,r) × B(r,512)│      │
+│  │ ConvT(512,256)  │ ◀── W_frozen ──▶ │ A(512,r) × B(r,256)│      │
+│  │ ...             │                  │ ...                  │      │
+│  └─────────────────┘                  └──────────────────────┘      │
+│         │                                      │                     │
+│         ▼                                      ▼                     │
+│  ┌─────────────────────────────────────────────────────────┐        │
+│  │            Effective Weight = W_frozen + α(AB)           │        │
+│  │            α = scaling factor (0.0 → 1.0 over time)     │        │
+│  └─────────────────────────────────────────────────────────┘        │
+│                              │                                       │
+│                              ▼                                       │
+│  ┌─────────────────────────────────────────────────────────┐        │
+│  │                    EWC++ Regularizer                      │        │
+│  │  L_total = L_task + λ Σ F_i (θ_i - θ*_i)²              │        │
+│  │                                                          │        │
+│  │  F_i = Fisher information (parameter importance)         │        │
+│  │  θ*_i = optimal parameters from previous tasks           │        │
+│  │  λ = regularization strength (10-100)                    │        │
+│  └─────────────────────────────────────────────────────────┘        │
+└──────────────────────────────────────────────────────────────────────┘
+```
+
+### LoRA Configuration per Layer
+
+```rust
+/// SONA LoRA configuration for WiFi-DensePose
+pub struct SonaConfig {
+    /// LoRA rank (r): dimensionality of adaptation matrices
+    /// r=4 for encoder layers (less variation needed)
+    /// r=8 for decoder layers (more expression needed)
+    /// r=16 for final output layers (maximum adaptability)
+    lora_ranks: HashMap<String, usize>,
+
+    /// Scaling factor alpha: controls adaptation strength
+    /// Starts at 0.0 (pure frozen model), increases to target
+    alpha: f64,  // Target: 0.3
+
+    /// Alpha warmup steps before reaching target
+    alpha_warmup_steps: usize,  // 100
+
+    /// EWC++ regularization strength
+    ewc_lambda: f64,  // 50.0
+
+    /// Fisher information estimation samples
+    fisher_samples: usize,  // 200
+
+    /// Online learning rate (much smaller than offline training)
+    online_lr: f64,  // 1e-5
+
+    /// Gradient accumulation steps before applying update
+    accumulation_steps: usize,  // 10
+
+    /// Maximum adaptation delta (safety bound)
+    max_delta_norm: f64,  // 0.1
+}
+```
+
+**Parameter budget**:
+
+| Layer | Original Params | LoRA Rank | LoRA Params | Overhead |
+|-------|----------------|-----------|-------------|----------|
+| Encoder Conv1 (64→128) | 73,728 | 4 | 768 | 1.0% |
+| Encoder Conv2 (128→256) | 294,912 | 4 | 1,536 | 0.5% |
+| Encoder Conv3 (256→512) | 1,179,648 | 4 | 3,072 | 0.3% |
+| Decoder ConvT1 (512→256) | 1,179,648 | 8 | 6,144 | 0.5% |
+| Decoder ConvT2 (256→128) | 294,912 | 8 | 3,072 | 1.0% |
+| Output Conv (128→24) | 27,648 | 16 | 2,432 | 8.8% |
+| **Total** | **3,050,496** | - | **17,024** | **0.56%** |
+
+SONA adapts **0.56% of parameters** while achieving 70-90% of the accuracy improvement of full fine-tuning.
+
+### Adaptation Trigger Conditions
+
+```rust
+/// When to trigger SONA adaptation
+pub enum AdaptationTrigger {
+    /// Detection confidence drops below threshold over N samples
+    ConfidenceDrop {
+        threshold: f64,     // 0.6
+        window_size: usize, // 50
+    },
+
+    /// CSI statistics drift beyond baseline (KL divergence)
+    DistributionDrift {
+        kl_threshold: f64,  // 0.5
+        reference_window: usize, // 1000
+    },
+
+    /// New environment detected (no close HNSW matches)
+    NewEnvironment {
+        min_distance: f64,  // 0.8 (far from all known fingerprints)
+    },
+
+    /// Periodic adaptation (maintenance)
+    Periodic {
+        interval_samples: usize, // 10000
+    },
+
+    /// Manual trigger via API
+    Manual,
+}
+```
+
+### Adaptation Feedback Sources
+
+Since WiFi-DensePose lacks camera ground truth in deployment, adaptation uses **self-supervised signals**:
+
+1. **Temporal consistency**: Pose estimates should change smoothly between frames. Jerky transitions indicate prediction error.
+   ```
+   L_temporal = ||pose(t) - pose(t-1)||² when Δt < 100ms
+   ```
+
+2. **Physical plausibility**: Body part positions must satisfy skeletal constraints (limb lengths, joint angles).
+   ```
+   L_skeleton = Σ max(0, |limb_length - expected_length| - tolerance)
+   ```
+
+3. **Multi-view agreement** (multi-AP): Different APs observing the same person should produce consistent poses.
+   ```
+   L_multiview = ||pose_AP1 - transform(pose_AP2)||²
+   ```
+
+4. **Detection stability**: Confidence should be high when the environment is stable.
+   ```
+   L_stability = -log(confidence) when variance(CSI_window) < threshold
+   ```
+
+### Safety Mechanisms
+
+```rust
+/// Safety bounds prevent adaptation from degrading the model
+pub struct AdaptationSafety {
+    /// Maximum parameter change per update step
+    max_step_norm: f64,
+
+    /// Rollback if validation loss increases by this factor
+    rollback_threshold: f64,  // 1.5 (50% worse = rollback)
+
+    /// Keep N checkpoints for rollback
+    checkpoint_count: usize,  // 5
+
+    /// Disable adaptation after N consecutive rollbacks
+    max_consecutive_rollbacks: usize,  // 3
+
+    /// Minimum samples between adaptations
+    cooldown_samples: usize,  // 100
+}
+```
+
+### Persistence via RVF
+
+Adaptation state is stored in the Model Container (ADR-003):
+- LoRA matrices A and B serialized to VEC segment
+- Fisher information matrix serialized alongside
+- Each adaptation creates a witness chain entry (ADR-010)
+- COW branching allows reverting to any previous adaptation state
+
+```
+model.rvf.model
+  ├── main (frozen base weights)
+  ├── branch/adapted-office-2024-01 (LoRA deltas)
+  ├── branch/adapted-warehouse (LoRA deltas)
+  └── branch/adapted-outdoor-disaster (LoRA deltas)
+```
+
+## Consequences
+
+### Positive
+- **Zero-downtime adaptation**: Model improves continuously during operation
+- **Tiny overhead**: 17K parameters (0.56%) vs 3M full model; <1ms per adaptation step
+- **No forgetting**: EWC++ preserves performance on previously-seen environments
+- **Portable adaptations**: LoRA deltas are ~70 KB, easily shared between devices
+- **Safe rollback**: Checkpoint system prevents runaway degradation
+- **Self-supervised**: No labeled data needed during deployment
+
+### Negative
+- **Bounded expressiveness**: LoRA rank limits the degree of adaptation; extreme environment changes may require offline retraining
+- **Feedback noise**: Self-supervised signals are weaker than ground-truth labels; adaptation is slower and less precise
+- **Compute on device**: Even small gradient computations require tensor math on the inference device
+- **Complexity**: Debugging adapted models is harder than static models
+- **Hyperparameter sensitivity**: EWC lambda, LoRA rank, learning rate require tuning
+
+### Validation Plan
+
+1. **Offline validation**: Train base model on Environment A, test SONA adaptation to Environment B with known ground truth. Measure pose estimation MPJPE (Mean Per-Joint Position Error) improvement.
+2. **A/B deployment**: Run static model and SONA-adapted model in parallel on same CSI stream. Compare detection rates and pose consistency.
+3. **Stress test**: Rapidly change environments (simulated) and verify EWC++ prevents catastrophic forgetting.
+4. **Edge latency**: Benchmark adaptation step on target hardware (Raspberry Pi 4, Jetson Nano, browser WASM).
+
+## References
+
+- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
+- [Elastic Weight Consolidation (EWC)](https://arxiv.org/abs/1612.00796)
+- [Continual Learning with SONA](https://github.com/ruvnet/ruvector)
+- [Self-Supervised WiFi Sensing](https://arxiv.org/abs/2203.11928)
+- ADR-002: RuVector RVF Integration Strategy
+- ADR-003: RVF Cognitive Containers for CSI Data

docs/adr/ADR-006-gnn-enhanced-csi-pattern-recognition.md

@@ -0,0 +1,261 @@
+# ADR-006: GNN-Enhanced CSI Pattern Recognition
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Limitations of Independent Vector Search
+
+ADR-004 introduces HNSW-based similarity search for CSI pattern matching. While HNSW provides fast nearest-neighbor retrieval, it treats each vector independently. CSI patterns, however, have rich relational structure:
+
+1. **Temporal adjacency**: CSI frames captured 10ms apart are more related than frames 10s apart. Sequential patterns reveal motion trajectories.
+
+2. **Spatial correlation**: CSI readings from adjacent subcarriers are highly correlated due to frequency proximity. Antenna pairs capture different spatial perspectives.
+
+3. **Cross-session similarity**: The "walking to kitchen" pattern from Tuesday should inform Wednesday's recognition, but the environment baseline may have shifted.
+
+4. **Multi-person entanglement**: When multiple people are present, CSI patterns are superpositions. Disentangling requires understanding which pattern fragments co-occur.
+
+Standard HNSW cannot capture these relationships. Each query returns neighbors based solely on vector distance, ignoring the graph structure of how patterns relate to each other.
+
+### RuVector's GNN Enhancement
+
+RuVector implements a Graph Neural Network layer that sits on top of the HNSW index:
+
+```
+Standard HNSW: Query → Distance-based neighbors → Results
+GNN-Enhanced:  Query → Distance-based neighbors → GNN refinement → Improved results
+```
+
+The GNN performs three operations in <1ms:
+1. **Message passing**: Each node aggregates information from its HNSW neighbors
+2. **Attention weighting**: Multi-head attention identifies which neighbors are most relevant for the current query context
+3. **Representation update**: Node embeddings are refined based on neighborhood context
+
+Additionally, **temporal learning** tracks query sequences to discover:
+- Vectors that frequently appear together in sessions
+- Temporal ordering patterns (A usually precedes B)
+- Session context that changes relevance rankings
+
+## Decision
+
+We will integrate RuVector's GNN layer to enhance CSI pattern recognition with three core capabilities: relational search, temporal sequence modeling, and multi-person disentanglement.
+
+### GNN Architecture for CSI
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│                 GNN-Enhanced CSI Pattern Graph                       │
+├─────────────────────────────────────────────────────────────────────┤
+│                                                                      │
+│  Layer 1: HNSW Spatial Graph                                        │
+│  ┌───────────────────────────────────────────────────────┐          │
+│  │  Nodes = CSI feature vectors                          │          │
+│  │  Edges = HNSW neighbor connections (distance-based)   │          │
+│  │  Node features = [amplitude | phase | doppler | PSD]  │          │
+│  └───────────────────────────────────────────────────────┘          │
+│                          │                                           │
+│                          ▼                                           │
+│  Layer 2: Temporal Edges                                            │
+│  ┌───────────────────────────────────────────────────────┐          │
+│  │  Additional edges between temporally adjacent vectors  │          │
+│  │  Edge weight = 1/Δt (closer in time = stronger)       │          │
+│  │  Direction = causal (past → future)                    │          │
+│  └───────────────────────────────────────────────────────┘          │
+│                          │                                           │
+│                          ▼                                           │
+│  Layer 3: GNN Message Passing (2 rounds)                            │
+│  ┌───────────────────────────────────────────────────────┐          │
+│  │  Round 1: h_i = σ(W₁·h_i + Σⱼ α_ij · W₂·h_j)       │          │
+│  │  Round 2: h_i = σ(W₃·h_i + Σⱼ α'_ij · W₄·h_j)      │          │
+│  │  α_ij = softmax(LeakyReLU(a^T[W·h_i || W·h_j]))     │          │
+│  │  (Graph Attention Network mechanism)                   │          │
+│  └───────────────────────────────────────────────────────┘          │
+│                          │                                           │
+│                          ▼                                           │
+│  Layer 4: Refined Representations                                   │
+│  ┌───────────────────────────────────────────────────────┐          │
+│  │  Updated vectors incorporate neighborhood context      │          │
+│  │  Re-rank search results using refined distances       │          │
+│  └───────────────────────────────────────────────────────┘          │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+### Three Integration Modes
+
+#### Mode 1: Query-Time Refinement (Default)
+
+GNN refines HNSW results after retrieval. No modifications to stored vectors.
+
+```rust
+pub struct GnnQueryRefiner {
+    /// GNN weights (small: ~50K parameters)
+    gnn_weights: GnnModel,
+
+    /// Number of message passing rounds
+    num_rounds: usize,  // 2
+
+    /// Attention heads for neighbor weighting
+    num_heads: usize,  // 4
+
+    /// How many HNSW neighbors to consider in GNN
+    neighborhood_size: usize,  // 20 (retrieve 20, GNN selects best 5)
+}
+
+impl GnnQueryRefiner {
+    /// Refine HNSW results using graph context
+    pub fn refine(&self, query: &[f32], hnsw_results: &[SearchResult]) -> Vec<SearchResult> {
+        // Build local subgraph from query + HNSW results
+        let subgraph = self.build_local_subgraph(query, hnsw_results);
+
+        // Run message passing
+        let refined = self.message_pass(&subgraph, self.num_rounds);
+
+        // Re-rank based on refined representations
+        self.rerank(query, &refined)
+    }
+}
+```
+
+**Latency**: +0.2ms on top of HNSW search (total <1.5ms for 100K vectors).
+
+#### Mode 2: Temporal Sequence Recognition
+
+Tracks CSI vector sequences to recognize activity patterns that span multiple frames:
+
+```rust
+/// Temporal pattern recognizer using GNN edges
+pub struct TemporalPatternRecognizer {
+    /// Sliding window of recent query vectors
+    window: VecDeque<TimestampedVector>,
+
+    /// Maximum window size (in frames)
+    max_window: usize,  // 100 (10 seconds at 10 Hz)
+
+    /// Temporal edge decay factor
+    decay: f64,  // 0.95 (edges weaken with time)
+
+    /// Known activity sequences (learned from data)
+    activity_templates: HashMap<String, Vec<Vec<f32>>>,
+}
+
+impl TemporalPatternRecognizer {
+    /// Feed new CSI vector and check for activity pattern matches
+    pub fn observe(&mut self, vector: &[f32], timestamp: f64) -> Vec<ActivityMatch> {
+        self.window.push_back(TimestampedVector { vector: vector.to_vec(), timestamp });
+
+        // Build temporal subgraph from window
+        let temporal_graph = self.build_temporal_graph();
+
+        // GNN aggregates temporal context
+        let sequence_embedding = self.gnn_aggregate(&temporal_graph);
+
+        // Match against known activity templates
+        self.match_activities(&sequence_embedding)
+    }
+}
+```
+
+**Activity patterns detectable**:
+
+| Activity | Frames Needed | CSI Signature |
+|----------|--------------|---------------|
+| Walking | 10-30 | Periodic Doppler oscillation |
+| Falling | 5-15 | Sharp amplitude spike → stillness |
+| Sitting down | 10-20 | Gradual descent in reflection height |
+| Breathing (still) | 30-100 | Micro-periodic phase variation |
+| Gesture (wave) | 5-15 | Localized high-frequency amplitude variation |
+
+#### Mode 3: Multi-Person Disentanglement
+
+When N>1 people are present, CSI is a superposition. The GNN learns to cluster pattern fragments:
+
+```rust
+/// Multi-person CSI disentanglement using GNN clustering
+pub struct MultiPersonDisentangler {
+    /// Maximum expected simultaneous persons
+    max_persons: usize,  // 10
+
+    /// GNN-based spectral clustering
+    cluster_gnn: GnnModel,
+
+    /// Per-person tracking state
+    person_tracks: Vec<PersonTrack>,
+}
+
+impl MultiPersonDisentangler {
+    /// Separate CSI features into per-person components
+    pub fn disentangle(&mut self, features: &CsiFeatures) -> Vec<PersonFeatures> {
+        // Decompose CSI into subcarrier groups using GNN attention
+        let subcarrier_graph = self.build_subcarrier_graph(features);
+
+        // GNN clusters subcarriers by person contribution
+        let clusters = self.cluster_gnn.cluster(&subcarrier_graph, self.max_persons);
+
+        // Extract per-person features from clustered subcarriers
+        clusters.iter().map(|c| self.extract_person_features(features, c)).collect()
+    }
+}
+```
+
+### GNN Learning Loop
+
+The GNN improves with every query through RuVector's built-in learning:
+
+```
+Query → HNSW retrieval → GNN refinement → User action (click/confirm/reject)
+                                              │
+                                              ▼
+                                    Update GNN weights via:
+                                    1. Positive: confirmed results get higher attention
+                                    2. Negative: rejected results get lower attention
+                                    3. Temporal: successful sequences reinforce edges
+```
+
+For WiFi-DensePose, "user action" is replaced by:
+- **Temporal consistency**: If frame N+1 confirms frame N's detection, reinforce
+- **Multi-AP agreement**: If two APs agree on detection, reinforce both
+- **Physical plausibility**: If pose satisfies skeletal constraints, reinforce
+
+### Performance Budget
+
+| Component | Parameters | Memory | Latency (per query) |
+|-----------|-----------|--------|-------------------|
+| GNN weights (2 layers, 4 heads) | 52K | 208 KB | 0.15 ms |
+| Temporal graph (100-frame window) | N/A | ~130 KB | 0.05 ms |
+| Multi-person clustering | 18K | 72 KB | 0.3 ms |
+| **Total GNN overhead** | **70K** | **410 KB** | **0.5 ms** |
+
+## Consequences
+
+### Positive
+- **Context-aware search**: Results account for temporal and spatial relationships, not just vector distance
+- **Activity recognition**: Temporal GNN enables sequence-level pattern matching
+- **Multi-person support**: GNN clustering separates overlapping CSI patterns
+- **Self-improving**: Every query provides learning signal to refine attention weights
+- **Lightweight**: 70K parameters, 410 KB memory, 0.5ms latency overhead
+
+### Negative
+- **Training data needed**: GNN weights require initial training on CSI pattern graphs
+- **Complexity**: Three modes increase testing and debugging surface
+- **Graph maintenance**: Temporal edges must be pruned to prevent unbounded growth
+- **Approximation**: GNN clustering for multi-person is approximate; may merge/split incorrectly
+
+### Interaction with Other ADRs
+- **ADR-004** (HNSW): GNN operates on HNSW graph structure; depends on HNSW being available
+- **ADR-005** (SONA): GNN weights can be adapted via SONA LoRA for environment-specific tuning
+- **ADR-003** (RVF): GNN weights stored in model container alongside inference weights
+- **ADR-010** (Witness): GNN weight updates recorded in witness chain
+
+## References
+
+- [Graph Attention Networks (GAT)](https://arxiv.org/abs/1710.10903)
+- [Temporal Graph Networks](https://arxiv.org/abs/2006.10637)
+- [Spectral Clustering with Graph Neural Networks](https://arxiv.org/abs/1907.00481)
+- [WiFi-based Multi-Person Sensing](https://dl.acm.org/doi/10.1145/3534592)
+- [RuVector GNN Implementation](https://github.com/ruvnet/ruvector)
+- ADR-004: HNSW Vector Search for Signal Fingerprinting

docs/adr/ADR-007-post-quantum-cryptography-secure-sensing.md

@@ -0,0 +1,215 @@
+# ADR-007: Post-Quantum Cryptography for Secure Sensing
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Threat Model
+
+WiFi-DensePose processes data that can reveal:
+- **Human presence/absence** in private spaces (surveillance risk)
+- **Health indicators** via breathing/heartbeat detection (medical privacy)
+- **Movement patterns** (behavioral profiling)
+- **Building occupancy** (physical security intelligence)
+
+In disaster scenarios (wifi-densepose-mat), the stakes are even higher:
+- **Triage classifications** affect rescue priority (life-or-death decisions)
+- **Survivor locations** are operationally sensitive
+- **Detection audit trails** may be used in legal proceedings (liability)
+- **False negatives** (missed survivors) could be forensically investigated
+
+Current security: The system uses standard JWT (HS256) for API authentication and has no cryptographic protection on data at rest, model integrity, or detection audit trails.
+
+### Quantum Threat Timeline
+
+NIST estimates cryptographically relevant quantum computers could emerge by 2030-2035. Data captured today with classical encryption may be decrypted retroactively ("harvest now, decrypt later"). For a system that may be deployed for decades in infrastructure, post-quantum readiness is prudent.
+
+### RuVector's Crypto Stack
+
+RuVector provides a layered cryptographic system:
+
+| Algorithm | Purpose | Standard | Quantum Resistant |
+|-----------|---------|----------|-------------------|
+| ML-DSA-65 | Digital signatures | FIPS 204 | Yes (lattice-based) |
+| Ed25519 | Digital signatures | RFC 8032 | No (classical fallback) |
+| SLH-DSA-128s | Digital signatures | FIPS 205 | Yes (hash-based) |
+| SHAKE-256 | Hashing | FIPS 202 | Yes |
+| AES-256-GCM | Symmetric encryption | FIPS 197 | Yes (Grover's halves, still 128-bit) |
+
+## Decision
+
+We will integrate RuVector's cryptographic layer to provide defense-in-depth for WiFi-DensePose data, using a **hybrid classical+PQ** approach where both Ed25519 and ML-DSA-65 signatures are applied (belt-and-suspenders until PQ algorithms mature).
+
+### Cryptographic Scope
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│              Cryptographic Protection Layers                      │
+├──────────────────────────────────────────────────────────────────┤
+│                                                                   │
+│  1. MODEL INTEGRITY                                              │
+│     ┌─────────────────────────────────────────────────────┐      │
+│     │ Model weights signed with ML-DSA-65 + Ed25519       │      │
+│     │ Signature verified at load time → reject tampered   │      │
+│     │ SONA adaptations co-signed with device key          │      │
+│     └─────────────────────────────────────────────────────┘      │
+│                                                                   │
+│  2. DATA AT REST (RVF containers)                                │
+│     ┌─────────────────────────────────────────────────────┐      │
+│     │ CSI vectors encrypted with AES-256-GCM              │      │
+│     │ Container integrity via SHAKE-256 Merkle tree       │      │
+│     │ Key management: per-container keys, sealed to device │      │
+│     └─────────────────────────────────────────────────────┘      │
+│                                                                   │
+│  3. DATA IN TRANSIT                                              │
+│     ┌─────────────────────────────────────────────────────┐      │
+│     │ API: TLS 1.3 with PQ key exchange (ML-KEM-768)      │      │
+│     │ WebSocket: Same TLS channel                         │      │
+│     │ Multi-AP sync: mTLS with device certificates        │      │
+│     └─────────────────────────────────────────────────────┘      │
+│                                                                   │
+│  4. AUDIT TRAIL (witness chains - see ADR-010)                   │
+│     ┌─────────────────────────────────────────────────────┐      │
+│     │ Every detection event hash-chained with SHAKE-256   │      │
+│     │ Chain anchors signed with ML-DSA-65                 │      │
+│     │ Cross-device attestation via SLH-DSA-128s           │      │
+│     └─────────────────────────────────────────────────────┘      │
+│                                                                   │
+│  5. DEVICE IDENTITY                                              │
+│     ┌─────────────────────────────────────────────────────┐      │
+│     │ Each sensing device has a key pair (ML-DSA-65)      │      │
+│     │ Device attestation proves hardware integrity        │      │
+│     │ Key rotation schedule: 90 days (or on compromise)   │      │
+│     └─────────────────────────────────────────────────────┘      │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+### Hybrid Signature Scheme
+
+```rust
+/// Hybrid signature combining classical Ed25519 with PQ ML-DSA-65
+pub struct HybridSignature {
+    /// Classical Ed25519 signature (64 bytes)
+    ed25519_sig: [u8; 64],
+
+    /// Post-quantum ML-DSA-65 signature (3309 bytes)
+    ml_dsa_sig: Vec<u8>,
+
+    /// Signer's public key fingerprint (SHAKE-256, 32 bytes)
+    signer_fingerprint: [u8; 32],
+
+    /// Timestamp of signing
+    timestamp: u64,
+}
+
+impl HybridSignature {
+    /// Verify requires BOTH signatures to be valid
+    pub fn verify(&self, message: &[u8], ed25519_pk: &Ed25519PublicKey,
+                  ml_dsa_pk: &MlDsaPublicKey) -> Result<bool, CryptoError> {
+        let ed25519_valid = ed25519_pk.verify(message, &self.ed25519_sig)?;
+        let ml_dsa_valid = ml_dsa_pk.verify(message, &self.ml_dsa_sig)?;
+
+        // Both must pass (defense in depth)
+        Ok(ed25519_valid && ml_dsa_valid)
+    }
+}
+```
+
+### Model Integrity Verification
+
+```rust
+/// Verify model weights have not been tampered with
+pub fn verify_model_integrity(model_container: &ModelContainer) -> Result<(), SecurityError> {
+    // 1. Extract embedded signature from container
+    let signature = model_container.crypto_segment().signature()?;
+
+    // 2. Compute SHAKE-256 hash of weight data
+    let weight_hash = shake256(model_container.weights_segment().data());
+
+    // 3. Verify hybrid signature
+    let publisher_keys = load_publisher_keys()?;
+    if !signature.verify(&weight_hash, &publisher_keys.ed25519, &publisher_keys.ml_dsa)? {
+        return Err(SecurityError::ModelTampered {
+            expected_signer: publisher_keys.fingerprint(),
+            container_path: model_container.path().to_owned(),
+        });
+    }
+
+    Ok(())
+}
+```
+
+### CSI Data Encryption
+
+For privacy-sensitive deployments, CSI vectors can be encrypted at rest:
+
+```rust
+/// Encrypt CSI vectors for storage in RVF container
+pub struct CsiEncryptor {
+    /// AES-256-GCM key (derived from device key + container salt)
+    key: Aes256GcmKey,
+}
+
+impl CsiEncryptor {
+    /// Encrypt a CSI feature vector
+    /// Note: HNSW search operates on encrypted vectors using
+    /// distance-preserving encryption (approximate, configurable trade-off)
+    pub fn encrypt_vector(&self, vector: &[f32]) -> EncryptedVector {
+        let nonce = generate_nonce();
+        let plaintext = bytemuck::cast_slice::<f32, u8>(vector);
+        let ciphertext = aes_256_gcm_encrypt(&self.key, &nonce, plaintext);
+        EncryptedVector { ciphertext, nonce }
+    }
+}
+```
+
+### Performance Impact
+
+| Operation | Without Crypto | With Crypto | Overhead |
+|-----------|---------------|-------------|----------|
+| Model load | 50 ms | 52 ms | +2 ms (signature verify) |
+| Vector insert | 0.1 ms | 0.15 ms | +0.05 ms (encrypt) |
+| HNSW search | 0.3 ms | 0.35 ms | +0.05 ms (decrypt top-K) |
+| Container open | 10 ms | 12 ms | +2 ms (integrity check) |
+| Detection event logging | 0.01 ms | 0.5 ms | +0.49 ms (hash chain) |
+
+### Feature Flags
+
+```toml
+[features]
+default = []
+crypto-classical = ["ed25519-dalek"]  # Ed25519 only
+crypto-pq = ["pqcrypto-dilithium", "pqcrypto-sphincsplus"]  # ML-DSA + SLH-DSA
+crypto-hybrid = ["crypto-classical", "crypto-pq"]  # Both (recommended)
+crypto-encrypt = ["aes-gcm"]  # Data-at-rest encryption
+crypto-full = ["crypto-hybrid", "crypto-encrypt"]
+```
+
+## Consequences
+
+### Positive
+- **Future-proof**: Lattice-based signatures resist quantum attacks
+- **Tamper detection**: Model poisoning and data manipulation are detectable
+- **Privacy compliance**: Encrypted CSI data meets GDPR/HIPAA requirements
+- **Forensic integrity**: Signed audit trails are admissible as evidence
+- **Low overhead**: <1ms per operation for most crypto operations
+
+### Negative
+- **Signature size**: ML-DSA-65 signatures are 3.3 KB vs 64 bytes for Ed25519
+- **Key management complexity**: Device key provisioning, rotation, revocation
+- **HNSW on encrypted data**: Distance-preserving encryption is approximate; search recall may degrade
+- **Dependency weight**: PQ crypto libraries add ~2 MB to binary
+- **Standards maturity**: FIPS 204/205 are finalized but implementations are evolving
+
+## References
+
+- [FIPS 204: ML-DSA (Module-Lattice Digital Signature)](https://csrc.nist.gov/pubs/fips/204/final)
+- [FIPS 205: SLH-DSA (Stateless Hash-Based Digital Signature)](https://csrc.nist.gov/pubs/fips/205/final)
+- [FIPS 202: SHA-3 / SHAKE](https://csrc.nist.gov/pubs/fips/202/final)
+- [RuVector Crypto Implementation](https://github.com/ruvnet/ruvector)
+- ADR-002: RuVector RVF Integration Strategy
+- ADR-010: Witness Chains for Audit Trail Integrity

docs/adr/ADR-008-distributed-consensus-multi-ap.md

@@ -0,0 +1,284 @@
+# ADR-008: Distributed Consensus for Multi-AP Coordination
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Multi-AP Sensing Architecture
+
+WiFi-DensePose achieves higher accuracy and coverage with multiple access points (APs) observing the same space from different angles. The disaster detection module (wifi-densepose-mat, ADR-001) explicitly requires distributed deployment:
+
+- **Portable**: Single TX/RX units deployed around a collapse site
+- **Distributed**: Multiple APs covering a large disaster zone
+- **Drone-mounted**: UAVs scanning from above with coordinated flight paths
+
+Each AP independently captures CSI data, extracts features, and runs local inference. But the distributed system needs coordination:
+
+1. **Consistent survivor registry**: All nodes must agree on the set of detected survivors, their locations, and triage classifications. Conflicting records cause rescue teams to waste time.
+
+2. **Coordinated scanning**: Avoid redundant scans of the same zone. Dynamically reassign APs as zones are cleared.
+
+3. **Model synchronization**: When SONA adapts a model on one node (ADR-005), other nodes should benefit from the adaptation without re-learning.
+
+4. **Clock synchronization**: CSI timestamps must be aligned across nodes for multi-view pose fusion (the GNN multi-person disentanglement in ADR-006 requires temporal alignment).
+
+5. **Partition tolerance**: In disaster scenarios, network connectivity is unreliable. The system must function during partitions and reconcile when connectivity restores.
+
+### Current State
+
+No distributed coordination exists. Each node operates independently. The Rust workspace has no consensus crate.
+
+### RuVector's Distributed Capabilities
+
+RuVector provides:
+- **Raft consensus**: Leader election and replicated log for strong consistency
+- **Vector clocks**: Logical timestamps for causal ordering without synchronized clocks
+- **Multi-master replication**: Concurrent writes with conflict resolution
+- **Delta consensus**: Tracks behavioral changes across nodes for anomaly detection
+- **Auto-sharding**: Distributes data based on access patterns
+
+## Decision
+
+We will integrate RuVector's Raft consensus implementation as the coordination backbone for multi-AP WiFi-DensePose deployments, with vector clocks for causal ordering and CRDT-based conflict resolution for partition-tolerant operation.
+
+### Consensus Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│              Multi-AP Coordination Architecture                      │
+├─────────────────────────────────────────────────────────────────────┤
+│                                                                      │
+│  Normal Operation (Connected):                                      │
+│                                                                      │
+│  ┌─────────┐     Raft      ┌─────────┐     Raft      ┌─────────┐  │
+│  │  AP-1   │◀────────────▶│  AP-2   │◀────────────▶│  AP-3   │  │
+│  │ (Leader)│    Replicated  │(Follower│   Replicated  │(Follower│  │
+│  │         │       Log      │        )│      Log      │        )│  │
+│  └────┬────┘               └────┬────┘               └────┬────┘  │
+│       │                         │                         │        │
+│       ▼                         ▼                         ▼        │
+│  ┌─────────┐              ┌─────────┐              ┌─────────┐    │
+│  │ Local   │              │ Local   │              │ Local   │    │
+│  │ RVF     │              │ RVF     │              │ RVF     │    │
+│  │Container│              │Container│              │Container│    │
+│  └─────────┘              └─────────┘              └─────────┘    │
+│                                                                      │
+│  Partitioned Operation (Disconnected):                              │
+│                                                                      │
+│  ┌─────────┐                              ┌──────────────────────┐  │
+│  │  AP-1   │  ← operates independently →  │  AP-2    AP-3       │  │
+│  │         │                              │  (form sub-cluster)  │  │
+│  │ Local   │                              │  Raft between 2+3    │  │
+│  │ writes  │                              │                      │  │
+│  └─────────┘                              └──────────────────────┘  │
+│       │                                            │                 │
+│       └──────── Reconnect: CRDT merge ─────────────┘                │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+### Replicated State Machine
+
+The Raft log replicates these operations across all nodes:
+
+```rust
+/// Operations replicated via Raft consensus
+#[derive(Serialize, Deserialize, Clone)]
+pub enum ConsensusOp {
+    /// New survivor detected
+    SurvivorDetected {
+        survivor_id: Uuid,
+        location: GeoCoord,
+        triage: TriageLevel,
+        detecting_ap: ApId,
+        confidence: f64,
+        timestamp: VectorClock,
+    },
+
+    /// Survivor status updated (e.g., triage reclassification)
+    SurvivorUpdated {
+        survivor_id: Uuid,
+        new_triage: TriageLevel,
+        updating_ap: ApId,
+        evidence: DetectionEvidence,
+    },
+
+    /// Zone assignment changed
+    ZoneAssignment {
+        zone_id: ZoneId,
+        assigned_aps: Vec<ApId>,
+        priority: ScanPriority,
+    },
+
+    /// Model adaptation delta shared
+    ModelDelta {
+        source_ap: ApId,
+        lora_delta: Vec<u8>,  // Serialized LoRA matrices
+        environment_hash: [u8; 32],
+        performance_metrics: AdaptationMetrics,
+    },
+
+    /// AP joined or left the cluster
+    MembershipChange {
+        ap_id: ApId,
+        action: MembershipAction,  // Join | Leave | Suspect
+    },
+}
+```
+
+### Vector Clocks for Causal Ordering
+
+Since APs may have unsynchronized physical clocks, vector clocks provide causal ordering:
+
+```rust
+/// Vector clock for causal ordering across APs
+#[derive(Clone, Serialize, Deserialize)]
+pub struct VectorClock {
+    /// Map from AP ID to logical timestamp
+    clocks: HashMap<ApId, u64>,
+}
+
+impl VectorClock {
+    /// Increment this AP's clock
+    pub fn tick(&mut self, ap_id: &ApId) {
+        *self.clocks.entry(ap_id.clone()).or_insert(0) += 1;
+    }
+
+    /// Merge with another clock (take max of each component)
+    pub fn merge(&mut self, other: &VectorClock) {
+        for (ap_id, &ts) in &other.clocks {
+            let entry = self.clocks.entry(ap_id.clone()).or_insert(0);
+            *entry = (*entry).max(ts);
+        }
+    }
+
+    /// Check if self happened-before other
+    pub fn happened_before(&self, other: &VectorClock) -> bool {
+        self.clocks.iter().all(|(k, &v)| {
+            other.clocks.get(k).map_or(false, |&ov| v <= ov)
+        }) && self.clocks != other.clocks
+    }
+}
+```
+
+### CRDT-Based Conflict Resolution
+
+During network partitions, concurrent updates may conflict. We use CRDTs (Conflict-free Replicated Data Types) for automatic resolution:
+
+```rust
+/// Survivor registry using Last-Writer-Wins Register CRDT
+pub struct SurvivorRegistry {
+    /// LWW-Element-Set: each survivor has a timestamp-tagged state
+    survivors: HashMap<Uuid, LwwRegister<SurvivorState>>,
+}
+
+/// Triage uses Max-wins semantics:
+/// If partition A says P1 (Red/Immediate) and partition B says P2 (Yellow/Delayed),
+/// after merge the survivor is classified P1 (more urgent wins)
+/// Rationale: false negative (missing critical) is worse than false positive
+impl CrdtMerge for TriageLevel {
+    fn merge(a: Self, b: Self) -> Self {
+        // Lower numeric priority = more urgent
+        if a.urgency() >= b.urgency() { a } else { b }
+    }
+}
+```
+
+**CRDT merge strategies by data type**:
+
+| Data Type | CRDT Type | Merge Strategy | Rationale |
+|-----------|-----------|---------------|-----------|
+| Survivor set | OR-Set | Union (never lose a detection) | Missing survivors = fatal |
+| Triage level | Max-Register | Most urgent wins | Err toward caution |
+| Location | LWW-Register | Latest timestamp wins | Survivors may move |
+| Zone assignment | LWW-Map | Leader's assignment wins | Need authoritative coord |
+| Model deltas | G-Set | Accumulate all deltas | All adaptations valuable |
+
+### Node Discovery and Health
+
+```rust
+/// AP cluster management
+pub struct ApCluster {
+    /// This node's identity
+    local_ap: ApId,
+
+    /// Raft consensus engine
+    raft: RaftEngine<ConsensusOp>,
+
+    /// Failure detector (phi-accrual)
+    failure_detector: PhiAccrualDetector,
+
+    /// Cluster membership
+    members: HashSet<ApId>,
+}
+
+impl ApCluster {
+    /// Heartbeat interval for failure detection
+    const HEARTBEAT_MS: u64 = 500;
+
+    /// Phi threshold for suspecting node failure
+    const PHI_THRESHOLD: f64 = 8.0;
+
+    /// Minimum cluster size for Raft (need majority)
+    const MIN_CLUSTER_SIZE: usize = 3;
+}
+```
+
+### Performance Characteristics
+
+| Operation | Latency | Notes |
+|-----------|---------|-------|
+| Raft heartbeat | 500 ms interval | Configurable |
+| Log replication | 1-5 ms (LAN) | Depends on payload size |
+| Leader election | 1-3 seconds | After leader failure detected |
+| CRDT merge (partition heal) | 10-100 ms | Proportional to divergence |
+| Vector clock comparison | <0.01 ms | O(n) where n = cluster size |
+| Model delta replication | 50-200 ms | ~70 KB LoRA delta |
+
+### Deployment Configurations
+
+| Scenario | Nodes | Consensus | Partition Strategy |
+|----------|-------|-----------|-------------------|
+| Single room | 1-2 | None (local only) | N/A |
+| Building floor | 3-5 | Raft (3-node quorum) | CRDT merge on heal |
+| Disaster site | 5-20 | Raft (5-node quorum) + zones | Zone-level sub-clusters |
+| Urban search | 20-100 | Hierarchical Raft | Regional leaders |
+
+## Consequences
+
+### Positive
+- **Consistent state**: All APs agree on survivor registry via Raft
+- **Partition tolerant**: CRDT merge allows operation during disconnection
+- **Causal ordering**: Vector clocks provide logical time without NTP
+- **Automatic failover**: Raft leader election handles AP failures
+- **Model sharing**: SONA adaptations propagate across cluster
+
+### Negative
+- **Minimum 3 nodes**: Raft requires odd-numbered quorum for leader election
+- **Network overhead**: Heartbeats and log replication consume bandwidth (~1-10 KB/s per node)
+- **Complexity**: Distributed systems are inherently harder to debug
+- **Latency for writes**: Raft requires majority acknowledgment before commit (1-5ms LAN)
+- **Split-brain risk**: If cluster splits evenly (2+2), neither partition has quorum
+
+### Disaster-Specific Considerations
+
+| Challenge | Mitigation |
+|-----------|------------|
+| Intermittent connectivity | Aggressive CRDT merge on reconnect; local operation during partition |
+| Power failures | Raft log persisted to local SSD; recovery on restart |
+| Node destruction | Raft tolerates minority failure; data replicated across survivors |
+| Drone mobility | Drone APs treated as ephemeral members; data synced on landing |
+| Bandwidth constraints | Delta-only replication; compress LoRA deltas |
+
+## References
+
+- [Raft Consensus Algorithm](https://raft.github.io/raft.pdf)
+- [CRDTs: Conflict-free Replicated Data Types](https://hal.inria.fr/inria-00609399)
+- [Vector Clocks](https://en.wikipedia.org/wiki/Vector_clock)
+- [Phi Accrual Failure Detector](https://www.computer.org/csdl/proceedings-article/srds/2004/22390066/12OmNyQYtlC)
+- [RuVector Distributed Consensus](https://github.com/ruvnet/ruvector)
+- ADR-001: WiFi-Mat Disaster Detection Architecture
+- ADR-002: RuVector RVF Integration Strategy

docs/adr/ADR-009-rvf-wasm-runtime-edge-deployment.md

@@ -0,0 +1,262 @@
+# ADR-009: RVF WASM Runtime for Edge Deployment
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Current WASM State
+
+The wifi-densepose-wasm crate provides basic WebAssembly bindings that expose Rust types to JavaScript. It enables browser-based visualization and lightweight inference but has significant limitations:
+
+1. **No self-contained operation**: WASM module depends on external model files loaded via fetch(). If the server is unreachable, the module is useless.
+
+2. **No persistent state**: Browser WASM has no built-in persistent storage for fingerprint databases, model weights, or session data.
+
+3. **No offline capability**: Without network access, the WASM module cannot load models or send results.
+
+4. **Binary size**: Current WASM bundle is not optimized. Full inference + signal processing compiles to ~5-15 MB.
+
+### Edge Deployment Requirements
+
+| Scenario | Platform | Constraints |
+|----------|----------|------------|
+| Browser dashboard | Chrome/Firefox | <10 MB download, no plugins |
+| IoT sensor node | ESP32/Raspberry Pi | 256 KB - 4 GB RAM, battery powered |
+| Mobile app | iOS/Android WebView | Limited background execution |
+| Drone payload | Embedded Linux + WASM | Weight/power limited, intermittent connectivity |
+| Field tablet | Android tablet | Offline operation in disaster zones |
+
+### RuVector's Edge Runtime
+
+RuVector provides a 5.5 KB WASM runtime that boots in 125ms, with:
+- Self-contained operation (models + data embedded in RVF container)
+- Persistent storage via RVF container (written to IndexedDB in browser, filesystem on native)
+- Offline-first architecture
+- SIMD acceleration when available (WASM SIMD proposal)
+
+## Decision
+
+We will replace the current wifi-densepose-wasm approach with an RVF-based edge runtime that packages models, fingerprint databases, and the inference engine into a single deployable RVF container.
+
+### Edge Runtime Architecture
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│                RVF Edge Deployment Container                      │
+│                    (.rvf.edge file)                                │
+├──────────────────────────────────────────────────────────────────┤
+│                                                                   │
+│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────┐    │
+│  │  WASM    │ │  VEC     │ │  INDEX   │ │  MODEL (ONNX)    │    │
+│  │  Runtime │ │  CSI     │ │  HNSW    │ │  + LoRA deltas   │    │
+│  │  (5.5KB) │ │  Finger- │ │  Graph   │ │                  │    │
+│  │          │ │  prints  │ │          │ │                  │    │
+│  └──────────┘ └──────────┘ └──────────┘ └──────────────────┘    │
+│                                                                   │
+│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────────┐    │
+│  │  CRYPTO  │ │  WITNESS │ │  COW_MAP │ │  CONFIG          │    │
+│  │  Keys    │ │  Audit   │ │  Branches│ │  Runtime params  │    │
+│  │          │ │  Chain   │ │          │ │                  │    │
+│  └──────────┘ └──────────┘ └──────────┘ └──────────────────┘    │
+│                                                                   │
+│  Total container: 1-50 MB depending on model + fingerprint size  │
+└──────────────────────────────────────────────────────────────────┘
+        │
+        │ Deploy to:
+        ▼
+┌───────────────────────────────────────────────────────────────┐
+│                                                                │
+│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────────────┐ │
+│  │ Browser │  │   IoT   │  │ Mobile  │  │ Disaster Field  │ │
+│  │         │  │ Device  │  │  App    │  │    Tablet       │ │
+│  │ IndexedDB  │ Flash   │  │ App     │  │ Local FS        │ │
+│  │ for state│  │ for     │  │ Sandbox │  │ for state       │ │
+│  │         │  │ state   │  │ for     │  │                 │ │
+│  │         │  │         │  │ state   │  │                 │ │
+│  └─────────┘  └─────────┘  └─────────┘  └─────────────────┘ │
+└───────────────────────────────────────────────────────────────┘
+```
+
+### Tiered Runtime Profiles
+
+Different deployment targets get different container configurations:
+
+```rust
+/// Edge runtime profiles
+pub enum EdgeProfile {
+    /// Full-featured browser deployment
+    /// ~10 MB container, full inference + HNSW + SONA
+    Browser {
+        model_quantization: Quantization::Int8,
+        max_fingerprints: 100_000,
+        enable_sona: true,
+        storage_backend: StorageBackend::IndexedDB,
+    },
+
+    /// Minimal IoT deployment
+    /// ~1 MB container, lightweight inference only
+    IoT {
+        model_quantization: Quantization::Int4,
+        max_fingerprints: 1_000,
+        enable_sona: false,
+        storage_backend: StorageBackend::Flash,
+    },
+
+    /// Mobile app deployment
+    /// ~5 MB container, inference + HNSW, limited SONA
+    Mobile {
+        model_quantization: Quantization::Int8,
+        max_fingerprints: 50_000,
+        enable_sona: true,
+        storage_backend: StorageBackend::AppSandbox,
+    },
+
+    /// Disaster field deployment (maximum capability)
+    /// ~50 MB container, full stack including multi-AP consensus
+    Field {
+        model_quantization: Quantization::Float16,
+        max_fingerprints: 1_000_000,
+        enable_sona: true,
+        storage_backend: StorageBackend::FileSystem,
+    },
+}
+```
+
+### Container Size Budget
+
+| Segment | Browser | IoT | Mobile | Field |
+|---------|---------|-----|--------|-------|
+| WASM runtime | 5.5 KB | 5.5 KB | 5.5 KB | 5.5 KB |
+| Model (ONNX) | 3 MB (int8) | 0.5 MB (int4) | 3 MB (int8) | 12 MB (fp16) |
+| HNSW index | 4 MB | 100 KB | 2 MB | 40 MB |
+| Fingerprint vectors | 2 MB | 50 KB | 1 MB | 10 MB |
+| Config + crypto | 50 KB | 10 KB | 50 KB | 100 KB |
+| **Total** | **~10 MB** | **~0.7 MB** | **~6 MB** | **~62 MB** |
+
+### Offline-First Data Flow
+
+```
+┌────────────────────────────────────────────────────────────────────┐
+│                    Offline-First Operation                          │
+├────────────────────────────────────────────────────────────────────┤
+│                                                                     │
+│  1. BOOT (125ms)                                                   │
+│     ├── Open RVF container from local storage                      │
+│     ├── Memory-map WASM runtime segment                            │
+│     ├── Load HNSW index into memory                                │
+│     └── Initialize inference engine with embedded model            │
+│                                                                     │
+│  2. OPERATE (continuous)                                           │
+│     ├── Receive CSI data from local hardware interface             │
+│     ├── Process through local pipeline (no network needed)         │
+│     ├── Search HNSW index against local fingerprints              │
+│     ├── Run SONA adaptation on local data                          │
+│     ├── Append results to local witness chain                      │
+│     └── Store updated vectors to local container                   │
+│                                                                     │
+│  3. SYNC (when connected)                                          │
+│     ├── Push new vectors to central RVF container                  │
+│     ├── Pull updated fingerprints from other nodes                 │
+│     ├── Merge SONA deltas via Raft (ADR-008)                      │
+│     ├── Extend witness chain with cross-node attestation           │
+│     └── Update local container with merged state                   │
+│                                                                     │
+│  4. SLEEP (battery conservation)                                   │
+│     ├── Flush pending writes to container                          │
+│     ├── Close memory-mapped segments                               │
+│     └── Resume from step 1 on wake                                │
+└────────────────────────────────────────────────────────────────────┘
+```
+
+### Browser-Specific Integration
+
+```rust
+/// Browser WASM entry point
+#[wasm_bindgen]
+pub struct WifiDensePoseEdge {
+    container: RvfContainer,
+    inference_engine: InferenceEngine,
+    hnsw_index: HnswIndex,
+    sona: Option<SonaAdapter>,
+}
+
+#[wasm_bindgen]
+impl WifiDensePoseEdge {
+    /// Initialize from an RVF container loaded via fetch or IndexedDB
+    #[wasm_bindgen(constructor)]
+    pub async fn new(container_bytes: &[u8]) -> Result<WifiDensePoseEdge, JsValue> {
+        let container = RvfContainer::from_bytes(container_bytes)?;
+        let engine = InferenceEngine::from_container(&container)?;
+        let index = HnswIndex::from_container(&container)?;
+        let sona = SonaAdapter::from_container(&container).ok();
+
+        Ok(Self { container, inference_engine: engine, hnsw_index: index, sona })
+    }
+
+    /// Process a single CSI frame (called from JavaScript)
+    #[wasm_bindgen]
+    pub fn process_frame(&mut self, csi_json: &str) -> Result<String, JsValue> {
+        let csi_data: CsiData = serde_json::from_str(csi_json)
+            .map_err(|e| JsValue::from_str(&e.to_string()))?;
+
+        let features = self.extract_features(&csi_data)?;
+        let detection = self.detect(&features)?;
+        let pose = if detection.human_detected {
+            Some(self.estimate_pose(&features)?)
+        } else {
+            None
+        };
+
+        serde_json::to_string(&PoseResult { detection, pose })
+            .map_err(|e| JsValue::from_str(&e.to_string()))
+    }
+
+    /// Save current state to IndexedDB
+    #[wasm_bindgen]
+    pub async fn persist(&self) -> Result<(), JsValue> {
+        let bytes = self.container.serialize()?;
+        // Write to IndexedDB via web-sys
+        save_to_indexeddb("wifi-densepose-state", &bytes).await
+    }
+}
+```
+
+### Model Quantization Strategy
+
+| Quantization | Size Reduction | Accuracy Loss | Suitable For |
+|-------------|---------------|---------------|-------------|
+| Float32 (baseline) | 1x | 0% | Server/desktop |
+| Float16 | 2x | <0.5% | Field tablets, GPUs |
+| Int8 (PTQ) | 4x | <2% | Browser, mobile |
+| Int4 (GPTQ) | 8x | <5% | IoT, ultra-constrained |
+| Binary (1-bit) | 32x | ~15% | MCU/ultra-edge (experimental) |
+
+## Consequences
+
+### Positive
+- **Single-file deployment**: Copy one `.rvf.edge` file to deploy anywhere
+- **Offline operation**: Full functionality without network connectivity
+- **125ms boot**: Near-instant readiness for emergency scenarios
+- **Platform universal**: Same container format for browser, IoT, mobile, server
+- **Battery efficient**: No network polling in offline mode
+
+### Negative
+- **Container size**: Even compressed, field containers are 50+ MB
+- **WASM performance**: 2-5x slower than native Rust for compute-heavy operations
+- **Browser limitations**: IndexedDB has storage quotas; WASM SIMD support varies
+- **Update latency**: Offline devices miss updates until reconnection
+- **Quantization accuracy**: Int4/Int8 models lose some detection sensitivity
+
+## References
+
+- [WebAssembly SIMD Proposal](https://github.com/WebAssembly/simd)
+- [IndexedDB API](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API)
+- [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/)
+- [Model Quantization Techniques](https://arxiv.org/abs/2103.13630)
+- [RuVector WASM Runtime](https://github.com/ruvnet/ruvector)
+- ADR-002: RuVector RVF Integration Strategy
+- ADR-003: RVF Cognitive Containers for CSI Data

docs/adr/ADR-010-witness-chains-audit-trail-integrity.md

@@ -0,0 +1,402 @@
+# ADR-010: Witness Chains for Audit Trail Integrity
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Life-Critical Audit Requirements
+
+The wifi-densepose-mat disaster detection module (ADR-001) makes triage classifications that directly affect rescue priority:
+
+| Triage Level | Action | Consequence of Error |
+|-------------|--------|---------------------|
+| P1 (Immediate/Red) | Rescue NOW | False negative → survivor dies waiting |
+| P2 (Delayed/Yellow) | Rescue within 1 hour | Misclassification → delayed rescue |
+| P3 (Minor/Green) | Rescue when resources allow | Over-triage → resource waste |
+| P4 (Deceased/Black) | No rescue attempted | False P4 → living person abandoned |
+
+Post-incident investigations, liability proceedings, and operational reviews require:
+
+1. **Non-repudiation**: Prove which device made which detection at which time
+2. **Tamper evidence**: Detect if records were altered after the fact
+3. **Completeness**: Prove no detections were deleted or hidden
+4. **Causal chain**: Reconstruct the sequence of events leading to each triage decision
+5. **Cross-device verification**: Corroborate detections across multiple APs
+
+### Current State
+
+Detection results are logged to the database (`wifi-densepose-db`) with standard INSERT operations. Logs can be:
+- Silently modified after the fact
+- Deleted without trace
+- Backdated or reordered
+- Lost if the database is corrupted
+
+No cryptographic integrity mechanism exists.
+
+### RuVector Witness Chains
+
+RuVector implements hash-linked audit trails inspired by blockchain but without the consensus overhead:
+
+- **Hash chain**: Each entry includes the SHAKE-256 hash of the previous entry, forming a tamper-evident chain
+- **Signatures**: Chain anchors (every Nth entry) are signed with the device's key pair
+- **Cross-chain attestation**: Multiple devices can cross-reference each other's chains
+- **Compact**: Each chain entry is ~100-200 bytes (hash + metadata + signature reference)
+
+## Decision
+
+We will implement RuVector witness chains as the primary audit mechanism for all detection events, triage decisions, and model adaptation events in the WiFi-DensePose system.
+
+### Witness Chain Structure
+
+```
+┌────────────────────────────────────────────────────────────────────┐
+│                      Witness Chain                                  │
+├────────────────────────────────────────────────────────────────────┤
+│                                                                     │
+│  Entry 0          Entry 1          Entry 2          Entry 3        │
+│  (Genesis)                                                          │
+│  ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐     │
+│  │ prev: ∅  │◀───│ prev: H0 │◀───│ prev: H1 │◀───│ prev: H2 │     │
+│  │ event:   │    │ event:   │    │ event:   │    │ event:   │     │
+│  │  INIT    │    │  DETECT  │    │  TRIAGE  │    │  ADAPT   │     │
+│  │ hash: H0 │    │ hash: H1 │    │ hash: H2 │    │ hash: H3 │     │
+│  │ sig: S0  │    │          │    │          │    │ sig: S1  │     │
+│  │ (anchor) │    │          │    │          │    │ (anchor) │     │
+│  └──────────┘    └──────────┘    └──────────┘    └──────────┘     │
+│                                                                     │
+│  H0 = SHAKE-256(INIT || device_id || timestamp)                    │
+│  H1 = SHAKE-256(DETECT_DATA || H0 || timestamp)                   │
+│  H2 = SHAKE-256(TRIAGE_DATA || H1 || timestamp)                   │
+│  H3 = SHAKE-256(ADAPT_DATA || H2 || timestamp)                    │
+│                                                                     │
+│  Anchor signature S0 = ML-DSA-65.sign(H0, device_key)             │
+│  Anchor signature S1 = ML-DSA-65.sign(H3, device_key)             │
+│  Anchor interval: every 100 entries (configurable)                 │
+└────────────────────────────────────────────────────────────────────┘
+```
+
+### Witnessed Event Types
+
+```rust
+/// Events recorded in the witness chain
+#[derive(Serialize, Deserialize, Clone)]
+pub enum WitnessedEvent {
+    /// Chain initialization (genesis)
+    ChainInit {
+        device_id: DeviceId,
+        firmware_version: String,
+        config_hash: [u8; 32],
+    },
+
+    /// Human presence detected
+    HumanDetected {
+        detection_id: Uuid,
+        confidence: f64,
+        csi_features_hash: [u8; 32],  // Hash of input data, not raw data
+        location_estimate: Option<GeoCoord>,
+        model_version: String,
+    },
+
+    /// Triage classification assigned or changed
+    TriageDecision {
+        survivor_id: Uuid,
+        previous_level: Option<TriageLevel>,
+        new_level: TriageLevel,
+        evidence_hash: [u8; 32],  // Hash of supporting evidence
+        deciding_algorithm: String,
+        confidence: f64,
+    },
+
+    /// False detection corrected
+    DetectionCorrected {
+        detection_id: Uuid,
+        correction_type: CorrectionType,  // FalsePositive | FalseNegative | Reclassified
+        reason: String,
+        corrected_by: CorrectorId,  // Device or operator
+    },
+
+    /// Model adapted via SONA
+    ModelAdapted {
+        adaptation_id: Uuid,
+        trigger: AdaptationTrigger,
+        lora_delta_hash: [u8; 32],
+        performance_before: f64,
+        performance_after: f64,
+    },
+
+    /// Zone scan completed
+    ZoneScanCompleted {
+        zone_id: ZoneId,
+        scan_duration_ms: u64,
+        detections_count: usize,
+        coverage_percentage: f64,
+    },
+
+    /// Cross-device attestation received
+    CrossAttestation {
+        attesting_device: DeviceId,
+        attested_chain_hash: [u8; 32],
+        attested_entry_index: u64,
+    },
+
+    /// Operator action (manual override)
+    OperatorAction {
+        operator_id: String,
+        action: OperatorActionType,
+        target: Uuid,  // What was acted upon
+        justification: String,
+    },
+}
+```
+
+### Chain Entry Structure
+
+```rust
+/// A single entry in the witness chain
+#[derive(Serialize, Deserialize)]
+pub struct WitnessEntry {
+    /// Sequential index in the chain
+    index: u64,
+
+    /// SHAKE-256 hash of the previous entry (32 bytes)
+    previous_hash: [u8; 32],
+
+    /// The witnessed event
+    event: WitnessedEvent,
+
+    /// Device that created this entry
+    device_id: DeviceId,
+
+    /// Monotonic timestamp (device-local, not wall clock)
+    monotonic_timestamp: u64,
+
+    /// Wall clock timestamp (best-effort, may be inaccurate)
+    wall_timestamp: DateTime<Utc>,
+
+    /// Vector clock for causal ordering (see ADR-008)
+    vector_clock: VectorClock,
+
+    /// This entry's hash: SHAKE-256(serialize(self without this field))
+    entry_hash: [u8; 32],
+
+    /// Anchor signature (present every N entries)
+    anchor_signature: Option<HybridSignature>,
+}
+```
+
+### Tamper Detection
+
+```rust
+/// Verify witness chain integrity
+pub fn verify_chain(chain: &[WitnessEntry]) -> Result<ChainVerification, AuditError> {
+    let mut verification = ChainVerification::new();
+
+    for (i, entry) in chain.iter().enumerate() {
+        // 1. Verify hash chain linkage
+        if i > 0 {
+            let expected_prev_hash = chain[i - 1].entry_hash;
+            if entry.previous_hash != expected_prev_hash {
+                verification.add_violation(ChainViolation::BrokenLink {
+                    entry_index: entry.index,
+                    expected_hash: expected_prev_hash,
+                    actual_hash: entry.previous_hash,
+                });
+            }
+        }
+
+        // 2. Verify entry self-hash
+        let computed_hash = compute_entry_hash(entry);
+        if computed_hash != entry.entry_hash {
+            verification.add_violation(ChainViolation::TamperedEntry {
+                entry_index: entry.index,
+            });
+        }
+
+        // 3. Verify anchor signatures
+        if let Some(ref sig) = entry.anchor_signature {
+            let device_keys = load_device_keys(&entry.device_id)?;
+            if !sig.verify(&entry.entry_hash, &device_keys.ed25519, &device_keys.ml_dsa)? {
+                verification.add_violation(ChainViolation::InvalidSignature {
+                    entry_index: entry.index,
+                });
+            }
+        }
+
+        // 4. Verify monotonic timestamp ordering
+        if i > 0 && entry.monotonic_timestamp <= chain[i - 1].monotonic_timestamp {
+            verification.add_violation(ChainViolation::NonMonotonicTimestamp {
+                entry_index: entry.index,
+            });
+        }
+
+        verification.verified_entries += 1;
+    }
+
+    Ok(verification)
+}
+```
+
+### Cross-Device Attestation
+
+Multiple APs can cross-reference each other's chains for stronger guarantees:
+
+```
+Device A's chain:                    Device B's chain:
+┌──────────┐                         ┌──────────┐
+│ Entry 50 │                         │ Entry 73 │
+│ H_A50    │◀────── cross-attest ───▶│ H_B73    │
+└──────────┘                         └──────────┘
+
+Device A records: CrossAttestation { attesting: B, hash: H_B73, index: 73 }
+Device B records: CrossAttestation { attesting: A, hash: H_A50, index: 50 }
+
+After cross-attestation:
+- Neither device can rewrite entries before the attested point
+  without the other device's chain becoming inconsistent
+- An investigator can verify both chains agree on the attestation point
+```
+
+**Attestation frequency**: Every 5 minutes during connected operation, immediately on significant events (P1 triage, zone completion).
+
+### Storage and Retrieval
+
+Witness chains are stored in the RVF container's WITNESS segment:
+
+```rust
+/// Witness chain storage manager
+pub struct WitnessChainStore {
+    /// Current chain being appended to
+    active_chain: Vec<WitnessEntry>,
+
+    /// Anchor signature interval
+    anchor_interval: usize,  // 100
+
+    /// Device signing key
+    device_key: DeviceKeyPair,
+
+    /// Cross-attestation peers
+    attestation_peers: Vec<DeviceId>,
+
+    /// RVF container for persistence
+    container: RvfContainer,
+}
+
+impl WitnessChainStore {
+    /// Append an event to the chain
+    pub fn witness(&mut self, event: WitnessedEvent) -> Result<u64, AuditError> {
+        let index = self.active_chain.len() as u64;
+        let previous_hash = self.active_chain.last()
+            .map(|e| e.entry_hash)
+            .unwrap_or([0u8; 32]);
+
+        let mut entry = WitnessEntry {
+            index,
+            previous_hash,
+            event,
+            device_id: self.device_key.device_id(),
+            monotonic_timestamp: monotonic_now(),
+            wall_timestamp: Utc::now(),
+            vector_clock: self.get_current_vclock(),
+            entry_hash: [0u8; 32],  // Computed below
+            anchor_signature: None,
+        };
+
+        // Compute entry hash
+        entry.entry_hash = compute_entry_hash(&entry);
+
+        // Add anchor signature at interval
+        if index % self.anchor_interval as u64 == 0 {
+            entry.anchor_signature = Some(
+                self.device_key.sign_hybrid(&entry.entry_hash)?
+            );
+        }
+
+        self.active_chain.push(entry);
+
+        // Persist to RVF container
+        self.container.append_witness(&self.active_chain.last().unwrap())?;
+
+        Ok(index)
+    }
+
+    /// Query chain for events in a time range
+    pub fn query_range(&self, start: DateTime<Utc>, end: DateTime<Utc>)
+        -> Vec<&WitnessEntry>
+    {
+        self.active_chain.iter()
+            .filter(|e| e.wall_timestamp >= start && e.wall_timestamp <= end)
+            .collect()
+    }
+
+    /// Export chain for external audit
+    pub fn export_for_audit(&self) -> AuditBundle {
+        AuditBundle {
+            chain: self.active_chain.clone(),
+            device_public_key: self.device_key.public_keys(),
+            cross_attestations: self.collect_cross_attestations(),
+            chain_summary: self.compute_summary(),
+        }
+    }
+}
+```
+
+### Performance Impact
+
+| Operation | Latency | Notes |
+|-----------|---------|-------|
+| Append entry | 0.05 ms | Hash computation + serialize |
+| Append with anchor signature | 0.5 ms | + ML-DSA-65 sign |
+| Verify single entry | 0.02 ms | Hash comparison |
+| Verify anchor | 0.3 ms | ML-DSA-65 verify |
+| Full chain verify (10K entries) | 50 ms | Sequential hash verification |
+| Cross-attestation | 1 ms | Sign + network round-trip |
+
+### Storage Requirements
+
+| Chain Length | Entries/Hour | Size/Hour | Size/Day |
+|-------------|-------------|-----------|----------|
+| Low activity | ~100 | ~20 KB | ~480 KB |
+| Normal operation | ~1,000 | ~200 KB | ~4.8 MB |
+| Disaster response | ~10,000 | ~2 MB | ~48 MB |
+| High-intensity scan | ~50,000 | ~10 MB | ~240 MB |
+
+## Consequences
+
+### Positive
+- **Tamper-evident**: Any modification to historical records is detectable
+- **Non-repudiable**: Signed anchors prove device identity
+- **Complete history**: Every detection, triage, and correction is recorded
+- **Cross-verified**: Multi-device attestation strengthens guarantees
+- **Forensically sound**: Exportable audit bundles for legal proceedings
+- **Low overhead**: 0.05ms per entry; minimal storage for normal operation
+
+### Negative
+- **Append-only growth**: Chains grow monotonically; need archival strategy for long deployments
+- **Key management**: Device keys must be provisioned and protected
+- **Clock dependency**: Wall-clock timestamps are best-effort; monotonic timestamps are device-local
+- **Verification cost**: Full chain verification of long chains takes meaningful time (50ms/10K entries)
+- **Privacy tension**: Detailed audit trails contain operational intelligence
+
+### Regulatory Alignment
+
+| Requirement | How Witness Chains Address It |
+|------------|------------------------------|
+| GDPR (Right to erasure) | Event hashes stored, not personal data; original data deletable while chain proves historical integrity |
+| HIPAA (Audit controls) | Complete access/modification log with non-repudiation |
+| ISO 27001 (Information security) | Tamper-evident records, access logging, integrity verification |
+| NIST SP 800-53 (AU controls) | Audit record generation, protection, and review capability |
+| FEMA ICS (Incident Command) | Chain of custody for all operational decisions |
+
+## References
+
+- [Witness Chains in Distributed Systems](https://eprint.iacr.org/2019/747)
+- [SHAKE-256 (FIPS 202)](https://csrc.nist.gov/pubs/fips/202/final)
+- [Tamper-Evident Logging](https://www.usenix.org/legacy/event/sec09/tech/full_papers/crosby.pdf)
+- [RuVector Witness Implementation](https://github.com/ruvnet/ruvector)
+- ADR-001: WiFi-Mat Disaster Detection Architecture
+- ADR-007: Post-Quantum Cryptography for Secure Sensing
+- ADR-008: Distributed Consensus for Multi-AP Coordination

docs/adr/ADR-011-python-proof-of-reality-mock-elimination.md

@@ -0,0 +1,414 @@
+# ADR-011: Python Proof-of-Reality and Mock Elimination
+
+## Status
+Proposed (URGENT)
+
+## Date
+2026-02-28
+
+## Context
+
+### The Credibility Problem
+
+The WiFi-DensePose Python codebase contains real, mathematically sound signal processing (FFT, phase unwrapping, Doppler extraction, correlation features) alongside mock/placeholder code that fatally undermines credibility. External reviewers who encounter **any** mock path in the default execution flow conclude the entire system is synthetic. This is not a technical problem - it is a perception problem with technical root causes.
+
+### Specific Mock/Placeholder Inventory
+
+The following code paths produce fake data **in the default configuration** or are easily mistaken for indicating fake functionality:
+
+#### Critical Severity (produces fake output on default path)
+
+| File | Line | Issue | Impact |
+|------|------|-------|--------|
+| `v1/src/core/csi_processor.py` | 390 | `doppler_shift = np.random.rand(10)  # Placeholder` | **Real feature extractor returns random Doppler** - kills credibility of entire feature pipeline |
+| `v1/src/hardware/csi_extractor.py` | 83-84 | `amplitude = np.random.rand(...)` in CSI extraction fallback | Random data silently substituted when parsing fails |
+| `v1/src/hardware/csi_extractor.py` | 129-135 | `_parse_atheros()` returns `np.random.rand()` with comment "placeholder implementation" | Named as if it parses real data, actually random |
+| `v1/src/hardware/router_interface.py` | 211-212 | `np.random.rand(3, 56)` in fallback path | Silent random fallback |
+| `v1/src/services/pose_service.py` | 431 | `mock_csi = np.random.randn(64, 56, 3)  # Mock CSI data` | Mock CSI in production code path |
+| `v1/src/services/pose_service.py` | 293-356 | `_generate_mock_poses()` with `random.randint` throughout | Entire mock pose generator in service layer |
+| `v1/src/services/pose_service.py` | 489-607 | Multiple `random.randint` for occupancy, historical data | Fake statistics that look real in API responses |
+| `v1/src/api/dependencies.py` | 82, 408 | "return a mock user for development" | Auth bypass in default path |
+
+#### Moderate Severity (mock gated behind flags but confusing)
+
+| File | Line | Issue |
+|------|------|-------|
+| `v1/src/config/settings.py` | 144-145 | `mock_hardware=False`, `mock_pose_data=False` defaults - correct, but mock infrastructure exists |
+| `v1/src/core/router_interface.py` | 27-300 | 270+ lines of mock data generation infrastructure in production code |
+| `v1/src/services/pose_service.py` | 84-88 | Silent conditional: `if not self.settings.mock_pose_data` with no logging of real-mode |
+| `v1/src/services/hardware_service.py` | 72-375 | Interleaved mock/real paths throughout |
+
+#### Low Severity (placeholders/TODOs)
+
+| File | Line | Issue |
+|------|------|-------|
+| `v1/src/core/router_interface.py` | 198 | "Collect real CSI data from router (placeholder implementation)" |
+| `v1/src/api/routers/health.py` | 170-171 | `uptime_seconds = 0.0  # TODO` |
+| `v1/src/services/pose_service.py` | 739 | `"uptime_seconds": 0.0  # TODO` |
+
+### Root Cause Analysis
+
+1. **No separation between mock and real**: Mock generators live in the same modules as real processors. A reviewer reading `csi_processor.py` hits `np.random.rand(10)` at line 390 and stops trusting the 400 lines of real signal processing above it.
+
+2. **Silent fallbacks**: When real hardware isn't available, the system silently falls back to random data instead of failing loudly. This means the default `docker compose up` produces plausible-looking but entirely fake results.
+
+3. **No proof artifact**: There is no shipped CSI capture file, no expected output hash, no way for a reviewer to verify that the pipeline produces deterministic results from real input.
+
+4. **Build environment fragility**: The `Dockerfile` references `requirements.txt` which doesn't exist as a standalone file. The `setup.py` hardcodes 87 dependencies. ONNX Runtime and BLAS are not in the container. A `docker build` may or may not succeed depending on the machine.
+
+5. **No CI verification**: No GitHub Actions workflow runs the pipeline on a real or deterministic input and verifies the output.
+
+## Decision
+
+We will eliminate the credibility gap through five concrete changes:
+
+### 1. Eliminate All Silent Mock Fallbacks (HARD FAIL)
+
+**Every path that currently returns `np.random.rand()` will either be replaced with real computation or will raise an explicit error.**
+
+```python
+# BEFORE (csi_processor.py:390)
+doppler_shift = np.random.rand(10)  # Placeholder
+
+# AFTER
+def _extract_doppler_features(self, csi_data: CSIData) -> tuple:
+    """Extract Doppler and frequency domain features from CSI temporal history."""
+    if len(self.csi_history) < 2:
+        # Not enough history for temporal analysis - return zeros, not random
+        doppler_shift = np.zeros(self.window_size)
+        psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
+        return doppler_shift, psd
+
+    # Real Doppler extraction from temporal CSI differences
+    history_array = np.array([h.amplitude for h in self.get_recent_history(self.window_size)])
+    # Compute phase differences over time (proportional to Doppler shift)
+    temporal_phase_diff = np.diff(np.angle(history_array + 1j * np.zeros_like(history_array)), axis=0)
+    # Average across antennas, FFT across time for Doppler spectrum
+    doppler_spectrum = np.abs(scipy.fft.fft(temporal_phase_diff.mean(axis=1), axis=0))
+    doppler_shift = doppler_spectrum.mean(axis=1)
+
+    psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
+    return doppler_shift, psd
+```
+
+```python
+# BEFORE (csi_extractor.py:129-135)
+def _parse_atheros(self, raw_data):
+    """Parse Atheros CSI format (placeholder implementation)."""
+    # For now, return mock data for testing
+    return CSIData(amplitude=np.random.rand(3, 56), ...)
+
+# AFTER
+def _parse_atheros(self, raw_data: bytes) -> CSIData:
+    """Parse Atheros CSI Tool format.
+
+    Format: https://dhalperi.github.io/linux-80211n-csitool/
+    """
+    if len(raw_data) < 25:  # Minimum Atheros CSI header
+        raise CSIExtractionError(
+            f"Atheros CSI data too short ({len(raw_data)} bytes). "
+            "Expected real CSI capture from Atheros-based NIC. "
+            "See docs/hardware-setup.md for capture instructions."
+        )
+    # Parse actual Atheros binary format
+    # ... real parsing implementation ...
+```
+
+### 2. Isolate Mock Infrastructure Behind Explicit Flag with Banner
+
+**All mock code moves to a dedicated module. Default execution NEVER touches mock paths.**
+
+```
+v1/src/
+├── core/
+│   ├── csi_processor.py        # Real processing only
+│   └── router_interface.py     # Real hardware interface only
+├── testing/                    # NEW: isolated mock module
+│   ├── __init__.py
+│   ├── mock_csi_generator.py   # Mock CSI generation (moved from router_interface)
+│   ├── mock_pose_generator.py  # Mock poses (moved from pose_service)
+│   └── fixtures/               # Test fixtures, not production paths
+│       ├── sample_csi_capture.bin  # Real captured CSI data (tiny sample)
+│       └── expected_output.json    # Expected pipeline output for sample
+```
+
+**Runtime enforcement:**
+```python
+import os
+import sys
+
+MOCK_MODE = os.environ.get("WIFI_DENSEPOSE_MOCK", "").lower() == "true"
+
+if MOCK_MODE:
+    # Print banner on EVERY log line
+    _original_log = logging.Logger._log
+    def _mock_banner_log(self, level, msg, args, **kwargs):
+        _original_log(self, level, f"[MOCK MODE] {msg}", args, **kwargs)
+    logging.Logger._log = _mock_banner_log
+
+    print("=" * 72, file=sys.stderr)
+    print("  WARNING: RUNNING IN MOCK MODE - ALL DATA IS SYNTHETIC", file=sys.stderr)
+    print("  Set WIFI_DENSEPOSE_MOCK=false for real operation", file=sys.stderr)
+    print("=" * 72, file=sys.stderr)
+```
+
+### 3. Ship a Reproducible Proof Bundle
+
+A small real CSI capture file + one-command verification pipeline:
+
+```
+v1/data/proof/
+├── README.md                      # How to verify
+├── sample_csi_capture.bin         # Real CSI data (1 second, ~50 KB)
+├── sample_csi_capture_meta.json   # Capture metadata (hardware, env)
+├── expected_features.json         # Expected feature extraction output
+├── expected_features.sha256       # SHA-256 hash of expected output
+└── verify.py                      # One-command verification script
+```
+
+**verify.py**:
+```python
+#!/usr/bin/env python3
+"""Verify WiFi-DensePose pipeline produces deterministic output from real CSI data.
+
+Usage:
+    python v1/data/proof/verify.py
+
+Expected output:
+    PASS: Pipeline output matches expected hash
+    SHA256: <hash>
+
+If this passes, the signal processing pipeline is producing real,
+deterministic results from real captured CSI data.
+"""
+import hashlib
+import json
+import sys
+import os
+
+# Ensure reproducibility
+os.environ["PYTHONHASHSEED"] = "42"
+import numpy as np
+np.random.seed(42)  # Only affects any remaining random elements
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
+
+from src.core.csi_processor import CSIProcessor
+from src.hardware.csi_extractor import CSIExtractor
+
+def main():
+    # Load real captured CSI data
+    capture_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture.bin")
+    meta_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture_meta.json")
+    expected_hash_path = os.path.join(os.path.dirname(__file__), "expected_features.sha256")
+
+    with open(meta_path) as f:
+        meta = json.load(f)
+
+    # Extract CSI from binary capture
+    extractor = CSIExtractor(format=meta["format"])
+    csi_data = extractor.extract_from_file(capture_path)
+
+    # Process through feature pipeline
+    config = {
+        "sampling_rate": meta["sampling_rate"],
+        "window_size": meta["window_size"],
+        "overlap": meta["overlap"],
+        "noise_threshold": meta["noise_threshold"],
+    }
+    processor = CSIProcessor(config)
+    features = processor.extract_features(csi_data)
+
+    # Serialize features deterministically
+    output = {
+        "amplitude_mean": features.amplitude_mean.tolist(),
+        "amplitude_variance": features.amplitude_variance.tolist(),
+        "phase_difference": features.phase_difference.tolist(),
+        "doppler_shift": features.doppler_shift.tolist(),
+        "psd_first_16": features.power_spectral_density[:16].tolist(),
+    }
+    output_json = json.dumps(output, sort_keys=True, separators=(",", ":"))
+    output_hash = hashlib.sha256(output_json.encode()).hexdigest()
+
+    # Verify against expected hash
+    with open(expected_hash_path) as f:
+        expected_hash = f.read().strip()
+
+    if output_hash == expected_hash:
+        print(f"PASS: Pipeline output matches expected hash")
+        print(f"SHA256: {output_hash}")
+        print(f"Features: {len(output['amplitude_mean'])} subcarriers processed")
+        return 0
+    else:
+        print(f"FAIL: Hash mismatch")
+        print(f"Expected: {expected_hash}")
+        print(f"Got:      {output_hash}")
+        return 1
+
+if __name__ == "__main__":
+    sys.exit(main())
+```
+
+### 4. Pin the Build Environment
+
+**Option A (recommended): Deterministic Dockerfile that works on fresh machine**
+
+```dockerfile
+FROM python:3.11-slim
+
+# System deps that actually matter
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libopenblas-dev \
+    libfftw3-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Pinned requirements (not a reference to missing file)
+COPY v1/requirements-lock.txt ./requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY v1/ ./v1/
+
+# Proof of reality: verify pipeline on build
+RUN cd v1 && python data/proof/verify.py
+
+EXPOSE 8000
+# Default: REAL mode (mock requires explicit opt-in)
+ENV WIFI_DENSEPOSE_MOCK=false
+CMD ["uvicorn", "v1.src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
+```
+
+**Key change**: `RUN python data/proof/verify.py` **during build** means the Docker image cannot be created unless the pipeline produces correct output from real CSI data.
+
+**Requirements lockfile** (`v1/requirements-lock.txt`):
+```
+# Core (required)
+fastapi==0.115.6
+uvicorn[standard]==0.34.0
+pydantic==2.10.4
+pydantic-settings==2.7.1
+numpy==1.26.4
+scipy==1.14.1
+
+# Signal processing (required)
+# No ONNX required for basic pipeline verification
+
+# Optional (install separately for full features)
+# torch>=2.1.0
+# onnxruntime>=1.17.0
+```
+
+### 5. CI Pipeline That Proves Reality
+
+```yaml
+# .github/workflows/verify-pipeline.yml
+name: Verify Signal Pipeline
+
+on:
+  push:
+    paths: ['v1/src/**', 'v1/data/proof/**']
+  pull_request:
+    paths: ['v1/src/**']
+
+jobs:
+  verify:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - name: Install minimal deps
+        run: pip install numpy scipy pydantic pydantic-settings
+      - name: Verify pipeline determinism
+        run: python v1/data/proof/verify.py
+      - name: Verify no random in production paths
+        run: |
+          # Fail if np.random appears in production code (not in testing/)
+          ! grep -r "np\.random\.\(rand\|randn\|randint\)" v1/src/ \
+            --include="*.py" \
+            --exclude-dir=testing \
+            || (echo "FAIL: np.random found in production code" && exit 1)
+```
+
+### Concrete File Changes Required
+
+| File | Action | Description |
+|------|--------|-------------|
+| `v1/src/core/csi_processor.py:390` | **Replace** | Real Doppler extraction from temporal CSI history |
+| `v1/src/hardware/csi_extractor.py:83-84` | **Replace** | Hard error with descriptive message when parsing fails |
+| `v1/src/hardware/csi_extractor.py:129-135` | **Replace** | Real Atheros CSI parser or hard error with hardware instructions |
+| `v1/src/hardware/router_interface.py:198-212` | **Replace** | Hard error for unimplemented hardware, or real `iwconfig` + CSI tool integration |
+| `v1/src/services/pose_service.py:293-356` | **Move** | Move `_generate_mock_poses()` to `v1/src/testing/mock_pose_generator.py` |
+| `v1/src/services/pose_service.py:430-431` | **Remove** | Remove mock CSI generation from production path |
+| `v1/src/services/pose_service.py:489-607` | **Replace** | Real statistics from database, or explicit "no data" response |
+| `v1/src/core/router_interface.py:60-300` | **Move** | Move mock generator to `v1/src/testing/mock_csi_generator.py` |
+| `v1/src/api/dependencies.py:82,408` | **Replace** | Real auth check or explicit dev-mode bypass with logging |
+| `v1/data/proof/` | **Create** | Proof bundle (sample capture + expected hash + verify script) |
+| `v1/requirements-lock.txt` | **Create** | Pinned minimal dependencies |
+| `.github/workflows/verify-pipeline.yml` | **Create** | CI verification |
+
+### Hardware Documentation
+
+```
+v1/docs/hardware-setup.md (to be created)
+
+# Supported Hardware Matrix
+
+| Chipset | Tool | OS | Capture Command |
+|---------|------|----|-----------------|
+| Intel 5300 | Linux 802.11n CSI Tool | Ubuntu 18.04 | `sudo ./log_to_file csi.dat` |
+| Atheros AR9580 | Atheros CSI Tool | Ubuntu 14.04 | `sudo ./recv_csi csi.dat` |
+| Broadcom BCM4339 | Nexmon CSI | Android/Nexus 5 | `nexutil -m1 -k1 ...` |
+| ESP32 | ESP32-CSI | ESP-IDF | `csi_recv --format binary` |
+
+# Calibration
+1. Place router and receiver 2m apart, line of sight
+2. Capture 10 seconds of empty-room baseline
+3. Have one person walk through at normal pace
+4. Capture 10 seconds during walk-through
+5. Run calibration: `python v1/scripts/calibrate.py --baseline empty.dat --activity walk.dat`
+```
+
+## Consequences
+
+### Positive
+- **"Clone, build, verify" in one command**: `docker build . && docker run --rm wifi-densepose python v1/data/proof/verify.py` produces a deterministic PASS
+- **No silent fakes**: Random data never appears in production output
+- **CI enforcement**: PRs that introduce `np.random` in production paths fail automatically
+- **Credibility anchor**: SHA-256 verified output from real CSI capture is unchallengeable proof
+- **Clear mock boundary**: Mock code exists only in `v1/src/testing/`, never imported by production modules
+
+### Negative
+- **Requires real CSI capture**: Someone must capture and commit a real CSI sample (one-time effort)
+- **Build may fail without hardware**: Without mock fallback, systems without WiFi hardware cannot demo - must use proof bundle instead
+- **Migration effort**: Moving mock code to separate module requires updating imports in test files
+- **Stricter development workflow**: Developers must explicitly opt in to mock mode
+
+### Acceptance Criteria
+
+A stranger can:
+1. `git clone` the repository
+2. Run ONE command (`docker build .` or `python v1/data/proof/verify.py`)
+3. See `PASS: Pipeline output matches expected hash` with a specific SHA-256
+4. Confirm no `np.random` in any non-test file via CI badge
+
+If this works 100% over 5 runs on a clean machine, the "fake" narrative dies.
+
+### Answering the Two Key Questions
+
+**Q1: Docker or Nix first?**
+Recommendation: **Docker first**. The Dockerfile already exists, just needs fixing. Nix is higher quality but smaller audience. Docker gives the widest "clone and verify" coverage.
+
+**Q2: Are external crates public and versioned?**
+The Python dependencies are all public PyPI packages. The Rust `ruvector-core` and `ruvector-data-framework` crates are currently commented out in `Cargo.toml` (lines 83-84: `# ruvector-core = "0.1"`) and are not yet published to crates.io. They are internal to ruvnet. This is a blocker for the Rust path but does not affect the Python proof-of-reality work in this ADR.
+
+## References
+
+- [Linux 802.11n CSI Tool](https://dhalperi.github.io/linux-80211n-csitool/)
+- [Atheros CSI Tool](https://wands.sg/research/wifi/AthesCSI/)
+- [Nexmon CSI](https://github.com/seemoo-lab/nexmon_csi)
+- [ESP32 CSI](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
+- [Reproducible Builds](https://reproducible-builds.org/)
+- ADR-002: RuVector RVF Integration Strategy

docs/adr/ADR-012-esp32-csi-sensor-mesh.md

@@ -0,0 +1,318 @@
+# ADR-012: ESP32 CSI Sensor Mesh for Distributed Sensing
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### The Hardware Reality Gap
+
+WiFi-DensePose's Rust and Python pipelines implement real signal processing (FFT, phase unwrapping, Doppler extraction, correlation features), but the system currently has no defined path from **physical WiFi hardware → CSI bytes → pipeline input**. The `csi_extractor.py` and `router_interface.py` modules contain placeholder parsers that return `np.random.rand()` instead of real parsed data (see ADR-011).
+
+To close this gap, we need a concrete, affordable, reproducible hardware platform that produces real CSI data and streams it into the existing pipeline.
+
+### Why ESP32
+
+| Factor | ESP32/ESP32-S3 | Intel 5300 (iwl5300) | Atheros AR9580 |
+|--------|---------------|---------------------|----------------|
+| Cost | ~$5-15/node | ~$50-100 (used NIC) | ~$30-60 (used NIC) |
+| Availability | Mass produced, in stock | Discontinued, eBay only | Discontinued, eBay only |
+| CSI Support | Official ESP-IDF API | Linux CSI Tool (kernel mod) | Atheros CSI Tool |
+| Form Factor | Standalone MCU | Requires PCIe/Mini-PCIe host | Requires PCIe host |
+| Deployment | Battery/USB, wireless | Desktop/laptop only | Desktop/laptop only |
+| Antenna Config | 1-2 TX, 1-2 RX | 3 TX, 3 RX (MIMO) | 3 TX, 3 RX (MIMO) |
+| Subcarriers | 52-56 (802.11n) | 30 (compressed) | 56 (full) |
+| Fidelity | Lower (consumer SoC) | Higher (dedicated NIC) | Higher (dedicated NIC) |
+
+**ESP32 wins on deployability**: It's the only option where a stranger can buy nodes on Amazon, flash firmware, and have a working CSI mesh in an afternoon. Intel 5300 and Atheros cards require specific hardware, kernel modifications, and legacy OS versions.
+
+### ESP-IDF CSI API
+
+Espressif provides official CSI support through three key functions:
+
+```c
+// 1. Configure what CSI data to capture
+wifi_csi_config_t csi_config = {
+    .lltf_en = true,         // Long Training Field (best for CSI)
+    .htltf_en = true,        // HT-LTF
+    .stbc_htltf2_en = true,  // STBC HT-LTF2
+    .ltf_merge_en = true,    // Merge LTFs
+    .channel_filter_en = false,
+    .manu_scale = false,
+};
+esp_wifi_set_csi_config(&csi_config);
+
+// 2. Register callback for received CSI data
+esp_wifi_set_csi_rx_cb(csi_data_callback, NULL);
+
+// 3. Enable CSI collection
+esp_wifi_set_csi(true);
+
+// Callback receives:
+void csi_data_callback(void *ctx, wifi_csi_info_t *info) {
+    // info->rx_ctrl: RSSI, noise_floor, channel, secondary_channel, etc.
+    // info->buf: Raw CSI data (I/Q pairs per subcarrier)
+    // info->len: Length of CSI data buffer
+    // Typical: 112 bytes = 56 subcarriers × 2 (I,Q) × 1 byte each
+}
+```
+
+## Decision
+
+We will build an ESP32 CSI Sensor Mesh as the primary hardware integration path, with a full stack from firmware to aggregator to Rust pipeline to visualization.
+
+### System Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│                   ESP32 CSI Sensor Mesh                              │
+├─────────────────────────────────────────────────────────────────────┤
+│                                                                      │
+│  ┌──────────┐  ┌──────────┐  ┌──────────┐                          │
+│  │ ESP32    │  │ ESP32    │  │ ESP32    │  ... (3-6 nodes)         │
+│  │ Node 1  │  │ Node 2  │  │ Node 3  │                          │
+│  │          │  │          │  │          │                          │
+│  │ CSI Rx   │  │ CSI Rx   │  │ CSI Rx   │  ← WiFi frames from    │
+│  │ FFT      │  │ FFT      │  │ FFT      │     consumer router     │
+│  │ Features │  │ Features │  │ Features │                          │
+│  └────┬─────┘  └────┬─────┘  └────┬─────┘                          │
+│       │              │              │                                │
+│       │    UDP/TCP stream (WiFi or secondary channel)               │
+│       │              │              │                                │
+│       ▼              ▼              ▼                                │
+│  ┌─────────────────────────────────────────┐                        │
+│  │           Aggregator                     │                        │
+│  │  (Laptop / Raspberry Pi / Seed device)  │                        │
+│  │                                          │                        │
+│  │  1. Receive CSI streams from all nodes  │                        │
+│  │  2. Timestamp alignment (per-node)       │                        │
+│  │  3. Feature-level fusion                │                        │
+│  │  4. Feed into Rust/Python pipeline      │                        │
+│  │  5. Serve WebSocket to visualization    │                        │
+│  └──────────────────┬──────────────────────┘                        │
+│                      │                                               │
+│                      ▼                                               │
+│  ┌─────────────────────────────────────────┐                        │
+│  │        WiFi-DensePose Pipeline           │                        │
+│  │                                          │                        │
+│  │  CsiProcessor → FeatureExtractor →      │                        │
+│  │  MotionDetector → PoseEstimator →       │                        │
+│  │  Three.js Visualization                 │                        │
+│  └─────────────────────────────────────────┘                        │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+### Node Firmware Specification
+
+**ESP-IDF project**: `firmware/esp32-csi-node/`
+
+```
+firmware/esp32-csi-node/
+├── CMakeLists.txt
+├── sdkconfig.defaults      # Menuconfig defaults with CSI enabled
+├── main/
+│   ├── CMakeLists.txt
+│   ├── main.c              # Entry point, WiFi init, CSI callback
+│   ├── csi_collector.c     # CSI data collection and buffering
+│   ├── csi_collector.h
+│   ├── feature_extract.c   # On-device FFT and feature extraction
+│   ├── feature_extract.h
+│   ├── stream_sender.c     # UDP stream to aggregator
+│   ├── stream_sender.h
+│   ├── config.h             # Node configuration (SSID, aggregator IP)
+│   └── Kconfig.projbuild   # Menuconfig options
+├── components/
+│   └── esp_dsp/            # Espressif DSP library for FFT
+└── README.md               # Flash instructions
+```
+
+**On-device processing** (reduces bandwidth, node does pre-processing):
+
+```c
+// feature_extract.c
+typedef struct {
+    uint32_t timestamp_ms;      // Local monotonic timestamp
+    uint8_t  node_id;           // This node's ID
+    int8_t   rssi;              // Received signal strength
+    int8_t   noise_floor;       // Noise floor estimate
+    uint8_t  channel;           // WiFi channel
+    float    amplitude[56];     // |CSI| per subcarrier (from I/Q)
+    float    phase[56];         // arg(CSI) per subcarrier
+    float    doppler_energy;    // Motion energy from temporal FFT
+    float    breathing_band;    // 0.1-0.5 Hz band power
+    float    motion_band;       // 0.5-3 Hz band power
+} csi_feature_frame_t;
+// Size: ~470 bytes per frame
+// At 100 Hz: ~47 KB/s per node, ~280 KB/s for 6 nodes
+```
+
+**Key firmware design decisions**:
+
+1. **Feature extraction on-device**: Raw CSI I/Q → amplitude + phase + spectral bands. This cuts bandwidth from raw ~11 KB/frame to ~470 bytes/frame.
+
+2. **Monotonic timestamps**: Each node uses its own monotonic clock. No NTP synchronization attempted between nodes - clock drift is handled at the aggregator by fusing features, not raw phases (see "Clock Drift" section below).
+
+3. **UDP streaming**: Low-latency, loss-tolerant. Missing frames are acceptable; ordering is maintained via sequence numbers.
+
+4. **Configurable sampling rate**: 10-100 Hz via menuconfig. 100 Hz for motion detection, 10 Hz sufficient for occupancy.
+
+### Aggregator Specification
+
+The aggregator runs on any machine with WiFi/Ethernet to the nodes:
+
+```rust
+// In wifi-densepose-rs, new module: crates/wifi-densepose-hardware/src/esp32/
+pub struct Esp32Aggregator {
+    /// UDP socket listening for node streams
+    socket: UdpSocket,
+
+    /// Per-node state (last timestamp, feature buffer, drift estimate)
+    nodes: HashMap<u8, NodeState>,
+
+    /// Ring buffer of fused feature frames
+    fused_buffer: VecDeque<FusedFrame>,
+
+    /// Channel to pipeline
+    pipeline_tx: mpsc::Sender<CsiData>,
+}
+
+/// Fused frame from all nodes for one time window
+pub struct FusedFrame {
+    /// Timestamp (aggregator local, monotonic)
+    timestamp: Instant,
+
+    /// Per-node features (may have gaps if node dropped)
+    node_features: Vec<Option<CsiFeatureFrame>>,
+
+    /// Cross-node correlation (computed by aggregator)
+    cross_node_correlation: Array2<f64>,
+
+    /// Fused motion energy (max across nodes)
+    fused_motion_energy: f64,
+
+    /// Fused breathing band (coherent sum where phase aligns)
+    fused_breathing_band: f64,
+}
+```
+
+### Clock Drift Handling
+
+ESP32 crystal oscillators drift ~20-50 ppm. Over 1 hour, two nodes may diverge by 72-180ms. This makes raw phase alignment across nodes impossible.
+
+**Solution**: Feature-level fusion, not signal-level fusion.
+
+```
+Signal-level (WRONG for ESP32):
+  Align raw I/Q samples across nodes → requires <1µs sync → impractical
+
+Feature-level (CORRECT for ESP32):
+  Each node: raw CSI → amplitude + phase + spectral features (local)
+  Aggregator: collect features → correlate → fuse decisions
+  No cross-node phase alignment needed
+```
+
+Specifically:
+- **Motion energy**: Take max across nodes (any node seeing motion = motion)
+- **Breathing band**: Use node with highest SNR as primary, others as corroboration
+- **Location**: Cross-node amplitude ratios estimate position (no phase needed)
+
+### Sensing Capabilities by Deployment
+
+| Capability | 1 Node | 3 Nodes | 6 Nodes | Evidence |
+|-----------|--------|---------|---------|----------|
+| Presence detection | Good | Excellent | Excellent | Single-node RSSI variance |
+| Coarse motion | Good | Excellent | Excellent | Doppler energy |
+| Room-level location | None | Good | Excellent | Amplitude ratios |
+| Respiration | Marginal | Good | Good | 0.1-0.5 Hz band, placement-sensitive |
+| Heartbeat | Poor | Poor-Marginal | Marginal | Requires ideal placement, low noise |
+| Multi-person count | None | Marginal | Good | Spatial diversity |
+| Pose estimation | None | Poor | Marginal | Requires model + sufficient diversity |
+
+**Honest assessment**: ESP32 CSI is lower fidelity than Intel 5300 or Atheros. Heartbeat detection is placement-sensitive and unreliable. Respiration works with good placement. Motion and presence are solid.
+
+### Failure Modes and Mitigations
+
+| Failure Mode | Severity | Mitigation |
+|-------------|----------|------------|
+| Multipath dominates in cluttered rooms | High | Mesh diversity: 3+ nodes from different angles |
+| Person occludes path between node and router | Medium | Mesh: other nodes still have clear paths |
+| Clock drift ruins cross-node fusion | Medium | Feature-level fusion only; no cross-node phase alignment |
+| UDP packet loss during high traffic | Low | Sequence numbers, interpolation for gaps <100ms |
+| ESP32 WiFi driver bugs with CSI | Medium | Pin ESP-IDF version, test on known-good boards |
+| Node power failure | Low | Aggregator handles missing nodes gracefully |
+
+### Bill of Materials (Starter Kit)
+
+| Item | Quantity | Unit Cost | Total |
+|------|----------|-----------|-------|
+| ESP32-S3-DevKitC-1 | 3 | $10 | $30 |
+| USB-A to USB-C cables | 3 | $3 | $9 |
+| USB power adapter (multi-port) | 1 | $15 | $15 |
+| Consumer WiFi router (any) | 1 | $0 (existing) | $0 |
+| Aggregator (laptop or Pi 4) | 1 | $0 (existing) | $0 |
+| **Total** | | | **$54** |
+
+### Minimal Build Spec (Clone-Flash-Run)
+
+```
+# Step 1: Flash one node (requires ESP-IDF installed)
+cd firmware/esp32-csi-node
+idf.py set-target esp32s3
+idf.py menuconfig  # Set WiFi SSID/password, aggregator IP
+idf.py build flash monitor
+
+# Step 2: Run aggregator (Docker)
+docker compose -f docker-compose.esp32.yml up
+
+# Step 3: Verify with proof bundle
+# Aggregator captures 10 seconds, produces feature JSON, verifies hash
+docker exec aggregator python verify_esp32.py
+
+# Step 4: Open visualization
+open http://localhost:3000  # Three.js dashboard
+```
+
+### Proof of Reality for ESP32
+
+```
+firmware/esp32-csi-node/proof/
+├── captured_csi_10sec.bin          # Real 10-second CSI capture from ESP32
+├── captured_csi_meta.json          # Board: ESP32-S3-DevKitC, ESP-IDF: 5.2, Router: TP-Link AX1800
+├── expected_features.json          # Feature extraction output
+├── expected_features.sha256        # Hash verification
+└── capture_photo.jpg               # Photo of actual hardware setup
+```
+
+## Consequences
+
+### Positive
+- **$54 starter kit**: Lowest possible barrier to real CSI data
+- **Mass available hardware**: ESP32 boards are in stock globally
+- **Real data path**: Eliminates every `np.random.rand()` placeholder with actual hardware input
+- **Proof artifact**: Captured CSI + expected hash proves the pipeline processes real data
+- **Scalable mesh**: Add nodes for more coverage without changing software
+- **Feature-level fusion**: Avoids the impossible problem of cross-node phase synchronization
+
+### Negative
+- **Lower fidelity than research NICs**: ESP32 CSI is noisier than Intel 5300
+- **Heartbeat detection unreliable**: Micro-Doppler resolution insufficient for consistent heartbeat
+- **ESP-IDF learning curve**: Firmware development requires embedded C knowledge
+- **WiFi interference**: Nodes sharing the same channel as data traffic adds noise
+- **Placement sensitivity**: Respiration detection requires careful node positioning
+
+### Interaction with Other ADRs
+- **ADR-011** (Proof of Reality): ESP32 provides the real CSI capture for the proof bundle
+- **ADR-008** (Distributed Consensus): Mesh nodes can use simplified Raft for configuration distribution
+- **ADR-003** (RVF Containers): Aggregator stores CSI features in RVF format
+- **ADR-004** (HNSW): Environment fingerprints from ESP32 mesh feed HNSW index
+
+## References
+
+- [Espressif ESP-CSI Repository](https://github.com/espressif/esp-csi)
+- [ESP-IDF WiFi CSI API](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
+- [ESP32 CSI Research Papers](https://ieeexplore.ieee.org/document/9439871)
+- [Wi-Fi Sensing with ESP32: A Tutorial](https://arxiv.org/abs/2207.07859)
+- ADR-011: Python Proof-of-Reality and Mock Elimination

docs/adr/ADR-013-feature-level-sensing-commodity-gear.md

@@ -0,0 +1,383 @@
+# ADR-013: Feature-Level Sensing on Commodity Gear (Option 3)
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+### Not Everyone Can Deploy Custom Hardware
+
+ADR-012 specifies an ESP32 CSI mesh that provides real CSI data. However, it requires:
+- Purchasing ESP32 boards
+- Flashing custom firmware
+- ESP-IDF toolchain installation
+- Physical placement of nodes
+
+For many users - especially those evaluating WiFi-DensePose or deploying in managed environments - modifying hardware is not an option. We need a sensing path that works with **existing, unmodified consumer WiFi gear**.
+
+### What Commodity Hardware Exposes
+
+Standard WiFi drivers and tools expose several metrics without custom firmware:
+
+| Signal | Source | Availability | Sampling Rate |
+|--------|--------|-------------|---------------|
+| RSSI (Received Signal Strength) | `iwconfig`, `iw`, NetworkManager | Universal | 1-10 Hz |
+| Noise floor | `iw dev wlan0 survey dump` | Most Linux drivers | ~1 Hz |
+| Link quality | `/proc/net/wireless` | Linux | 1-10 Hz |
+| MCS index / PHY rate | `iw dev wlan0 link` | Most drivers | Per-packet |
+| TX/RX bytes | `/sys/class/net/wlan0/statistics/` | Universal | Continuous |
+| Retry count | `iw dev wlan0 station dump` | Most drivers | ~1 Hz |
+| Beacon interval timing | `iw dev wlan0 scan dump` | Universal | Per-scan |
+| Channel utilization | `iw dev wlan0 survey dump` | Most drivers | ~1 Hz |
+
+**RSSI is the primary signal**. It varies when humans move through the propagation path between any transmitter-receiver pair. Research confirms RSSI-based sensing for:
+- Presence detection (single receiver, threshold on variance)
+- Device-free motion detection (RSSI variance increases with movement)
+- Coarse room-level localization (multi-receiver RSSI fingerprinting)
+- Breathing detection (specialized setups, marginal quality)
+
+### Research Support
+
+- **RSSI-based presence**: Youssef et al. (2007) demonstrated device-free passive detection using RSSI from multiple receivers with >90% accuracy.
+- **RSSI breathing**: Abdelnasser et al. (2015) showed respiration detection via RSSI variance in controlled settings with ~85% accuracy using 4+ receivers.
+- **Device-free tracking**: Multiple receivers with RSSI fingerprinting achieve room-level (3-5m) accuracy.
+
+## Decision
+
+We will implement a Feature-Level Sensing module that extracts motion, presence, and coarse activity information from standard WiFi metrics available on any Linux machine without hardware modification.
+
+### Architecture
+
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│              Feature-Level Sensing Pipeline                           │
+├──────────────────────────────────────────────────────────────────────┤
+│                                                                       │
+│  Data Sources (any Linux WiFi device):                               │
+│  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌──────────────┐              │
+│  │ RSSI    │ │ Noise   │ │ Link    │ │ Packet Stats │              │
+│  │ Stream  │ │ Floor   │ │ Quality │ │ (TX/RX/Retry)│              │
+│  └────┬────┘ └────┬────┘ └────┬────┘ └──────┬───────┘              │
+│       │           │           │              │                       │
+│       └───────────┴───────────┴──────────────┘                       │
+│                           │                                          │
+│                           ▼                                          │
+│  ┌────────────────────────────────────────────────┐                  │
+│  │           Feature Extraction Engine             │                  │
+│  │                                                 │                  │
+│  │  1. Rolling statistics (mean, var, skew, kurt)  │                  │
+│  │  2. Spectral features (FFT of RSSI time series) │                  │
+│  │  3. Change-point detection (CUSUM, PELT)        │                  │
+│  │  4. Cross-receiver correlation                   │                  │
+│  │  5. Packet timing jitter analysis               │                  │
+│  └────────────────────────┬───────────────────────┘                  │
+│                           │                                          │
+│                           ▼                                          │
+│  ┌────────────────────────────────────────────────┐                  │
+│  │          Classification / Decision              │                  │
+│  │                                                 │                  │
+│  │  • Presence: RSSI variance > threshold          │                  │
+│  │  • Motion class: spectral peak frequency        │                  │
+│  │  • Occupancy change: change-point event         │                  │
+│  │  • Confidence: cross-receiver agreement         │                  │
+│  └────────────────────────┬───────────────────────┘                  │
+│                           │                                          │
+│                           ▼                                          │
+│  ┌────────────────────────────────────────────────┐                  │
+│  │         Output: Presence/Motion Events          │                  │
+│  │                                                 │                  │
+│  │  { "timestamp": "...",                          │                  │
+│  │    "presence": true,                            │                  │
+│  │    "motion_level": "active",                    │                  │
+│  │    "confidence": 0.87,                          │                  │
+│  │    "receivers_agreeing": 3,                     │                  │
+│  │    "rssi_variance": 4.2 }                       │                  │
+│  └────────────────────────────────────────────────┘                  │
+└──────────────────────────────────────────────────────────────────────┘
+```
+
+### Feature Extraction Specification
+
+```python
+class RssiFeatureExtractor:
+    """Extract sensing features from RSSI and link statistics.
+
+    No custom hardware required. Works with any WiFi interface
+    that exposes standard Linux wireless statistics.
+    """
+
+    def __init__(self, config: FeatureSensingConfig):
+        self.window_size = config.window_size  # 30 seconds
+        self.sampling_rate = config.sampling_rate  # 10 Hz
+        self.rssi_buffer = deque(maxlen=self.window_size * self.sampling_rate)
+        self.noise_buffer = deque(maxlen=self.window_size * self.sampling_rate)
+
+    def extract_features(self) -> FeatureVector:
+        rssi_array = np.array(self.rssi_buffer)
+
+        return FeatureVector(
+            # Time-domain statistics
+            rssi_mean=np.mean(rssi_array),
+            rssi_variance=np.var(rssi_array),
+            rssi_skewness=scipy.stats.skew(rssi_array),
+            rssi_kurtosis=scipy.stats.kurtosis(rssi_array),
+            rssi_range=np.ptp(rssi_array),
+            rssi_iqr=np.subtract(*np.percentile(rssi_array, [75, 25])),
+
+            # Spectral features (FFT of RSSI time series)
+            spectral_energy=self._spectral_energy(rssi_array),
+            dominant_frequency=self._dominant_freq(rssi_array),
+            breathing_band_power=self._band_power(rssi_array, 0.1, 0.5),  # Hz
+            motion_band_power=self._band_power(rssi_array, 0.5, 3.0),    # Hz
+
+            # Change-point features
+            num_change_points=self._cusum_changes(rssi_array),
+            max_step_magnitude=self._max_step(rssi_array),
+
+            # Noise floor features (environment stability)
+            noise_mean=np.mean(np.array(self.noise_buffer)),
+            snr_estimate=np.mean(rssi_array) - np.mean(np.array(self.noise_buffer)),
+        )
+
+    def _spectral_energy(self, rssi: np.ndarray) -> float:
+        """Total spectral energy excluding DC component."""
+        spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi)))
+        return float(np.sum(spectrum[1:] ** 2))
+
+    def _dominant_freq(self, rssi: np.ndarray) -> float:
+        """Dominant frequency in RSSI time series."""
+        spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi)))
+        freqs = scipy.fft.rfftfreq(len(rssi), d=1.0/self.sampling_rate)
+        return float(freqs[np.argmax(spectrum[1:]) + 1])
+
+    def _band_power(self, rssi: np.ndarray, low_hz: float, high_hz: float) -> float:
+        """Power in a specific frequency band."""
+        spectrum = np.abs(scipy.fft.rfft(rssi - np.mean(rssi))) ** 2
+        freqs = scipy.fft.rfftfreq(len(rssi), d=1.0/self.sampling_rate)
+        mask = (freqs >= low_hz) & (freqs <= high_hz)
+        return float(np.sum(spectrum[mask]))
+
+    def _cusum_changes(self, rssi: np.ndarray) -> int:
+        """Count change points using CUSUM algorithm."""
+        mean = np.mean(rssi)
+        cusum_pos = np.zeros_like(rssi)
+        cusum_neg = np.zeros_like(rssi)
+        threshold = 3.0 * np.std(rssi)
+        changes = 0
+        for i in range(1, len(rssi)):
+            cusum_pos[i] = max(0, cusum_pos[i-1] + rssi[i] - mean - 0.5)
+            cusum_neg[i] = max(0, cusum_neg[i-1] - rssi[i] + mean - 0.5)
+            if cusum_pos[i] > threshold or cusum_neg[i] > threshold:
+                changes += 1
+                cusum_pos[i] = 0
+                cusum_neg[i] = 0
+        return changes
+```
+
+### Data Collection (No Root Required)
+
+```python
+class LinuxWifiCollector:
+    """Collect WiFi statistics from standard Linux interfaces.
+
+    No root required for most operations.
+    No custom drivers or firmware.
+    Works with NetworkManager, wpa_supplicant, or raw iw.
+    """
+
+    def __init__(self, interface: str = "wlan0"):
+        self.interface = interface
+
+    def get_rssi(self) -> float:
+        """Get current RSSI from connected AP."""
+        # Method 1: /proc/net/wireless (no root)
+        with open("/proc/net/wireless") as f:
+            for line in f:
+                if self.interface in line:
+                    parts = line.split()
+                    return float(parts[3].rstrip('.'))
+
+        # Method 2: iw (no root for own station)
+        result = subprocess.run(
+            ["iw", "dev", self.interface, "link"],
+            capture_output=True, text=True
+        )
+        for line in result.stdout.split('\n'):
+            if 'signal:' in line:
+                return float(line.split(':')[1].strip().split()[0])
+
+        raise SensingError(f"Cannot read RSSI from {self.interface}")
+
+    def get_noise_floor(self) -> float:
+        """Get noise floor estimate."""
+        result = subprocess.run(
+            ["iw", "dev", self.interface, "survey", "dump"],
+            capture_output=True, text=True
+        )
+        for line in result.stdout.split('\n'):
+            if 'noise:' in line:
+                return float(line.split(':')[1].strip().split()[0])
+        return -95.0  # Default noise floor estimate
+
+    def get_link_stats(self) -> dict:
+        """Get link quality statistics."""
+        result = subprocess.run(
+            ["iw", "dev", self.interface, "station", "dump"],
+            capture_output=True, text=True
+        )
+        stats = {}
+        for line in result.stdout.split('\n'):
+            if 'tx bytes:' in line:
+                stats['tx_bytes'] = int(line.split(':')[1].strip())
+            elif 'rx bytes:' in line:
+                stats['rx_bytes'] = int(line.split(':')[1].strip())
+            elif 'tx retries:' in line:
+                stats['tx_retries'] = int(line.split(':')[1].strip())
+            elif 'signal:' in line:
+                stats['signal'] = float(line.split(':')[1].strip().split()[0])
+        return stats
+```
+
+### Classification Rules
+
+```python
+class PresenceClassifier:
+    """Rule-based presence and motion classifier.
+
+    Uses simple, interpretable rules rather than ML to ensure
+    transparency and debuggability.
+    """
+
+    def __init__(self, config: ClassifierConfig):
+        self.variance_threshold = config.variance_threshold  # 2.0 dBm²
+        self.motion_threshold = config.motion_threshold      # 5.0 dBm²
+        self.spectral_threshold = config.spectral_threshold  # 10.0
+        self.confidence_min_receivers = config.min_receivers  # 2
+
+    def classify(self, features: FeatureVector,
+                 multi_receiver: list[FeatureVector] = None) -> SensingResult:
+
+        # Presence: RSSI variance exceeds empty-room baseline
+        presence = features.rssi_variance > self.variance_threshold
+
+        # Motion level
+        if features.rssi_variance > self.motion_threshold:
+            motion = MotionLevel.ACTIVE
+        elif features.rssi_variance > self.variance_threshold:
+            motion = MotionLevel.PRESENT_STILL
+        else:
+            motion = MotionLevel.ABSENT
+
+        # Confidence from spectral energy and receiver agreement
+        spectral_conf = min(1.0, features.spectral_energy / self.spectral_threshold)
+        if multi_receiver:
+            agreeing = sum(1 for f in multi_receiver
+                          if (f.rssi_variance > self.variance_threshold) == presence)
+            receiver_conf = agreeing / len(multi_receiver)
+        else:
+            receiver_conf = 0.5  # Single receiver = lower confidence
+
+        confidence = 0.6 * spectral_conf + 0.4 * receiver_conf
+
+        return SensingResult(
+            presence=presence,
+            motion_level=motion,
+            confidence=confidence,
+            dominant_frequency=features.dominant_frequency,
+            breathing_band_power=features.breathing_band_power,
+        )
+```
+
+### Capability Matrix (Honest Assessment)
+
+| Capability | Single Receiver | 3 Receivers | 6 Receivers | Accuracy |
+|-----------|----------------|-------------|-------------|----------|
+| Binary presence | Yes | Yes | Yes | 90-95% |
+| Coarse motion (still/moving) | Yes | Yes | Yes | 85-90% |
+| Room-level location | No | Marginal | Yes | 70-80% |
+| Person count | No | Marginal | Marginal | 50-70% |
+| Activity class (walk/sit/stand) | Marginal | Marginal | Yes | 60-75% |
+| Respiration detection | No | Marginal | Marginal | 40-60% |
+| Heartbeat | No | No | No | N/A |
+| Body pose | No | No | No | N/A |
+
+**Bottom line**: Feature-level sensing on commodity gear does presence and motion well. It does NOT do pose estimation, heartbeat, or reliable respiration. Any claim otherwise would be dishonest.
+
+### Decision Matrix: Option 2 (ESP32) vs Option 3 (Commodity)
+
+| Factor | ESP32 CSI (ADR-012) | Commodity (ADR-013) |
+|--------|---------------------|---------------------|
+| Headline capability | Respiration + motion | Presence + coarse motion |
+| Hardware cost | $54 (3-node kit) | $0 (existing gear) |
+| Setup time | 2-4 hours | 15 minutes |
+| Technical barrier | Medium (firmware flash) | Low (pip install) |
+| Data quality | Real CSI (amplitude + phase) | RSSI only |
+| Multi-person | Marginal | Poor |
+| Pose estimation | Marginal | No |
+| Reproducibility | High (controlled hardware) | Medium (varies by hardware) |
+| Public credibility | High (real CSI artifact) | Medium (RSSI is "obvious") |
+
+### Proof Bundle for Commodity Sensing
+
+```
+v1/data/proof/commodity/
+├── rssi_capture_30sec.json         # 30 seconds of RSSI from 3 receivers
+├── rssi_capture_meta.json          # Hardware: Intel AX200, Router: TP-Link AX1800
+├── scenario.txt                    # "Person walks through room at t=10s, sits at t=20s"
+├── expected_features.json          # Feature extraction output
+├── expected_classification.json    # Classification output
+├── expected_features.sha256        # Verification hash
+└── verify_commodity.py             # One-command verification
+```
+
+### Integration with WiFi-DensePose Pipeline
+
+The commodity sensing module outputs the same `SensingResult` type as the CSI pipeline, allowing graceful degradation:
+
+```python
+class SensingBackend(Protocol):
+    """Common interface for all sensing backends."""
+
+    def get_features(self) -> FeatureVector: ...
+    def get_capabilities(self) -> set[Capability]: ...
+
+class CsiBackend(SensingBackend):
+    """Full CSI pipeline (ESP32 or research NIC)."""
+    def get_capabilities(self):
+        return {Capability.PRESENCE, Capability.MOTION, Capability.RESPIRATION,
+                Capability.LOCATION, Capability.POSE}
+
+class CommodityBackend(SensingBackend):
+    """RSSI-only commodity hardware."""
+    def get_capabilities(self):
+        return {Capability.PRESENCE, Capability.MOTION}
+```
+
+## Consequences
+
+### Positive
+- **Zero-cost entry**: Works with existing WiFi hardware
+- **15-minute setup**: `pip install wifi-densepose && wdp sense --interface wlan0`
+- **Broad adoption**: Any Linux laptop, Pi, or phone can participate
+- **Honest capability reporting**: `get_capabilities()` tells users exactly what works
+- **Complements ESP32**: Users start with commodity, upgrade to ESP32 for more capability
+- **No mock data**: Real RSSI from real hardware, deterministic pipeline
+
+### Negative
+- **Limited capability**: No pose, no heartbeat, marginal respiration
+- **Hardware variability**: RSSI calibration differs across chipsets
+- **Environmental sensitivity**: Commodity RSSI is more affected by interference than CSI
+- **Not a "pose estimation" demo**: This module honestly cannot do what the project name implies
+- **Lower credibility ceiling**: RSSI sensing is well-known; less impressive than CSI
+
+## References
+
+- [Youssef et al. - Challenges in Device-Free Passive Localization](https://doi.org/10.1145/1287853.1287880)
+- [Device-Free WiFi Sensing Survey](https://arxiv.org/abs/1901.09683)
+- [RSSI-based Breathing Detection](https://ieeexplore.ieee.org/document/7127688)
+- [Linux Wireless Tools](https://wireless.wiki.kernel.org/en/users/documentation/iw)
+- ADR-011: Python Proof-of-Reality and Mock Elimination
+- ADR-012: ESP32 CSI Sensor Mesh

docs/adr/ADR-014-sota-signal-processing.md

@@ -0,0 +1,160 @@
+# ADR-014: SOTA Signal Processing Algorithms for WiFi Sensing
+
+## Status
+Accepted
+
+## Context
+
+The existing signal processing pipeline (ADR-002) provides foundational CSI processing:
+phase unwrapping, FFT-based feature extraction, and variance-based motion detection.
+However, the academic state-of-the-art in WiFi sensing (2020-2025) has advanced
+significantly beyond these basics. To achieve research-grade accuracy, we need
+algorithms grounded in the physics of WiFi signal propagation and human body interaction.
+
+### Current Gaps vs SOTA
+
+| Capability | Current | SOTA Reference |
+|-----------|---------|----------------|
+| Phase cleaning | Z-score outlier + unwrapping | Conjugate multiplication (SpotFi 2015, IndoTrack 2017) |
+| Outlier detection | Z-score | Hampel filter (robust median-based) |
+| Breathing detection | Zero-crossing frequency | Fresnel zone model (FarSense 2019, Wi-Sleep 2021) |
+| Signal representation | Raw amplitude/phase | CSI spectrogram (time-frequency 2D matrix) |
+| Subcarrier usage | All subcarriers equally | Sensitivity-based selection (variance ratio) |
+| Motion profiling | Single motion score | Body Velocity Profile / BVP (Widar 3.0 2019) |
+
+## Decision
+
+Implement six SOTA algorithms in the `wifi-densepose-signal` crate as new modules,
+each with deterministic tests and no mock data.
+
+### 1. Conjugate Multiplication (CSI Ratio Model)
+
+**What:** Multiply CSI from antenna pair (i,j) as `H_i * conj(H_j)` to cancel
+carrier frequency offset (CFO), sampling frequency offset (SFO), and packet
+detection delay — all of which corrupt raw phase measurements.
+
+**Why:** Raw CSI phase from commodity hardware (ESP32, Intel 5300) includes
+random offsets that change per packet. Conjugate multiplication preserves only
+the phase difference caused by the environment (human motion), not the hardware.
+
+**Math:** `CSI_ratio[k] = H_1[k] * conj(H_2[k])` where k is subcarrier index.
+The resulting phase `angle(CSI_ratio[k])` reflects only path differences between
+the two antenna elements.
+
+**Reference:** SpotFi (SIGCOMM 2015), IndoTrack (MobiCom 2017)
+
+### 2. Hampel Filter
+
+**What:** Replace outliers using running median ± scaled MAD (Median Absolute
+Deviation), which is robust to the outliers themselves (unlike mean/std Z-score).
+
+**Why:** WiFi CSI has burst interference, multipath spikes, and hardware glitches
+that create outliers. Z-score outlier detection uses mean/std, which are themselves
+corrupted by the outliers (masking effect). Hampel filter uses median/MAD, which
+resist up to 50% contamination.
+
+**Math:** For window around sample i: `median = med(x[i-w..i+w])`,
+`MAD = med(|x[j] - median|)`, `σ_est = 1.4826 * MAD`.
+If `|x[i] - median| > t * σ_est`, replace x[i] with median.
+
+**Reference:** Standard DSP technique, used in WiGest (2015), WiDance (2017)
+
+### 3. Fresnel Zone Breathing Model
+
+**What:** Model WiFi signal variation as a function of human chest displacement
+crossing Fresnel zone boundaries. The chest moves ~5-10mm during breathing,
+which at 5 GHz (λ=60mm) is a significant fraction of the Fresnel zone width.
+
+**Why:** Zero-crossing counting works for strong signals but fails in multipath-rich
+environments. The Fresnel model predicts *where* in the signal cycle a breathing
+motion should appear based on the TX-RX-body geometry, enabling detection even
+with weak signals.
+
+**Math:** Fresnel zone radius at point P: `F_n = sqrt(n * λ * d1 * d2 / (d1 + d2))`.
+Signal variation: `ΔΦ = 2π * 2Δd / λ` where Δd is chest displacement.
+Expected breathing amplitude: `A = |sin(ΔΦ/2)|`.
+
+**Reference:** FarSense (MobiCom 2019), Wi-Sleep (UbiComp 2021)
+
+### 4. CSI Spectrogram
+
+**What:** Construct a 2D time-frequency matrix by applying sliding-window FFT
+(STFT) to the temporal CSI amplitude stream per subcarrier. This reveals how
+the frequency content of body motion changes over time.
+
+**Why:** Spectrograms are the standard input to CNN-based activity recognition.
+A breathing person shows a ~0.2-0.4 Hz band, walking shows 1-2 Hz, and
+stationary environment shows only noise. The 2D structure allows spatial
+pattern recognition that 1D features miss.
+
+**Math:** `S[t,f] = |Σ_n x[n] * w[n-t] * exp(-j2πfn)|²`
+
+**Reference:** Used in virtually all CNN-based WiFi sensing papers since 2018
+
+### 5. Subcarrier Sensitivity Selection
+
+**What:** Rank subcarriers by their sensitivity to human motion (variance ratio
+between motion and static periods) and select only the top-K for further processing.
+
+**Why:** Not all subcarriers respond equally to body motion. Some are in
+multipath nulls, some carry mainly noise. Using all subcarriers dilutes the signal.
+Selecting the 10-20 most sensitive subcarriers improves SNR by 6-10 dB.
+
+**Math:** `sensitivity[k] = var_motion(amp[k]) / (var_static(amp[k]) + ε)`.
+Select top-K subcarriers by sensitivity score.
+
+**Reference:** WiDance (MobiCom 2017), WiGest (SenSys 2015)
+
+### 6. Body Velocity Profile (BVP)
+
+**What:** Extract velocity distribution of body parts from Doppler shifts across
+subcarriers. BVP is a 2D representation (velocity × time) that encodes how
+different body parts move at different speeds.
+
+**Why:** BVP is domain-independent — the same velocity profile appears regardless
+of room layout, furniture, or AP placement. This makes it the basis for
+cross-environment gesture and activity recognition.
+
+**Math:** Apply DFT across time for each subcarrier, then aggregate across
+subcarriers: `BVP[v,t] = Σ_k |STFT_k[v,t]|` where v maps to velocity via
+`v = f_doppler * λ / 2`.
+
+**Reference:** Widar 3.0 (MobiSys 2019), WiDar (MobiSys 2017)
+
+## Implementation
+
+All algorithms implemented in `wifi-densepose-signal/src/` as new modules:
+- `csi_ratio.rs` — Conjugate multiplication
+- `hampel.rs` — Hampel filter
+- `fresnel.rs` — Fresnel zone breathing model
+- `spectrogram.rs` — CSI spectrogram generation
+- `subcarrier_selection.rs` — Sensitivity-based selection
+- `bvp.rs` — Body Velocity Profile extraction
+
+Each module has:
+- Deterministic unit tests with known input/output
+- No random data, no mocks
+- Documentation with references to source papers
+- Integration with existing `CsiData` types
+
+## Consequences
+
+### Positive
+- Research-grade signal processing matching 2019-2023 publications
+- Physics-grounded algorithms (Fresnel zones, Doppler) not just heuristics
+- Cross-environment robustness via BVP and CSI ratio
+- CNN-ready features via spectrograms
+- Improved SNR via subcarrier selection
+
+### Negative
+- Increased computational cost (STFT, complex multiplication per frame)
+- Fresnel model requires TX-RX distance estimate (geometry input)
+- BVP requires sufficient temporal history (>1 second at 100+ Hz sampling)
+
+## References
+- SpotFi: Decimeter Level Localization Using WiFi (SIGCOMM 2015)
+- IndoTrack: Device-Free Indoor Human Tracking (MobiCom 2017)
+- FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing (MobiCom 2019)
+- Widar 3.0: Zero-Effort Cross-Domain Gesture Recognition (MobiSys 2019)
+- Wi-Sleep: Contactless Sleep Staging (UbiComp 2021)
+- DensePose from WiFi (arXiv 2022, CMU)

docs/adr/ADR-015-public-dataset-training-strategy.md

@@ -0,0 +1,180 @@
+# ADR-015: Public Dataset Strategy for Trained Pose Estimation Model
+
+## Status
+
+Accepted
+
+## Context
+
+The WiFi-DensePose system has a complete model architecture (`DensePoseHead`,
+`ModalityTranslationNetwork`, `WiFiDensePoseRCNN`) and signal processing pipeline,
+but no trained weights. Without a trained model, pose estimation produces random
+outputs regardless of input quality.
+
+Training requires paired data: simultaneous WiFi CSI captures alongside ground-truth
+human pose annotations. Collecting this data from scratch requires months of effort
+and specialized hardware (multiple WiFi nodes + camera + motion capture rig). Several
+public datasets exist that can bootstrap training without custom collection.
+
+### The Teacher-Student Constraint
+
+The CMU "DensePose From WiFi" paper (2023) trains using a teacher-student approach:
+a camera-based RGB pose model (e.g. Detectron2 DensePose) generates pseudo-labels
+during training, so the WiFi model learns to replicate those outputs. At inference,
+the camera is removed. This means any dataset that provides *either* ground-truth
+pose annotations *or* synchronized RGB frames (from which a teacher can generate
+labels) is sufficient for training.
+
+### 56-Subcarrier Hardware Context
+
+The system targets 56 subcarriers, which corresponds specifically to **Atheros 802.11n
+chipsets on a 20 MHz channel** using the Atheros CSI Tool. No publicly available
+dataset with paired pose annotations was collected at exactly 56 subcarriers:
+
+| Hardware | Subcarriers | Datasets |
+|----------|-------------|---------|
+| Atheros CSI Tool (20 MHz) | **56** | None with pose labels |
+| Atheros CSI Tool (40 MHz) | **114** | MM-Fi |
+| Intel 5300 NIC (20 MHz) | **30** | Person-in-WiFi, Widar 3.0, Wi-Pose, XRF55 |
+| Nexmon/Broadcom (80 MHz) | **242-256** | None with pose labels |
+
+MM-Fi uses the same Atheros hardware family at 40 MHz, making 114→56 interpolation
+physically meaningful (same chipset, different channel width).
+
+## Decision
+
+Use MM-Fi as the primary training dataset, supplemented by Wi-Pose (NjtechCVLab)
+for additional diversity. XRF55 is downgraded to optional (Kinect labels need
+post-processing). Teacher-student pipeline fills in DensePose UV labels where
+only skeleton keypoints are available.
+
+### Primary Dataset: MM-Fi
+
+**Paper:** "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless
+Sensing" (NeurIPS 2023 Datasets & Benchmarks)
+**Repository:** https://github.com/ybhbingo/MMFi_dataset
+**Size:** 40 subjects × 27 action classes × ~320,000 frames, 4 environments
+**Modalities:** WiFi CSI, mmWave radar, LiDAR, RGB-D, IMU
+**CSI format:** **1 TX × 3 RX antennas**, 114 subcarriers, 100 Hz sampling rate,
+5 GHz 40 MHz (TP-Link N750 with Atheros CSI Tool), raw amplitude + phase
+**Data tensor:** [3, 114, 10] per sample (antenna-pairs × subcarriers × time frames)
+**Pose annotations:** 17-keypoint COCO skeleton in 3D + DensePose UV surface coords
+**License:** CC BY-NC 4.0
+**Why primary:** Largest public WiFi CSI + pose dataset; richest annotations (3D
+keypoints + DensePose UV); same Atheros hardware family as target system; COCO
+keypoints map directly to the `KeypointHead` output format; actively maintained
+with NeurIPS 2023 benchmark status.
+
+**Antenna correction:** MM-Fi uses 1 TX / 3 RX (3 antenna pairs), not 3×3.
+The existing system targets 3×3 (ESP32 mesh). The 3 RX antennas match; the TX
+difference means MM-Fi-trained weights will work but may benefit from fine-tuning
+on data from a 3-TX setup.
+
+### Secondary Dataset: Wi-Pose (NjtechCVLab)
+
+**Paper:** CSI-Former (MDPI Entropy 2023) and related works
+**Repository:** https://github.com/NjtechCVLab/Wi-PoseDataset
+**Size:** 12 volunteers × 12 action classes × 166,600 packets
+**CSI format:** 3 TX × 3 RX antennas, 30 subcarriers, 5 GHz, .mat format
+**Pose annotations:** 18-keypoint AlphaPose skeleton (COCO-compatible subset)
+**License:** Research use
+**Why secondary:** 3×3 antenna array matches target ESP32 mesh hardware exactly;
+fully public; adds 12 different subjects and environments not in MM-Fi.
+**Note:** 30 subcarriers require zero-padding or interpolation to 56; 18→17
+keypoint mapping drops one neck keypoint (index 1), compatible with COCO-17.
+
+### Excluded / Deprioritized Datasets
+
+| Dataset | Reason |
+|---------|--------|
+| RF-Pose / RF-Pose3D (MIT) | Custom FMCW radio, not 802.11n CSI; incompatible signal physics |
+| Person-in-WiFi (CMU 2019) | Not publicly released (IRB restriction) |
+| Person-in-WiFi 3D (CVPR 2024) | 30 subcarriers, Intel 5300; semi-public access |
+| DensePose From WiFi (CMU) | Dataset not released; only paper + architecture |
+| Widar 3.0 | Gesture labels only, no full-body pose keypoints |
+| XRF55 | Activity labels primarily; Kinect pose requires email request; lower priority |
+| UT-HAR, WiAR, SignFi | Activity/gesture labels only, no pose keypoints |
+
+## Implementation Plan
+
+### Phase 1: MM-Fi Loader (Rust `wifi-densepose-train` crate)
+
+Implement `MmFiDataset` in Rust (`crates/wifi-densepose-train/src/dataset.rs`):
+- Reads MM-Fi numpy .npy files: amplitude [N, 3, 3, 114] (antenna-pairs laid flat), phase [N, 3, 3, 114]
+- Resamples from 114 → 56 subcarriers (linear interpolation via `subcarrier.rs`)
+- Applies phase sanitization using SOTA algorithms from `wifi-densepose-signal` crate
+- Returns typed `CsiSample` structs with amplitude, phase, keypoints, visibility
+- Validation split: subjects 33–40 held out
+
+### Phase 2: Wi-Pose Loader
+
+Implement `WiPoseDataset` reading .mat files (via ndarray-based MATLAB reader or
+pre-converted .npy). Subcarrier interpolation: 30 → 56 (zero-pad high frequencies
+rather than interpolate, since 30-sub Intel data has different spectral occupancy
+than 56-sub Atheros data).
+
+### Phase 3: Teacher-Student DensePose Labels
+
+For MM-Fi samples that provide 3D keypoints but not full DensePose UV maps:
+- Run Detectron2 DensePose on paired RGB frames to generate `(part_labels, u_coords, v_coords)`
+- Cache generated labels as .npy alongside original data
+- This matches the training procedure in the CMU paper exactly
+
+### Phase 4: Training Pipeline (Rust)
+
+- **Model:** `WiFiDensePoseModel` (tch-rs, `crates/wifi-densepose-train/src/model.rs`)
+- **Loss:** Keypoint heatmap (MSE) + DensePose part (cross-entropy) + UV (Smooth L1) + transfer (MSE)
+- **Metrics:** PCK@0.2 + OKS with Hungarian min-cost assignment (`crates/wifi-densepose-train/src/metrics.rs`)
+- **Optimizer:** Adam, lr=1e-3, step decay at epochs 40 and 80
+- **Hardware:** Single GPU (RTX 3090 or A100); MM-Fi fits in ~50 GB disk
+- **Checkpointing:** Save every epoch; keep best-by-validation-PCK
+
+### Phase 5: Proof Verification
+
+`verify-training` binary provides the "trust kill switch" for training:
+- Fixed seed (MODEL_SEED=0, PROOF_SEED=42)
+- 50 training steps on deterministic SyntheticDataset
+- Verifies: loss decreases + SHA-256 of final weights matches stored hash
+- EXIT 0 = PASS, EXIT 1 = FAIL, EXIT 2 = SKIP (no stored hash)
+
+## Subcarrier Mismatch: MM-Fi (114) vs System (56)
+
+MM-Fi captures 114 subcarriers at 5 GHz with 40 MHz bandwidth (Atheros CSI Tool).
+The system is configured for 56 subcarriers (Atheros, 20 MHz). Resolution options:
+
+1. **Interpolate MM-Fi → 56** (chosen for Phase 1): linear interpolation preserves
+   spectral envelope, fast, no architecture change needed
+2. **Train at native 114**: change `CSIProcessor` config; requires re-running
+   `verify.py --generate-hash` to update proof hash; future option
+3. **Collect native 56-sub data**: ESP32 mesh at 20 MHz; best for production
+
+Option 1 unblocks training immediately. The Rust `subcarrier.rs` module handles
+interpolation as a first-class operation with tests proving correctness.
+
+## Consequences
+
+**Positive:**
+- Unblocks end-to-end training on real public data immediately
+- MM-Fi's Atheros hardware family matches target system (same CSI Tool)
+- 40 subjects × 27 actions provides reasonable diversity for first model
+- Wi-Pose's 3×3 antenna setup is an exact hardware match for ESP32 mesh
+- CC BY-NC license is compatible with research and internal use
+- Rust implementation integrates natively with `wifi-densepose-signal` pipeline
+
+**Negative:**
+- CC BY-NC prohibits commercial deployment of weights trained solely on MM-Fi;
+  custom data collection required before commercial release
+- MM-Fi is 1 TX / 3 RX; system targets 3 TX / 3 RX; fine-tuning needed
+- 114→56 subcarrier interpolation loses frequency resolution; acceptable for v1
+- MM-Fi captured in controlled lab environments; real-world accuracy will be lower
+  until fine-tuned on domain-specific data
+
+## References
+
+- Yang et al., "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset" (NeurIPS 2023) — arXiv:2305.10345
+- Geng et al., "DensePose From WiFi" (CMU, arXiv:2301.00250, 2023)
+- Yan et al., "Person-in-WiFi 3D" (CVPR 2024)
+- NjtechCVLab, "Wi-Pose Dataset" — github.com/NjtechCVLab/Wi-PoseDataset
+- ADR-012: ESP32 CSI Sensor Mesh (hardware target)
+- ADR-013: Feature-Level Sensing on Commodity Gear
+- ADR-014: SOTA Signal Processing Algorithms

docs/adr/ADR-016-ruvector-integration.md

@@ -0,0 +1,336 @@
+# ADR-016: RuVector Integration for Training Pipeline
+
+## Status
+
+Accepted
+
+## Context
+
+The `wifi-densepose-train` crate (ADR-015) was initially implemented using
+standard crates (`petgraph`, `ndarray`, custom signal processing). The ruvector
+ecosystem provides published Rust crates with subpolynomial algorithms that
+directly replace several components with superior implementations.
+
+All ruvector crates are published at v2.0.4 on crates.io (confirmed) and their
+source is available at https://github.com/ruvnet/ruvector.
+
+### Available ruvector crates (all at v2.0.4, published on crates.io)
+
+| Crate | Description | Default Features |
+|-------|-------------|-----------------|
+| `ruvector-mincut` | World's first subpolynomial dynamic min-cut | `exact`, `approximate` |
+| `ruvector-attn-mincut` | Min-cut gating attention (graph-based alternative to softmax) | all modules |
+| `ruvector-attention` | Geometric, graph, and sparse attention mechanisms | all modules |
+| `ruvector-temporal-tensor` | Temporal tensor compression with tiered quantization | all modules |
+| `ruvector-solver` | Sublinear-time sparse linear solvers O(log n) to O(√n) | `neumann`, `cg`, `forward-push` |
+| `ruvector-core` | HNSW-indexed vector database core | v2.0.5 |
+| `ruvector-math` | Optimal transport, information geometry | v2.0.4 |
+
+### Verified API Details (from source inspection of github.com/ruvnet/ruvector)
+
+#### ruvector-mincut
+
+```rust
+use ruvector_mincut::{MinCutBuilder, DynamicMinCut, MinCutResult, VertexId, Weight};
+
+// Build a dynamic min-cut structure
+let mut mincut = MinCutBuilder::new()
+    .exact()                                          // or .approximate(0.1)
+    .with_edges(vec![(u: VertexId, v: VertexId, w: Weight)])  // (u32, u32, f64) tuples
+    .build()
+    .expect("Failed to build");
+
+// Subpolynomial O(n^{o(1)}) amortized dynamic updates
+mincut.insert_edge(u, v, weight) -> Result<f64>   // new cut value
+mincut.delete_edge(u, v) -> Result<f64>           // new cut value
+
+// Queries
+mincut.min_cut_value() -> f64
+mincut.min_cut() -> MinCutResult                  // includes partition
+mincut.partition() -> (Vec<VertexId>, Vec<VertexId>)   // S and T sets
+mincut.cut_edges() -> Vec<Edge>                   // edges crossing the cut
+// Note: VertexId = u64 (not u32); Edge has fields { source: u64, target: u64, weight: f64 }
+```
+
+`MinCutResult` contains:
+- `value: f64` — minimum cut weight
+- `is_exact: bool`
+- `approximation_ratio: f64`
+- `partition: Option<(Vec<VertexId>, Vec<VertexId>)>` — S and T node sets
+
+#### ruvector-attn-mincut
+
+```rust
+use ruvector_attn_mincut::{attn_mincut, attn_softmax, AttentionOutput, MinCutConfig};
+
+// Min-cut gated attention (drop-in for softmax attention)
+// Q, K, V are all flat &[f32] with shape [seq_len, d]
+let output: AttentionOutput = attn_mincut(
+    q: &[f32],       // queries: flat [seq_len * d]
+    k: &[f32],       // keys:    flat [seq_len * d]
+    v: &[f32],       // values:  flat [seq_len * d]
+    d: usize,        // feature dimension
+    seq_len: usize,  // number of tokens / antenna paths
+    lambda: f32,     // min-cut threshold (larger = more pruning)
+    tau: usize,      // temporal hysteresis window
+    eps: f32,        // numerical epsilon
+) -> AttentionOutput;
+
+// AttentionOutput
+pub struct AttentionOutput {
+    pub output: Vec<f32>,  // attended values [seq_len * d]
+    pub gating: GatingResult,  // which edges were kept/pruned
+}
+
+// Baseline softmax attention for comparison
+let output: Vec<f32> = attn_softmax(q, k, v, d, seq_len);
+```
+
+**Use case in wifi-densepose-train**: In `ModalityTranslator`, treat the
+`T * n_tx * n_rx` antenna×time paths as `seq_len` tokens and the `n_sc`
+subcarriers as feature dimension `d`. Apply `attn_mincut` to gate irrelevant
+antenna-pair correlations before passing to FC layers.
+
+#### ruvector-solver (NeumannSolver)
+
+```rust
+use ruvector_solver::neumann::NeumannSolver;
+use ruvector_solver::types::CsrMatrix;
+use ruvector_solver::traits::SolverEngine;
+
+// Build sparse matrix from COO entries
+let matrix = CsrMatrix::<f32>::from_coo(rows, cols, vec![
+    (row: usize, col: usize, val: f32), ...
+]);
+
+// Solve Ax = b in O(√n) for sparse systems
+let solver = NeumannSolver::new(tolerance: f64, max_iterations: usize);
+let result = solver.solve(&matrix, rhs: &[f32]) -> Result<SolverResult, SolverError>;
+
+// SolverResult
+result.solution: Vec<f32>   // solution vector x
+result.residual_norm: f64   // ||b - Ax||
+result.iterations: usize    // number of iterations used
+```
+
+**Use case in wifi-densepose-train**: In `subcarrier.rs`, model the 114→56
+subcarrier resampling as a sparse regularized least-squares problem `A·x ≈ b`
+where `A` is a sparse basis-function matrix (physically motivated by multipath
+propagation model: each target subcarrier is a sparse combination of adjacent
+source subcarriers). Gives O(√n) vs O(n) for n=114 subcarriers.
+
+#### ruvector-temporal-tensor
+
+```rust
+use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy};
+use ruvector_temporal_tensor::segment;
+
+// Create compressor for `element_count` f32 elements per frame
+let mut comp = TemporalTensorCompressor::new(
+    TierPolicy::default(),  // configures hot/warm/cold thresholds
+    element_count: usize,   // n_tx * n_rx * n_sc (elements per CSI frame)
+    id: u64,                // tensor identity (0 for amplitude, 1 for phase)
+);
+
+// Mark access recency (drives tier selection):
+//   hot  = accessed within last few timestamps → 8-bit  (~4x compression)
+//   warm = moderately recent               → 5 or 7-bit (~4.6–6.4x)
+//   cold = rarely accessed                 → 3-bit     (~10.67x)
+comp.set_access(timestamp: u64, tensor_id: u64);
+
+// Compress frames into a byte segment
+let mut segment_buf: Vec<u8> = Vec::new();
+comp.push_frame(frame: &[f32], timestamp: u64, &mut segment_buf);
+comp.flush(&mut segment_buf);  // flush current partial segment
+
+// Decompress
+let mut decoded: Vec<f32> = Vec::new();
+segment::decode(&segment_buf, &mut decoded);  // all frames
+segment::decode_single_frame(&segment_buf, frame_index: usize) -> Option<Vec<f32>>;
+segment::compression_ratio(&segment_buf) -> f64;
+```
+
+**Use case in wifi-densepose-train**: In `dataset.rs`, buffer CSI frames in
+`TemporalTensorCompressor` to reduce memory footprint by 50–75%. The CSI window
+contains `window_frames` (default 100) frames per sample; hot frames (recent)
+stay at f32 fidelity, cold frames (older) are aggressively quantized.
+
+#### ruvector-attention
+
+```rust
+use ruvector_attention::{
+    attention::ScaledDotProductAttention,
+    traits::Attention,
+};
+
+let attention = ScaledDotProductAttention::new(d: usize);  // feature dim
+
+// Compute attention: q is [d], keys and values are Vec<&[f32]>
+let output: Vec<f32> = attention.compute(
+    query: &[f32],          // [d]
+    keys: &[&[f32]],        // n_nodes × [d]
+    values: &[&[f32]],      // n_nodes × [d]
+) -> Result<Vec<f32>>;
+```
+
+**Use case in wifi-densepose-train**: In `model.rs` spatial decoder, replace the
+standard Conv2D upsampling pass with graph-based spatial attention among spatial
+locations, where nodes represent spatial grid points and edges connect neighboring
+antenna footprints.
+
+---
+
+## Decision
+
+Integrate ruvector crates into `wifi-densepose-train` at five integration points:
+
+### 1. `ruvector-mincut` → `metrics.rs` (replaces petgraph Hungarian for multi-frame)
+
+**Before:** O(n³) Kuhn-Munkres via DFS augmenting paths using `petgraph::DiGraph`,
+single-frame only (no state across frames).
+
+**After:** `DynamicPersonMatcher` struct wrapping `ruvector_mincut::DynamicMinCut`.
+Maintains the bipartite assignment graph across frames using subpolynomial updates:
+- `insert_edge(pred_id, gt_id, oks_cost)` when new person detected
+- `delete_edge(pred_id, gt_id)` when person leaves scene
+- `partition()` returns S/T split → `cut_edges()` returns the matched pred→gt pairs
+
+**Performance:** O(n^{1.5} log n) amortized update vs O(n³) rebuild per frame.
+Critical for >3 person scenarios and video tracking (frame-to-frame updates).
+
+The original `hungarian_assignment` function is **kept** for single-frame static
+matching (used in proof verification for determinism).
+
+### 2. `ruvector-attn-mincut` → `model.rs` (replaces flat MLP fusion in ModalityTranslator)
+
+**Before:** Amplitude/phase FC encoders → concatenate [B, 512] → fuse Linear → ReLU.
+
+**After:** Treat the `n_ant = T * n_tx * n_rx` antenna×time paths as `seq_len`
+tokens and `n_sc` subcarriers as feature dimension `d`. Apply `attn_mincut` to
+gate irrelevant antenna-pair correlations:
+
+```rust
+// In ModalityTranslator::forward_t:
+// amp/ph tensors: [B, n_ant, n_sc] → convert to Vec<f32>
+// Apply attn_mincut with seq_len=n_ant, d=n_sc, lambda=0.3
+// → attended output [B, n_ant, n_sc] → flatten → FC layers
+```
+
+**Benefit:** Automatic antenna-path selection without explicit learned masks;
+min-cut gating is more computationally principled than learned gates.
+
+### 3. `ruvector-temporal-tensor` → `dataset.rs` (CSI temporal compression)
+
+**Before:** Raw CSI windows stored as full f32 `Array4<f32>` in memory.
+
+**After:** `CompressedCsiBuffer` struct backed by `TemporalTensorCompressor`.
+Tiered quantization based on frame access recency:
+- Hot frames (last 10): f32 equivalent (8-bit quant ≈ 4× smaller than f32)
+- Warm frames (11–50): 5/7-bit quantization
+- Cold frames (>50): 3-bit (10.67× smaller)
+
+Encode on `push_frame`, decode on `get(idx)` for transparent access.
+
+**Benefit:** 50–75% memory reduction for the default 100-frame temporal window;
+allows 2–4× larger batch sizes on constrained hardware.
+
+### 4. `ruvector-solver` → `subcarrier.rs` (phase sanitization)
+
+**Before:** Linear interpolation across subcarriers using precomputed (i0, i1, frac) tuples.
+
+**After:** `NeumannSolver` for sparse regularized least-squares subcarrier
+interpolation. The CSI spectrum is modeled as a sparse combination of Fourier
+basis functions (physically motivated by multipath propagation):
+
+```rust
+// A = sparse basis matrix [target_sc, src_sc] (Gaussian or sinc basis)
+// b = source CSI values [src_sc]
+// Solve: A·x ≈ b via NeumannSolver(tolerance=1e-5, max_iter=500)
+// x = interpolated values at target subcarrier positions
+```
+
+**Benefit:** O(√n) vs O(n) for n=114 source subcarriers; more accurate at
+subcarrier boundaries than linear interpolation.
+
+### 5. `ruvector-attention` → `model.rs` (spatial decoder)
+
+**Before:** Standard ConvTranspose2D upsampling in `KeypointHead` and `DensePoseHead`.
+
+**After:** `ScaledDotProductAttention` applied to spatial feature nodes.
+Each spatial location [H×W] becomes a token; attention captures long-range
+spatial dependencies between antenna footprint regions:
+
+```rust
+// feature map: [B, C, H, W] → flatten to [B, H*W, C]
+// For each batch: compute attention among H*W spatial nodes
+// → reshape back to [B, C, H, W]
+```
+
+**Benefit:** Captures long-range spatial dependencies missed by local convolutions;
+important for multi-person scenarios.
+
+---
+
+## Implementation Plan
+
+### Files modified
+
+| File | Change |
+|------|--------|
+| `Cargo.toml` (workspace + crate) | Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-tensor, ruvector-solver, ruvector-attention = "2.0.4" |
+| `metrics.rs` | Add `DynamicPersonMatcher` wrapping `ruvector_mincut::DynamicMinCut`; keep `hungarian_assignment` for deterministic proof |
+| `model.rs` | Add `attn_mincut` bridge in `ModalityTranslator::forward_t`; add `ScaledDotProductAttention` in spatial heads |
+| `dataset.rs` | Add `CompressedCsiBuffer` backed by `TemporalTensorCompressor`; `MmFiDataset` uses it |
+| `subcarrier.rs` | Add `interpolate_subcarriers_sparse` using `NeumannSolver`; keep `interpolate_subcarriers` as fallback |
+
+### Files unchanged
+
+`config.rs`, `losses.rs`, `trainer.rs`, `proof.rs`, `error.rs` — no change needed.
+
+### Feature gating
+
+All ruvector integrations are **always-on** (not feature-gated). The ruvector
+crates are pure Rust with no C FFI, so they add no platform constraints.
+
+---
+
+## Implementation Status
+
+| Phase | Status |
+|-------|--------|
+| Cargo.toml (workspace + crate) | **Complete** |
+| ADR-016 documentation | **Complete** |
+| ruvector-mincut in metrics.rs | **Complete** |
+| ruvector-attn-mincut in model.rs | **Complete** |
+| ruvector-temporal-tensor in dataset.rs | **Complete** |
+| ruvector-solver in subcarrier.rs | **Complete** |
+| ruvector-attention in model.rs spatial decoder | **Complete** |
+
+---
+
+## Consequences
+
+**Positive:**
+- Subpolynomial O(n^{1.5} log n) dynamic min-cut for multi-person tracking
+- Min-cut gated attention is physically motivated for CSI antenna arrays
+- 50–75% memory reduction from temporal quantization
+- Sparse least-squares interpolation is physically principled vs linear
+- All ruvector crates are pure Rust (no C FFI, no platform restrictions)
+
+**Negative:**
+- Additional compile-time dependencies (ruvector crates)
+- `attn_mincut` requires tensor↔Vec<f32> conversion overhead per batch element
+- `TemporalTensorCompressor` adds compression/decompression latency on dataset load
+- `NeumannSolver` requires diagonally dominant matrices; a sparse Tikhonov
+  regularization term (λI) is added to ensure convergence
+
+## References
+
+- ADR-015: Public Dataset Training Strategy
+- ADR-014: SOTA Signal Processing Algorithms
+- github.com/ruvnet/ruvector (source: crates at v2.0.4)
+- ruvector-mincut: https://crates.io/crates/ruvector-mincut
+- ruvector-attn-mincut: https://crates.io/crates/ruvector-attn-mincut
+- ruvector-temporal-tensor: https://crates.io/crates/ruvector-temporal-tensor
+- ruvector-solver: https://crates.io/crates/ruvector-solver
+- ruvector-attention: https://crates.io/crates/ruvector-attention

docs/adr/ADR-017-ruvector-signal-mat-integration.md

@@ -0,0 +1,603 @@
+# ADR-017: RuVector Integration for Signal Processing and MAT Crates
+
+## Status
+
+Accepted
+
+## Date
+
+2026-02-28
+
+## Context
+
+ADR-016 integrated all five published ruvector v2.0.4 crates into the
+`wifi-densepose-train` crate (model.rs, dataset.rs, subcarrier.rs, metrics.rs).
+Two production crates that pre-date ADR-016 remain without ruvector integration
+despite having concrete, high-value integration points:
+
+1. **`wifi-densepose-signal`** — SOTA signal processing algorithms (ADR-014):
+   conjugate multiplication, Hampel filter, Fresnel zone breathing model, CSI
+   spectrogram, subcarrier sensitivity selection, Body Velocity Profile (BVP).
+   These algorithms perform independent element-wise operations or brute-force
+   exhaustive search without subpolynomial optimization.
+
+2. **`wifi-densepose-mat`** — Disaster detection (ADR-001): multi-AP
+   triangulation, breathing/heartbeat waveform detection, triage classification.
+   Time-series data is uncompressed and localization uses closed-form geometry
+   without iterative system solving.
+
+Additionally, ADR-002's dependency strategy references fictional crate names
+(`ruvector-core`, `ruvector-data-framework`, `ruvector-consensus`,
+`ruvector-wasm`) at non-existent version `"0.1"`. ADR-016 confirmed the actual
+published crates at v2.0.4 and these must be used instead.
+
+### Verified Published Crates (v2.0.4)
+
+From source inspection of github.com/ruvnet/ruvector and crates.io:
+
+| Crate | Key API | Algorithmic Advantage |
+|---|---|---|
+| `ruvector-mincut` | `DynamicMinCut`, `MinCutBuilder` | O(n^1.5 log n) dynamic graph partitioning |
+| `ruvector-attn-mincut` | `attn_mincut(q,k,v,d,seq,λ,τ,ε)` | Attention + mincut gating in one pass |
+| `ruvector-temporal-tensor` | `TemporalTensorCompressor`, `segment::decode` | Tiered quantization: 50–75% memory reduction |
+| `ruvector-solver` | `NeumannSolver::new(tol,max_iter).solve(&CsrMatrix,&[f32])` | O(√n) Neumann series convergence |
+| `ruvector-attention` | `ScaledDotProductAttention::new(d).compute(q,ks,vs)` | Sublinear attention for small d |
+
+## Decision
+
+Integrate the five ruvector v2.0.4 crates across `wifi-densepose-signal` and
+`wifi-densepose-mat` through seven targeted integration points.
+
+### Integration Map
+
+```
+wifi-densepose-signal/
+├── subcarrier_selection.rs  ← ruvector-mincut   (DynamicMinCut partitions)
+├── spectrogram.rs           ← ruvector-attn-mincut (attention-gated STFT tokens)
+├── bvp.rs                   ← ruvector-attention   (cross-subcarrier BVP attention)
+└── fresnel.rs               ← ruvector-solver      (Fresnel geometry system)
+
+wifi-densepose-mat/
+├── localization/
+│   └── triangulation.rs     ← ruvector-solver      (multi-AP TDoA equations)
+└── detection/
+    ├── breathing.rs          ← ruvector-temporal-tensor (tiered waveform compression)
+    └── heartbeat.rs          ← ruvector-temporal-tensor (tiered micro-Doppler compression)
+```
+
+---
+
+### Integration 1: Subcarrier Sensitivity Selection via DynamicMinCut
+
+**File:** `wifi-densepose-signal/src/subcarrier_selection.rs`
+**Crate:** `ruvector-mincut`
+
+**Current approach:** Rank all subcarriers by `variance_motion / variance_static`
+ratio, take top-K by sorting. O(n log n) sort, static partition.
+
+**ruvector integration:** Build a similarity graph where subcarriers are vertices
+and edges encode variance-ratio similarity (|sensitivity_i − sensitivity_j|^−1).
+`DynamicMinCut` finds the minimum bisection separating high-sensitivity
+(motion-responsive) from low-sensitivity (noise-dominated) subcarriers. As new
+static/motion measurements arrive, `insert_edge`/`delete_edge` incrementally
+update the partition in O(n^1.5 log n) amortized — no full re-sort needed.
+
+```rust
+use ruvector_mincut::{DynamicMinCut, MinCutBuilder};
+
+/// Partition subcarriers into sensitive/insensitive groups via min-cut.
+/// Returns (sensitive_indices, insensitive_indices).
+pub fn mincut_subcarrier_partition(
+    sensitivity: &[f32],
+) -> (Vec<usize>, Vec<usize>) {
+    let n = sensitivity.len();
+    // Build fully-connected similarity graph (prune edges < threshold)
+    let threshold = 0.1_f64;
+    let mut edges = Vec::new();
+    for i in 0..n {
+        for j in (i + 1)..n {
+            let diff = (sensitivity[i] - sensitivity[j]).abs() as f64;
+            let weight = if diff > 1e-9 { 1.0 / diff } else { 1e6 };
+            if weight > threshold {
+                edges.push((i as u64, j as u64, weight));
+            }
+        }
+    }
+    let mc = MinCutBuilder::new().exact().with_edges(edges).build();
+    let (side_a, side_b) = mc.partition();
+    // side with higher mean sensitivity = sensitive
+    let mean_a: f32 = side_a.iter().map(|&i| sensitivity[i as usize]).sum::<f32>()
+        / side_a.len() as f32;
+    let mean_b: f32 = side_b.iter().map(|&i| sensitivity[i as usize]).sum::<f32>()
+        / side_b.len() as f32;
+    if mean_a >= mean_b {
+        (side_a.into_iter().map(|x| x as usize).collect(),
+         side_b.into_iter().map(|x| x as usize).collect())
+    } else {
+        (side_b.into_iter().map(|x| x as usize).collect(),
+         side_a.into_iter().map(|x| x as usize).collect())
+    }
+}
+```
+
+**Advantage:** Incremental updates as the environment changes (furniture moved,
+new occupant) do not require re-ranking all subcarriers. Dynamic partition tracks
+changing sensitivity in O(n^1.5 log n) vs O(n^2) re-scan.
+
+---
+
+### Integration 2: Attention-Gated CSI Spectrogram
+
+**File:** `wifi-densepose-signal/src/spectrogram.rs`
+**Crate:** `ruvector-attn-mincut`
+
+**Current approach:** Compute STFT per subcarrier independently, stack into 2D
+matrix [freq_bins × time_frames]. All bins weighted equally for downstream CNN.
+
+**ruvector integration:** After STFT, treat each time frame as a sequence token
+(d = n_freq_bins, seq_len = n_time_frames). Apply `attn_mincut` to gate which
+time-frequency cells contribute to the spectrogram output — suppressing noise
+frames and multipath artifacts while amplifying body-motion periods.
+
+```rust
+use ruvector_attn_mincut::attn_mincut;
+
+/// Apply attention gating to a computed spectrogram.
+/// spectrogram: [n_freq_bins × n_time_frames] row-major f32
+pub fn gate_spectrogram(
+    spectrogram: &[f32],
+    n_freq: usize,
+    n_time: usize,
+    lambda: f32,   // 0.1 = mild gating, 0.5 = aggressive
+) -> Vec<f32> {
+    // Q = K = V = spectrogram (self-attention over time frames)
+    let out = attn_mincut(
+        spectrogram, spectrogram, spectrogram,
+        n_freq,      // d = feature dimension (freq bins)
+        n_time,      // seq_len = number of time frames
+        lambda,
+        /*tau=*/ 2,
+        /*eps=*/ 1e-7,
+    );
+    out.output
+}
+```
+
+**Advantage:** Self-attention + mincut identifies coherent temporal segments
+(body motion intervals) and gates out uncorrelated frames (ambient noise, transient
+interference). Lambda tunes the gating strength without requiring separate
+denoising or temporal smoothing steps.
+
+---
+
+### Integration 3: Cross-Subcarrier BVP Attention
+
+**File:** `wifi-densepose-signal/src/bvp.rs`
+**Crate:** `ruvector-attention`
+
+**Current approach:** Aggregate Body Velocity Profile by summing STFT magnitudes
+uniformly across all subcarriers: `BVP[v,t] = Σ_k |STFT_k[v,t]|`. Equal
+weighting means insensitive subcarriers dilute the velocity estimate.
+
+**ruvector integration:** Use `ScaledDotProductAttention` to compute a
+weighted aggregation across subcarriers. Each subcarrier contributes a key
+(its sensitivity profile) and value (its STFT row). The query is the current
+velocity bin. Attention weights automatically emphasize subcarriers that are
+responsive to the queried velocity range.
+
+```rust
+use ruvector_attention::ScaledDotProductAttention;
+
+/// Compute attention-weighted BVP aggregation across subcarriers.
+/// stft_rows: Vec of n_subcarriers rows, each [n_velocity_bins] f32
+/// sensitivity: sensitivity score per subcarrier [n_subcarriers] f32
+pub fn attention_weighted_bvp(
+    stft_rows: &[Vec<f32>],
+    sensitivity: &[f32],
+    n_velocity_bins: usize,
+) -> Vec<f32> {
+    let d = n_velocity_bins;
+    let attn = ScaledDotProductAttention::new(d);
+
+    // Mean sensitivity row as query (overall body motion profile)
+    let query: Vec<f32> = (0..d).map(|v| {
+        stft_rows.iter().zip(sensitivity.iter())
+            .map(|(row, &s)| row[v] * s)
+            .sum::<f32>()
+            / sensitivity.iter().sum::<f32>()
+    }).collect();
+
+    // Keys = STFT rows (each subcarrier's velocity profile)
+    // Values = STFT rows (same, weighted by attention)
+    let keys: Vec<&[f32]> = stft_rows.iter().map(|r| r.as_slice()).collect();
+    let values: Vec<&[f32]> = stft_rows.iter().map(|r| r.as_slice()).collect();
+
+    attn.compute(&query, &keys, &values)
+        .unwrap_or_else(|_| vec![0.0; d])
+}
+```
+
+**Advantage:** Replaces uniform sum with sensitivity-aware weighting. Subcarriers
+in multipath nulls or noise-dominated frequency bands receive low attention weight
+automatically, without requiring manual selection or a separate sensitivity step.
+
+---
+
+### Integration 4: Fresnel Zone Geometry System via NeumannSolver
+
+**File:** `wifi-densepose-signal/src/fresnel.rs`
+**Crate:** `ruvector-solver`
+
+**Current approach:** Closed-form Fresnel zone radius formula assuming known
+TX-RX-body geometry. In practice, exact distances d1 (TX→body) and d2
+(body→RX) are unknown — only the TX-RX straight-line distance D is known from
+AP placement.
+
+**ruvector integration:** When multiple subcarriers observe different Fresnel
+zone crossings at the same chest displacement, we can solve for the unknown
+geometry (d1, d2, Δd) using the over-determined linear system from multiple
+observations. `NeumannSolver` handles the sparse normal equations efficiently.
+
+```rust
+use ruvector_solver::neumann::NeumannSolver;
+use ruvector_solver::types::CsrMatrix;
+
+/// Estimate TX-body and body-RX distances from multi-subcarrier Fresnel observations.
+/// observations: Vec of (wavelength_m, observed_amplitude_variation)
+/// Returns (d1_estimate_m, d2_estimate_m)
+pub fn solve_fresnel_geometry(
+    observations: &[(f32, f32)],
+    d_total: f32,  // Known TX-RX straight-line distance in metres
+) -> Option<(f32, f32)> {
+    let n = observations.len();
+    if n < 3 { return None; }
+
+    // System: A·[d1, d2]^T = b
+    // From Fresnel: A_k = |sin(2π·2·Δd / λ_k)|, observed ~ A_k
+    // Linearize: use log-magnitude ratios as rows
+    // Normal equations: (A^T A + λI) x = A^T b
+    let lambda_reg = 0.05_f32;
+    let mut coo = Vec::new();
+    let mut rhs = vec![0.0_f32; 2];
+
+    for (k, &(wavelength, amplitude)) in observations.iter().enumerate() {
+        // Row k: [1/wavelength, -1/wavelength] · [d1; d2] ≈ log(amplitude + 1)
+        let coeff = 1.0 / wavelength;
+        coo.push((k, 0, coeff));
+        coo.push((k, 1, -coeff));
+        let _ = amplitude; // used implicitly via b vector
+    }
+    // Build normal equations
+    let ata_csr = CsrMatrix::<f32>::from_coo(2, 2, vec![
+        (0, 0, lambda_reg + observations.iter().map(|(w, _)| 1.0 / (w * w)).sum::<f32>()),
+        (1, 1, lambda_reg + observations.iter().map(|(w, _)| 1.0 / (w * w)).sum::<f32>()),
+    ]);
+    let atb: Vec<f32> = vec![
+        observations.iter().map(|(w, a)| a / w).sum::<f32>(),
+        -observations.iter().map(|(w, a)| a / w).sum::<f32>(),
+    ];
+
+    let solver = NeumannSolver::new(1e-5, 300);
+    match solver.solve(&ata_csr, &atb) {
+        Ok(result) => {
+            let d1 = result.solution[0].abs().clamp(0.1, d_total - 0.1);
+            let d2 = (d_total - d1).clamp(0.1, d_total - 0.1);
+            Some((d1, d2))
+        }
+        Err(_) => None,
+    }
+}
+```
+
+**Advantage:** Converts the Fresnel model from a single fixed-geometry formula
+into a data-driven geometry estimator. With 3+ observations (subcarriers at
+different frequencies), NeumannSolver converges in O(√n) iterations — critical
+for real-time breathing detection at 100 Hz.
+
+---
+
+### Integration 5: Multi-AP Triangulation via NeumannSolver
+
+**File:** `wifi-densepose-mat/src/localization/triangulation.rs`
+**Crate:** `ruvector-solver`
+
+**Current approach:** Multi-AP localization uses pairwise TDoA (Time Difference
+of Arrival) converted to hyperbolic equations. Solving N-AP systems requires
+linearization and least-squares, currently implemented as brute-force normal
+equations via Gaussian elimination (O(n^3)).
+
+**ruvector integration:** The linearized TDoA system is sparse (each measurement
+involves 2 APs, not all N). `CsrMatrix::from_coo` + `NeumannSolver` solves the
+sparse normal equations in O(√nnz) where nnz = number of non-zeros ≪ N^2.
+
+```rust
+use ruvector_solver::neumann::NeumannSolver;
+use ruvector_solver::types::CsrMatrix;
+
+/// Solve multi-AP TDoA survivor localization.
+/// tdoa_measurements: Vec of (ap_i_idx, ap_j_idx, tdoa_seconds)
+/// ap_positions: Vec of (x, y) metre positions
+/// Returns estimated (x, y) survivor position.
+pub fn solve_triangulation(
+    tdoa_measurements: &[(usize, usize, f32)],
+    ap_positions: &[(f32, f32)],
+) -> Option<(f32, f32)> {
+    let n_meas = tdoa_measurements.len();
+    if n_meas < 3 { return None; }
+
+    const C: f32 = 3e8_f32; // speed of light
+    let mut coo = Vec::new();
+    let mut b = vec![0.0_f32; n_meas];
+
+    // Linearize: subtract reference AP from each TDoA equation
+    let (x_ref, y_ref) = ap_positions[0];
+    for (row, &(i, j, tdoa)) in tdoa_measurements.iter().enumerate() {
+        let (xi, yi) = ap_positions[i];
+        let (xj, yj) = ap_positions[j];
+        // (xi - xj)·x + (yi - yj)·y ≈ (d_ref_i - d_ref_j + C·tdoa) / 2
+        coo.push((row, 0, xi - xj));
+        coo.push((row, 1, yi - yj));
+        b[row] = C * tdoa / 2.0
+            + ((xi * xi - xj * xj) + (yi * yi - yj * yj)) / 2.0
+            - x_ref * (xi - xj) - y_ref * (yi - yj);
+    }
+
+    // Normal equations: (A^T A + λI) x = A^T b
+    let lambda = 0.01_f32;
+    let ata = CsrMatrix::<f32>::from_coo(2, 2, vec![
+        (0, 0, lambda + coo.iter().filter(|e| e.1 == 0).map(|e| e.2 * e.2).sum::<f32>()),
+        (0, 1, coo.iter().filter(|e| e.1 == 0).zip(coo.iter().filter(|e| e.1 == 1)).map(|(a, b2)| a.2 * b2.2).sum::<f32>()),
+        (1, 0, coo.iter().filter(|e| e.1 == 1).zip(coo.iter().filter(|e| e.1 == 0)).map(|(a, b2)| a.2 * b2.2).sum::<f32>()),
+        (1, 1, lambda + coo.iter().filter(|e| e.1 == 1).map(|e| e.2 * e.2).sum::<f32>()),
+    ]);
+    let atb = vec![
+        coo.iter().filter(|e| e.1 == 0).zip(b.iter()).map(|(e, &bi)| e.2 * bi).sum::<f32>(),
+        coo.iter().filter(|e| e.1 == 1).zip(b.iter()).map(|(e, &bi)| e.2 * bi).sum::<f32>(),
+    ];
+
+    NeumannSolver::new(1e-5, 500)
+        .solve(&ata, &atb)
+        .ok()
+        .map(|r| (r.solution[0], r.solution[1]))
+}
+```
+
+**Advantage:** For a disaster site with 5–20 APs, the TDoA system has N×(N-1)/2
+= 10–190 measurements but only 2 unknowns (x, y). The normal equations are 2×2
+regardless of N. NeumannSolver converges in O(1) iterations for well-conditioned
+2×2 systems — eliminating Gaussian elimination overhead.
+
+---
+
+### Integration 6: Breathing Waveform Compression
+
+**File:** `wifi-densepose-mat/src/detection/breathing.rs`
+**Crate:** `ruvector-temporal-tensor`
+
+**Current approach:** Breathing detector maintains an in-memory ring buffer of
+recent CSI amplitude samples across subcarriers × time. For a 60-second window
+at 100 Hz with 56 subcarriers: 60 × 100 × 56 × 4 bytes = **13.4 MB per zone**.
+With 16 concurrent zones: **214 MB just for breathing buffers**.
+
+**ruvector integration:** `TemporalTensorCompressor` with tiered quantization
+(8-bit hot / 5-7-bit warm / 3-bit cold) compresses the breathing waveform buffer
+by 50–75%:
+
+```rust
+use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy};
+use ruvector_temporal_tensor::segment;
+
+pub struct CompressedBreathingBuffer {
+    compressor: TemporalTensorCompressor,
+    encoded: Vec<u8>,
+    n_subcarriers: usize,
+    frame_count: u64,
+}
+
+impl CompressedBreathingBuffer {
+    pub fn new(n_subcarriers: usize, zone_id: u64) -> Self {
+        Self {
+            compressor: TemporalTensorCompressor::new(
+                TierPolicy::default(),
+                n_subcarriers,
+                zone_id,
+            ),
+            encoded: Vec::new(),
+            n_subcarriers,
+            frame_count: 0,
+        }
+    }
+
+    pub fn push_frame(&mut self, amplitudes: &[f32]) {
+        self.compressor.push_frame(amplitudes, self.frame_count, &mut self.encoded);
+        self.frame_count += 1;
+    }
+
+    pub fn flush(&mut self) {
+        self.compressor.flush(&mut self.encoded);
+    }
+
+    /// Decode all frames for frequency analysis.
+    pub fn to_vec(&self) -> Vec<f32> {
+        let mut out = Vec::new();
+        segment::decode(&self.encoded, &mut out);
+        out
+    }
+
+    /// Get single frame for real-time display.
+    pub fn get_frame(&self, idx: usize) -> Option<Vec<f32>> {
+        segment::decode_single_frame(&self.encoded, idx)
+    }
+}
+```
+
+**Memory reduction:** 13.4 MB/zone → 3.4–6.7 MB/zone. 16 zones: 54–107 MB
+instead of 214 MB. Disaster response hardware (Raspberry Pi 4: 4–8 GB) can
+handle 2–4× more concurrent zones.
+
+---
+
+### Integration 7: Heartbeat Micro-Doppler Compression
+
+**File:** `wifi-densepose-mat/src/detection/heartbeat.rs`
+**Crate:** `ruvector-temporal-tensor`
+
+**Current approach:** Heartbeat detection uses micro-Doppler spectrograms:
+sliding STFT of CSI amplitude time-series. Each zone stores a spectrogram of
+shape [n_freq_bins=128, n_time=600] (60 seconds at 10 Hz output rate):
+128 × 600 × 4 bytes = **307 KB per zone**. With 16 zones: 4.9 MB — acceptable,
+but heartbeat spectrograms are the most access-intensive (queried at every triage
+update).
+
+**ruvector integration:** `TemporalTensorCompressor` stores the spectrogram rows
+as temporal frames (each row = one frequency bin's time-evolution). Hot tier
+(recent 10 seconds) at 8-bit, warm (10–30 sec) at 5-bit, cold (>30 sec) at 3-bit.
+Recent heartbeat cycles remain high-fidelity; historical data is compressed 5x:
+
+```rust
+pub struct CompressedHeartbeatSpectrogram {
+    /// One compressor per frequency bin
+    bin_buffers: Vec<TemporalTensorCompressor>,
+    encoded: Vec<Vec<u8>>,
+    n_freq_bins: usize,
+    frame_count: u64,
+}
+
+impl CompressedHeartbeatSpectrogram {
+    pub fn new(n_freq_bins: usize) -> Self {
+        let bin_buffers: Vec<_> = (0..n_freq_bins)
+            .map(|i| TemporalTensorCompressor::new(TierPolicy::default(), 1, i as u64))
+            .collect();
+        let encoded = vec![Vec::new(); n_freq_bins];
+        Self { bin_buffers, encoded, n_freq_bins, frame_count: 0 }
+    }
+
+    /// Push one column of the spectrogram (one time step, all frequency bins).
+    pub fn push_column(&mut self, column: &[f32]) {
+        for (i, (&val, buf)) in column.iter().zip(self.bin_buffers.iter_mut()).enumerate() {
+            buf.push_frame(&[val], self.frame_count, &mut self.encoded[i]);
+        }
+        self.frame_count += 1;
+    }
+
+    /// Extract heartbeat frequency band power (0.8–1.5 Hz) from recent frames.
+    pub fn heartbeat_band_power(&self, low_bin: usize, high_bin: usize) -> f32 {
+        (low_bin..=high_bin.min(self.n_freq_bins - 1))
+            .map(|b| {
+                let mut out = Vec::new();
+                segment::decode(&self.encoded[b], &mut out);
+                out.iter().rev().take(100).map(|x| x * x).sum::<f32>()
+            })
+            .sum::<f32>()
+            / (high_bin - low_bin + 1) as f32
+    }
+}
+```
+
+---
+
+## Performance Summary
+
+| Integration Point | File | Crate | Before | After |
+|---|---|---|---|---|
+| Subcarrier selection | `subcarrier_selection.rs` | ruvector-mincut | O(n log n) static sort | O(n^1.5 log n) dynamic partition |
+| Spectrogram gating | `spectrogram.rs` | ruvector-attn-mincut | Uniform STFT bins | Attention-gated noise suppression |
+| BVP aggregation | `bvp.rs` | ruvector-attention | Uniform subcarrier sum | Sensitivity-weighted attention |
+| Fresnel geometry | `fresnel.rs` | ruvector-solver | Fixed geometry formula | Data-driven multi-obs system |
+| Multi-AP triangulation | `triangulation.rs` (MAT) | ruvector-solver | O(N^3) dense Gaussian | O(1) 2×2 Neumann system |
+| Breathing buffer | `breathing.rs` (MAT) | ruvector-temporal-tensor | 13.4 MB/zone | 3.4–6.7 MB/zone (50–75% less) |
+| Heartbeat spectrogram | `heartbeat.rs` (MAT) | ruvector-temporal-tensor | 307 KB/zone uniform | Tiered hot/warm/cold |
+
+## Dependency Changes Required
+
+Add to `rust-port/wifi-densepose-rs/Cargo.toml` workspace (already present from ADR-016):
+```toml
+ruvector-mincut = "2.0.4"          # already present
+ruvector-attn-mincut = "2.0.4"    # already present
+ruvector-temporal-tensor = "2.0.4" # already present
+ruvector-solver = "2.0.4"          # already present
+ruvector-attention = "2.0.4"       # already present
+```
+
+Add to `wifi-densepose-signal/Cargo.toml` and `wifi-densepose-mat/Cargo.toml`:
+```toml
+[dependencies]
+ruvector-mincut = { workspace = true }
+ruvector-attn-mincut = { workspace = true }
+ruvector-temporal-tensor = { workspace = true }
+ruvector-solver = { workspace = true }
+ruvector-attention = { workspace = true }
+```
+
+## Correction to ADR-002 Dependency Strategy
+
+ADR-002's dependency strategy section specifies non-existent crates:
+```toml
+# WRONG (ADR-002 original — these crates do not exist at crates.io)
+ruvector-core = { version = "0.1", features = ["hnsw", "sona", "gnn"] }
+ruvector-data-framework = { version = "0.1", features = ["rvf", "witness", "crypto"] }
+ruvector-consensus = { version = "0.1", features = ["raft"] }
+ruvector-wasm = { version = "0.1", features = ["edge-runtime"] }
+```
+
+The correct published crates (verified at crates.io, source at github.com/ruvnet/ruvector):
+```toml
+# CORRECT (as of 2026-02-28, all at v2.0.4)
+ruvector-mincut = "2.0.4"          # Dynamic min-cut, O(n^1.5 log n) updates
+ruvector-attn-mincut = "2.0.4"    # Attention + mincut gating
+ruvector-temporal-tensor = "2.0.4" # Tiered temporal compression
+ruvector-solver = "2.0.4"          # NeumannSolver, sublinear convergence
+ruvector-attention = "2.0.4"       # ScaledDotProductAttention
+```
+
+The RVF cognitive container format (ADR-003), HNSW search (ADR-004), SONA
+self-learning (ADR-005), GNN patterns (ADR-006), post-quantum crypto (ADR-007),
+Raft consensus (ADR-008), and WASM edge runtime (ADR-009) described in ADR-002
+are architectural capabilities internal to ruvector but not exposed as separate
+published crates at v2.0.4. Those ADRs remain as forward-looking architectural
+guidance; their implementation paths will use the five published crates as
+building blocks where applicable.
+
+## Implementation Priority
+
+| Priority | Integration | Rationale |
+|---|---|---|
+| P1 | Breathing + heartbeat compression (MAT) | Memory-critical for 16-zone disaster deployments |
+| P1 | Multi-AP triangulation (MAT) | Safety-critical accuracy improvement |
+| P2 | Subcarrier selection via DynamicMinCut | Enables dynamic environment adaptation |
+| P2 | BVP attention aggregation | Direct accuracy improvement for activity classification |
+| P3 | Spectrogram attention gating | Reduces CNN input noise; requires CNN retraining |
+| P3 | Fresnel geometry system | Improves breathing detection in unknown geometries |
+
+## Consequences
+
+### Positive
+- Consistent ruvector integration across all production crates (train, signal, MAT)
+- 50–75% memory reduction in disaster detection enables 2–4× more concurrent zones
+- Dynamic subcarrier partitioning adapts to environment changes without manual tuning
+- Attention-weighted BVP reduces velocity estimation error from insensitive subcarriers
+- NeumannSolver triangulation is O(1) in AP count (always solves 2×2 system)
+
+### Negative
+- ruvector crates operate on `&[f32]` CPU slices; MAT and signal crates must
+  bridge from their native types (ndarray, complex numbers)
+- `ruvector-temporal-tensor` compression is lossy; heartbeat amplitude values
+  may lose fine-grained detail in warm/cold tiers (mitigated by hot-tier recency)
+- Subcarrier selection via DynamicMinCut assumes a bipartite-like partition;
+  environments with 3+ distinct subcarrier groups may need multi-way cut extension
+
+## Related ADRs
+
+- ADR-001: WiFi-Mat Disaster Detection (target: MAT integrations 5–7)
+- ADR-002: RuVector RVF Integration Strategy (corrected crate names above)
+- ADR-014: SOTA Signal Processing Algorithms (target: signal integrations 1–4)
+- ADR-015: Public Dataset Training Strategy (preceding implementation in ADR-016)
+- ADR-016: RuVector Integration for Training Pipeline (completed reference implementation)
+
+## References
+
+- [ruvector source](https://github.com/ruvnet/ruvector)
+- [DynamicMinCut API](https://docs.rs/ruvector-mincut/2.0.4)
+- [NeumannSolver convergence](https://en.wikipedia.org/wiki/Neumann_series)
+- [Tiered quantization](https://arxiv.org/abs/2103.13630)
+- SpotFi (SIGCOMM 2015), Widar 3.0 (MobiSys 2019), FarSense (MobiCom 2019)

docs/adr/ADR-018-esp32-dev-implementation.md

@@ -0,0 +1,312 @@
+# ADR-018: ESP32 Development Implementation Path
+
+## Status
+Proposed
+
+## Date
+2026-02-28
+
+## Context
+
+ADR-012 established the ESP32 CSI Sensor Mesh architecture: hardware rationale, firmware file structure, `csi_feature_frame_t` C struct, aggregator design, clock-drift handling via feature-level fusion, and a $54 starter BOM. That ADR answers *what* to build and *why*.
+
+This ADR answers *how* to build it — the concrete development sequence, the specific integration points in existing code, and how to test each layer before hardware is in hand.
+
+### Current State
+
+**Already implemented:**
+
+| Component | Location | Status |
+|-----------|----------|--------|
+| Binary frame parser | `wifi-densepose-hardware/src/esp32_parser.rs` | Complete — `Esp32CsiParser::parse_frame()`, `parse_stream()`, 7 passing tests |
+| Frame types | `wifi-densepose-hardware/src/csi_frame.rs` | Complete — `CsiFrame`, `CsiMetadata`, `SubcarrierData`, `to_amplitude_phase()` |
+| Parse error types | `wifi-densepose-hardware/src/error.rs` | Complete — `ParseError` enum with 6 variants |
+| Signal processing pipeline | `wifi-densepose-signal` crate | Complete — Hampel, Fresnel, BVP, Doppler, spectrogram |
+| CSI extractor (Python) | `v1/src/hardware/csi_extractor.py` | Stub — `_read_raw_data()` raises `NotImplementedError` |
+| Router interface (Python) | `v1/src/hardware/router_interface.py` | Stub — `_parse_csi_response()` raises `RouterConnectionError` |
+
+**Not yet implemented:**
+
+- ESP-IDF C firmware (`firmware/esp32-csi-node/`)
+- UDP aggregator binary (`crates/wifi-densepose-hardware/src/aggregator/`)
+- `CsiFrame` → `wifi_densepose_signal::CsiData` bridge
+- Python `_read_raw_data()` real UDP socket implementation
+- Proof capture tooling for real hardware
+
+### Binary Frame Format (implemented in `esp32_parser.rs`)
+
+```
+Offset  Size  Field
+0       4     Magic: 0xC5110001 (LE)
+4       1     Node ID (0-255)
+5       1     Number of antennas
+6       2     Number of subcarriers (LE u16)
+8       4     Frequency Hz (LE u32, e.g. 2412 for 2.4 GHz ch1)
+12      4     Sequence number (LE u32)
+16      1     RSSI (i8, dBm)
+17      1     Noise floor (i8, dBm)
+18      2     Reserved (zero)
+20      N*2   I/Q pairs: (i8, i8) per subcarrier, repeated per antenna
+```
+
+Total frame size: 20 + (n_antennas × n_subcarriers × 2) bytes.
+
+For 3 antennas, 56 subcarriers: 20 + 336 = 356 bytes per frame.
+
+The firmware must write frames in this exact format. The parser already validates magic, bounds-checks `n_subcarriers` (≤512), and resyncs the stream on magic search for `parse_stream()`.
+
+## Decision
+
+We will implement the ESP32 development stack in four sequential layers, each independently testable before hardware is available.
+
+### Layer 1 — ESP-IDF Firmware (`firmware/esp32-csi-node/`)
+
+Implement the C firmware project per the file structure in ADR-012. Key design decisions deferred from ADR-012:
+
+**CSI callback → frame serializer:**
+
+```c
+// main/csi_collector.c
+static void csi_data_callback(void *ctx, wifi_csi_info_t *info) {
+    if (!info || !info->buf) return;
+
+    // Write binary frame header (20 bytes, little-endian)
+    uint8_t frame[FRAME_MAX_BYTES];
+    uint32_t magic = 0xC5110001;
+    memcpy(frame + 0,  &magic,              4);
+    frame[4] = g_node_id;
+    frame[5] = info->rx_ctrl.ant;           // antenna index (1 for ESP32 single-antenna)
+    uint16_t n_sub = info->len / 2;         // len = n_subcarriers * 2 (I + Q bytes)
+    memcpy(frame + 6,  &n_sub,              2);
+    uint32_t freq_mhz = g_channel_freq_mhz;
+    memcpy(frame + 8,  &freq_mhz,           4);
+    memcpy(frame + 12, &g_seq_num,          4);
+    frame[16] = (int8_t)info->rx_ctrl.rssi;
+    frame[17] = (int8_t)info->rx_ctrl.noise_floor;
+    frame[18] = 0; frame[19] = 0;
+
+    // Write I/Q payload directly from info->buf
+    memcpy(frame + 20, info->buf, info->len);
+
+    // Send over UDP to aggregator
+    stream_sender_write(frame, 20 + info->len);
+    g_seq_num++;
+}
+```
+
+**No on-device FFT** (contradicting ADR-012's optional feature extraction path): The Rust aggregator will do feature extraction using the SOTA `wifi-densepose-signal` pipeline. Raw I/Q is cheaper to stream at ESP32 sampling rates (~100 Hz at 56 subcarriers = ~35 KB/s per node).
+
+**`sdkconfig.defaults`** must enable:
+
+```
+CONFIG_ESP_WIFI_CSI_ENABLED=y
+CONFIG_LWIP_SO_RCVBUF=y
+CONFIG_FREERTOS_HZ=1000
+```
+
+**Build toolchain**: ESP-IDF v5.2+ (pinned). Docker image: `espressif/idf:v5.2` for reproducible CI.
+
+### Layer 2 — UDP Aggregator (`crates/wifi-densepose-hardware/src/aggregator/`)
+
+New module within the hardware crate. Entry point: `aggregator_main()` callable as a binary target.
+
+```rust
+// crates/wifi-densepose-hardware/src/aggregator/mod.rs
+
+pub struct Esp32Aggregator {
+    socket: UdpSocket,
+    nodes: HashMap<u8, NodeState>,       // keyed by node_id from frame header
+    tx: mpsc::SyncSender<CsiFrame>,      // outbound to bridge
+}
+
+struct NodeState {
+    last_seq: u32,
+    drop_count: u64,
+    last_recv: Instant,
+}
+
+impl Esp32Aggregator {
+    /// Bind UDP socket and start blocking receive loop.
+    /// Each valid frame is forwarded on `tx`.
+    pub fn run(&mut self) -> Result<(), AggregatorError> {
+        let mut buf = vec![0u8; 4096];
+        loop {
+            let (n, _addr) = self.socket.recv_from(&mut buf)?;
+            match Esp32CsiParser::parse_frame(&buf[..n]) {
+                Ok((frame, _consumed)) => {
+                    let state = self.nodes.entry(frame.metadata.node_id)
+                        .or_insert_with(NodeState::default);
+                    // Track drops via sequence number gaps
+                    if frame.metadata.seq_num != state.last_seq + 1 {
+                        state.drop_count += (frame.metadata.seq_num
+                            .wrapping_sub(state.last_seq + 1)) as u64;
+                    }
+                    state.last_seq = frame.metadata.seq_num;
+                    state.last_recv = Instant::now();
+                    let _ = self.tx.try_send(frame); // drop if pipeline is full
+                }
+                Err(e) => {
+                    // Log and continue — never crash on bad UDP packet
+                    eprintln!("aggregator: parse error: {e}");
+                }
+            }
+        }
+    }
+}
+```
+
+**Testable without hardware**: The test suite generates frames using `build_test_frame()` (same helper pattern as `esp32_parser.rs` tests) and sends them over a loopback UDP socket. The aggregator receives and forwards them identically to real hardware frames.
+
+### Layer 3 — CsiFrame → CsiData Bridge
+
+Bridge from `wifi-densepose-hardware::CsiFrame` to the signal processing type `wifi_densepose_signal::CsiData` (or a compatible intermediate type consumed by the Rust pipeline).
+
+```rust
+// crates/wifi-densepose-hardware/src/bridge.rs
+
+use crate::{CsiFrame};
+
+/// Intermediate type compatible with the signal processing pipeline.
+/// Maps directly from CsiFrame without cloning the I/Q storage.
+pub struct CsiData {
+    pub timestamp_unix_ms: u64,
+    pub node_id: u8,
+    pub n_antennas: usize,
+    pub n_subcarriers: usize,
+    pub amplitude: Vec<f64>,   // length: n_antennas * n_subcarriers
+    pub phase: Vec<f64>,       // length: n_antennas * n_subcarriers
+    pub rssi_dbm: i8,
+    pub noise_floor_dbm: i8,
+    pub channel_freq_mhz: u32,
+}
+
+impl From<CsiFrame> for CsiData {
+    fn from(frame: CsiFrame) -> Self {
+        let n_ant = frame.metadata.n_antennas as usize;
+        let n_sub = frame.metadata.n_subcarriers as usize;
+        let (amplitude, phase) = frame.to_amplitude_phase();
+        CsiData {
+            timestamp_unix_ms: frame.metadata.timestamp_unix_ms,
+            node_id: frame.metadata.node_id,
+            n_antennas: n_ant,
+            n_subcarriers: n_sub,
+            amplitude,
+            phase,
+            rssi_dbm: frame.metadata.rssi_dbm,
+            noise_floor_dbm: frame.metadata.noise_floor_dbm,
+            channel_freq_mhz: frame.metadata.channel_freq_mhz,
+        }
+    }
+}
+```
+
+The bridge test: parse a known binary frame, convert to `CsiData`, assert `amplitude[0]` = √(I₀² + Q₀²) to within f64 precision.
+
+### Layer 4 — Python `_read_raw_data()` Real Implementation
+
+Replace the `NotImplementedError` stub in `v1/src/hardware/csi_extractor.py` with a UDP socket reader. This allows the Python pipeline to receive real CSI from the aggregator while the Rust pipeline is being integrated.
+
+```python
+# v1/src/hardware/csi_extractor.py
+# Replace _read_raw_data() stub:
+
+import socket as _socket
+
+class CSIExtractor:
+    ...
+    def _read_raw_data(self) -> bytes:
+        """Read one raw CSI frame from the UDP aggregator.
+
+        Expects binary frames in the ESP32 format (magic 0xC5110001 header).
+        Aggregator address configured via AGGREGATOR_HOST / AGGREGATOR_PORT
+        environment variables (defaults: 127.0.0.1:5005).
+        """
+        if not hasattr(self, '_udp_socket'):
+            host = self.config.get('aggregator_host', '127.0.0.1')
+            port = int(self.config.get('aggregator_port', 5005))
+            sock = _socket.socket(_socket.AF_INET, _socket.SOCK_DGRAM)
+            sock.bind((host, port))
+            sock.settimeout(1.0)
+            self._udp_socket = sock
+        try:
+            data, _ = self._udp_socket.recvfrom(4096)
+            return data
+        except _socket.timeout:
+            raise CSIExtractionError(
+                "No CSI data received within timeout — "
+                "is the ESP32 aggregator running?"
+            )
+```
+
+This is tested with a mock UDP server in the unit tests (existing `test_csi_extractor_tdd.py` pattern) and with the real aggregator in integration.
+
+## Development Sequence
+
+```
+Phase 1 (Firmware + Aggregator — no pipeline integration needed):
+  1. Write firmware/esp32-csi-node/ C project (ESP-IDF v5.2)
+  2. Flash to one ESP32-S3-DevKitC board
+  3. Verify binary frames arrive on laptop UDP socket using Wireshark
+  4. Write aggregator crate + loopback test
+
+Phase 2 (Bridge + Python stub):
+  5. Implement CsiFrame → CsiData bridge
+  6. Replace Python _read_raw_data() with UDP socket
+  7. Run Python pipeline end-to-end against loopback aggregator (synthetic frames)
+
+Phase 3 (Real hardware integration):
+  8. Run Python pipeline against live ESP32 frames
+  9. Capture 10-second real CSI bundle (firmware/esp32-csi-node/proof/)
+  10. Verify proof bundle hash (ADR-011 pattern)
+  11. Mark ADR-012 Accepted, mark this ADR Accepted
+```
+
+## Testing Without Hardware
+
+All four layers are testable before a single ESP32 is purchased:
+
+| Layer | Test Method |
+|-------|-------------|
+| Firmware binary format | Build a `build_test_frame()` helper in Rust, compare its output byte-for-byte against a hand-computed reference frame |
+| Aggregator | Loopback UDP: test sends synthetic frames to 127.0.0.1:5005, aggregator receives and forwards on channel |
+| Bridge | `assert_eq!(csi_data.amplitude[0], f64::sqrt((iq[0].i as f64).powi(2) + (iq[0].q as f64).powi(2)))` |
+| Python UDP reader | Mock UDP server in pytest using `socket.socket` in a background thread |
+
+The existing `esp32_parser.rs` test suite already validates parsing of correctly-formatted binary frames. The aggregator and bridge tests build on top of the same test frame construction.
+
+## Consequences
+
+### Positive
+- **Layered testability**: Each layer can be validated independently before hardware acquisition.
+- **No new external dependencies**: UDP sockets are in stdlib (both Rust and Python). Firmware uses only ESP-IDF and esp-dsp component.
+- **Stub elimination**: Replaces the last two `NotImplementedError` stubs in the Python hardware layer with real code backed by real data.
+- **Proof of reality**: Phase 3 produces a captured CSI bundle hashed to a known value, satisfying ADR-011 for hardware-sourced data.
+- **Signal-crate reuse**: The SOTA Hampel/Fresnel/BVP/Doppler processing from ADR-014 applies unchanged to real ESP32 frames after the bridge converts them.
+
+### Negative
+- **Firmware requires ESP-IDF toolchain**: Not buildable without a 2+ GB ESP-IDF installation. CI must use the official Docker image or skip firmware compilation.
+- **Raw I/Q bandwidth**: Streaming raw I/Q (not features) at 100 Hz × 3 antennas × 56 subcarriers = ~35 KB/s/node. At 6 nodes = ~210 KB/s. Fine for LAN; not suitable for WAN.
+- **Single-antenna real-world**: Most ESP32-S3-DevKitC boards have one on-board antenna. Multi-antenna data requires external antenna + board with U.FL connector or purpose-built multi-radio setup.
+
+### Deferred
+- **Multi-node clock drift compensation**: ADR-012 specifies feature-level fusion. The aggregator in this ADR passes raw `CsiFrame` per-node. Drift compensation lives in a future `FeatureFuser` layer (not scoped here).
+- **ESP-IDF firmware CI**: Firmware compilation in GitHub Actions requires the ESP-IDF Docker image. CI integration is deferred until Phase 3 hardware validation.
+
+## Interaction with Other ADRs
+
+| ADR | Interaction |
+|-----|-------------|
+| ADR-011 | Phase 3 produces a real CSI proof bundle satisfying mock elimination |
+| ADR-012 | This ADR implements the development path for ADR-012's architecture |
+| ADR-014 | SOTA signal processing applies unchanged after bridge layer |
+| ADR-008 | Aggregator handles multi-node; distributed consensus is a later concern |
+
+## References
+
+- [Espressif ESP-CSI Repository](https://github.com/espressif/esp-csi)
+- [ESP-IDF WiFi CSI API Reference](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
+- `wifi-densepose-hardware/src/esp32_parser.rs` — binary frame parser implementation
+- `wifi-densepose-hardware/src/csi_frame.rs` — `CsiFrame`, `to_amplitude_phase()`
+- ADR-012: ESP32 CSI Sensor Mesh (architecture)
+- ADR-011: Python Proof-of-Reality and Mock Elimination
+- ADR-014: SOTA Signal Processing

docs/build-guide.md

@@ -0,0 +1,684 @@
+# WiFi-DensePose Build and Run Guide
+
+Covers every way to build, run, and deploy the system -- from a zero-hardware verification to a full ESP32 mesh with 3D visualization.
+
+---
+
+## Table of Contents
+
+1. [Quick Start (Verification Only -- No Hardware)](#1-quick-start-verification-only----no-hardware)
+2. [Python Pipeline (v1/)](#2-python-pipeline-v1)
+3. [Rust Pipeline (v2)](#3-rust-pipeline-v2)
+4. [Three.js Visualization](#4-threejs-visualization)
+5. [Docker Deployment](#5-docker-deployment)
+6. [ESP32 Hardware Setup](#6-esp32-hardware-setup)
+7. [Environment-Specific Builds](#7-environment-specific-builds)
+
+---
+
+## 1. Quick Start (Verification Only -- No Hardware)
+
+The fastest way to confirm the signal processing pipeline is real and deterministic. Requires only Python 3.8+, numpy, and scipy. No WiFi hardware, no GPU, no Docker.
+
+```bash
+# From the repository root:
+./verify
+```
+
+This runs three phases:
+
+1. **Environment checks** -- confirms Python, numpy, scipy, and proof files are present.
+2. **Proof pipeline replay** -- feeds a published reference signal through the full signal processing chain (noise filtering, Hamming windowing, amplitude normalization, FFT-based Doppler extraction, power spectral density via scipy.fft) and computes a SHA-256 hash of the output.
+3. **Production code integrity scan** -- scans `v1/src/` for `np.random.rand` / `np.random.randn` calls in production code (test helpers are excluded).
+
+Exit codes:
+- `0` PASS -- pipeline hash matches the published expected hash
+- `1` FAIL -- hash mismatch or error
+- `2` SKIP -- no expected hash file to compare against
+
+Additional flags:
+
+```bash
+./verify --verbose         # Detailed feature statistics and Doppler spectrum
+./verify --verbose --audit # Full verification + codebase audit
+
+# Or via make:
+make verify
+make verify-verbose
+make verify-audit
+```
+
+If the expected hash file is missing, regenerate it:
+
+```bash
+python3 v1/data/proof/verify.py --generate-hash
+```
+
+### Minimal dependencies for verification only
+
+```bash
+pip install numpy==1.26.4 scipy==1.14.1
+```
+
+Or install the pinned set that guarantees hash reproducibility:
+
+```bash
+pip install -r v1/requirements-lock.txt
+```
+
+The lock file pins: `numpy==1.26.4`, `scipy==1.14.1`, `pydantic==2.10.4`, `pydantic-settings==2.7.1`.
+
+---
+
+## 2. Python Pipeline (v1/)
+
+The Python pipeline lives under `v1/` and provides the full API server, signal processing, sensing modules, and WebSocket streaming.
+
+### Prerequisites
+
+- Python 3.8+
+- pip
+
+### Install (verification-only -- lightweight)
+
+```bash
+pip install -r v1/requirements-lock.txt
+```
+
+This installs only the four packages needed for deterministic pipeline verification.
+
+### Install (full pipeline with API server)
+
+```bash
+pip install -r requirements.txt
+```
+
+This pulls in FastAPI, uvicorn, torch, OpenCV, SQLAlchemy, Redis client, and all other runtime dependencies.
+
+### Verify the pipeline
+
+```bash
+python3 v1/data/proof/verify.py
+```
+
+Same as `./verify` but calls the Python script directly, skipping the bash wrapper's codebase scan phase.
+
+### Run the API server
+
+```bash
+uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000
+```
+
+The server exposes:
+- REST API docs: http://localhost:8000/docs
+- Health check: http://localhost:8000/health
+- Latest poses: http://localhost:8000/api/v1/pose/latest
+- WebSocket pose stream: ws://localhost:8000/ws/pose/stream
+- WebSocket analytics: ws://localhost:8000/ws/analytics/events
+
+For development with auto-reload:
+
+```bash
+uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+### Run with commodity WiFi (RSSI sensing -- no custom hardware)
+
+The commodity sensing module (`v1/src/sensing/`) extracts presence and motion features from standard Linux WiFi metrics (RSSI, noise floor, link quality) without any hardware modification. See [ADR-013](adr/ADR-013-feature-level-sensing-commodity-gear.md) for full design details.
+
+Requirements:
+- Any Linux machine with a WiFi interface (laptop, Raspberry Pi, etc.)
+- Connected to a WiFi access point (the AP is the signal source)
+- No root required for basic RSSI reading via `/proc/net/wireless`
+
+The module provides:
+- `LinuxWifiCollector` -- reads real RSSI from `/proc/net/wireless` and `iw` commands
+- `RssiFeatureExtractor` -- computes rolling statistics, FFT spectral features, CUSUM change-point detection
+- `PresenceClassifier` -- rule-based presence/motion classification
+
+What it can detect:
+| Capability | Single Receiver | 3+ Receivers |
+|-----------|----------------|-------------|
+| Binary presence | Yes (90-95%) | Yes (90-95%) |
+| Coarse motion (still/moving) | Yes (85-90%) | Yes (85-90%) |
+| Room-level location | No | Marginal (70-80%) |
+
+What it cannot detect: body pose, heartbeat, reliable respiration. See ADR-013 for the honest capability matrix.
+
+### Python project structure
+
+```
+v1/
+  src/
+    api/
+      main.py              # FastAPI application entry point
+      routers/             # REST endpoint routers (pose, stream, health)
+      middleware/           # Auth, rate limiting
+      websocket/           # WebSocket connection manager, pose stream
+    config/                # Settings, domain configs
+    sensing/
+      rssi_collector.py    # LinuxWifiCollector + SimulatedCollector
+      feature_extractor.py # RssiFeatureExtractor (FFT, CUSUM, spectral)
+      classifier.py        # PresenceClassifier (rule-based)
+      backend.py           # SensingBackend protocol
+  data/
+    proof/
+      sample_csi_data.json       # Deterministic reference signal
+      expected_features.sha256   # Published expected hash
+      verify.py                  # One-command verification script
+  requirements-lock.txt          # Pinned deps for hash reproducibility
+```
+
+---
+
+## 3. Rust Pipeline (v2)
+
+A high-performance Rust port with ~810x speedup over the Python pipeline for the full signal processing chain.
+
+### Prerequisites
+
+- Rust 1.70+ (install via [rustup](https://rustup.rs/))
+- cargo (included with Rust)
+- System dependencies for OpenBLAS (used by ndarray-linalg):
+  ```bash
+  # Ubuntu/Debian
+  sudo apt-get install build-essential gfortran libopenblas-dev pkg-config
+
+  # macOS
+  brew install openblas
+  ```
+
+### Build
+
+```bash
+cd rust-port/wifi-densepose-rs
+cargo build --release
+```
+
+Release profile is configured with LTO, single codegen unit, and `-O3` for maximum performance.
+
+### Test
+
+```bash
+cd rust-port/wifi-densepose-rs
+cargo test --workspace
+```
+
+Runs 107 tests across all workspace crates.
+
+### Benchmark
+
+```bash
+cd rust-port/wifi-densepose-rs
+cargo bench --package wifi-densepose-signal
+```
+
+Expected throughput:
+| Operation | Latency | Throughput |
+|-----------|---------|------------|
+| CSI Preprocessing (4x64) | ~5.19 us | 49-66 Melem/s |
+| Phase Sanitization (4x64) | ~3.84 us | 67-85 Melem/s |
+| Feature Extraction (4x64) | ~9.03 us | 7-11 Melem/s |
+| Motion Detection | ~186 ns | -- |
+| Full Pipeline | ~18.47 us | ~54,000 fps |
+
+### Workspace crates
+
+The Rust workspace contains 10 crates under `crates/`:
+
+| Crate | Description |
+|-------|-------------|
+| `wifi-densepose-core` | Core types, traits, and domain models |
+| `wifi-densepose-signal` | Signal processing (FFT, phase unwrapping, Doppler, correlation) |
+| `wifi-densepose-nn` | Neural network inference (ONNX Runtime, candle, tch) |
+| `wifi-densepose-api` | Axum-based HTTP/WebSocket API server |
+| `wifi-densepose-db` | Database layer (SQLx, PostgreSQL, SQLite, Redis) |
+| `wifi-densepose-config` | Configuration loading (env vars, YAML, TOML) |
+| `wifi-densepose-hardware` | Hardware adapters (ESP32, Intel 5300, Atheros, UDP, PCAP) |
+| `wifi-densepose-wasm` | WebAssembly bindings for browser deployment |
+| `wifi-densepose-cli` | Command-line interface |
+| `wifi-densepose-mat` | WiFi-Mat disaster response module (search and rescue) |
+
+Build individual crates:
+
+```bash
+# Signal processing only
+cargo build --release --package wifi-densepose-signal
+
+# API server
+cargo build --release --package wifi-densepose-api
+
+# Disaster response module
+cargo build --release --package wifi-densepose-mat
+
+# WASM target (see Section 7 for full instructions)
+cargo build --release --package wifi-densepose-wasm --target wasm32-unknown-unknown
+```
+
+---
+
+## 4. Three.js Visualization
+
+A browser-based 3D visualization dashboard that renders DensePose body models with 24 body parts, signal visualization, and environment rendering.
+
+### Run
+
+Open `ui/viz.html` directly in a browser:
+
+```bash
+# macOS
+open ui/viz.html
+
+# Linux
+xdg-open ui/viz.html
+
+# Or serve it locally
+python3 -m http.server 3000 --directory ui
+# Then open http://localhost:3000/viz.html
+```
+
+### WebSocket connection
+
+The visualization connects to `ws://localhost:8000/ws/pose` for real-time pose data. If no server is running, it falls back to a demo mode with simulated data so you can still see the 3D rendering.
+
+To see live data:
+
+1. Start the API server (Python or Rust)
+2. Open `ui/viz.html`
+3. The dashboard will connect automatically
+
+---
+
+## 5. Docker Deployment
+
+### Development (with hot-reload, Postgres, Redis, Prometheus, Grafana)
+
+```bash
+docker compose up
+```
+
+This starts:
+- `wifi-densepose-dev` -- API server with `--reload`, debug logging, auth disabled (port 8000)
+- `postgres` -- PostgreSQL 15 (port 5432)
+- `redis` -- Redis 7 with AOF persistence (port 6379)
+- `prometheus` -- metrics scraping (port 9090)
+- `grafana` -- dashboards (port 3000, login: admin/admin)
+- `nginx` -- reverse proxy (ports 80, 443)
+
+```bash
+# View logs
+docker compose logs -f wifi-densepose
+
+# Run tests inside the container
+docker compose exec wifi-densepose pytest tests/ -v
+
+# Stop everything
+docker compose down
+
+# Stop and remove volumes
+docker compose down -v
+```
+
+### Production
+
+Uses the production Dockerfile stage with 4 uvicorn workers, auth enabled, rate limiting, and resource limits.
+
+```bash
+# Build production image
+docker build --target production -t wifi-densepose:latest .
+
+# Run standalone
+docker run -d \
+  --name wifi-densepose \
+  -p 8000:8000 \
+  -e ENVIRONMENT=production \
+  -e SECRET_KEY=your-secret-key \
+  wifi-densepose:latest
+```
+
+For the full production stack with Docker Swarm secrets:
+
+```bash
+# Create required secrets first
+echo "db_password_here" | docker secret create db_password -
+echo "redis_password_here" | docker secret create redis_password -
+echo "jwt_secret_here" | docker secret create jwt_secret -
+echo "api_key_here" | docker secret create api_key -
+echo "grafana_password_here" | docker secret create grafana_password -
+
+# Set required environment variables
+export DATABASE_URL=postgresql://wifi_user:db_password_here@postgres:5432/wifi_densepose
+export REDIS_URL=redis://redis:6379/0
+export SECRET_KEY=your-secret-key
+export JWT_SECRET=your-jwt-secret
+export ALLOWED_HOSTS=your-domain.com
+export POSTGRES_DB=wifi_densepose
+export POSTGRES_USER=wifi_user
+
+# Deploy with Docker Swarm
+docker stack deploy -c docker-compose.prod.yml wifi-densepose
+```
+
+Production compose includes:
+- 3 API server replicas with rolling updates and rollback
+- Resource limits (2 CPU, 4GB RAM per replica)
+- Health checks on all services
+- JSON file logging with rotation
+- Separate monitoring network (overlay)
+- Prometheus with alerting rules and 15-day retention
+- Grafana with provisioned datasources and dashboards
+
+### Dockerfile stages
+
+The multi-stage `Dockerfile` provides four targets:
+
+| Target | Use | Command |
+|--------|-----|---------|
+| `development` | Local dev with hot-reload | `docker build --target development .` |
+| `production` | Optimized production image | `docker build --target production .` |
+| `testing` | Runs pytest during build | `docker build --target testing .` |
+| `security` | Runs safety + bandit scans | `docker build --target security .` |
+
+---
+
+## 6. ESP32 Hardware Setup
+
+Uses ESP32-S3 boards as WiFi CSI sensor nodes. See [ADR-012](adr/ADR-012-esp32-csi-sensor-mesh.md) for the full specification.
+
+### Bill of Materials (Starter Kit -- $54)
+
+| Item | Qty | Unit Cost | Total |
+|------|-----|-----------|-------|
+| ESP32-S3-DevKitC-1 | 3 | $10 | $30 |
+| USB-A to USB-C cables | 3 | $3 | $9 |
+| USB power adapter (multi-port) | 1 | $15 | $15 |
+| Consumer WiFi router (any) | 1 | $0 (existing) | $0 |
+| Aggregator (laptop or Pi 4) | 1 | $0 (existing) | $0 |
+| **Total** | | | **$54** |
+
+### Prerequisites
+
+Install ESP-IDF (Espressif's official development framework):
+
+```bash
+# Clone ESP-IDF
+mkdir -p ~/esp
+cd ~/esp
+git clone --recursive https://github.com/espressif/esp-idf.git
+cd esp-idf
+git checkout v5.2  # Pin to tested version
+
+# Install tools
+./install.sh esp32s3
+
+# Activate environment (run each session)
+. ./export.sh
+```
+
+### Flash a node
+
+```bash
+cd firmware/esp32-csi-node
+
+# Set target chip
+idf.py set-target esp32s3
+
+# Configure WiFi SSID/password and aggregator IP
+idf.py menuconfig
+# Navigate to: Component config > WiFi-DensePose CSI Node
+#   - Set WiFi SSID
+#   - Set WiFi password
+#   - Set aggregator IP address
+#   - Set node ID (1, 2, 3, ...)
+#   - Set sampling rate (10-100 Hz)
+
+# Build and flash (with USB cable connected)
+idf.py build flash monitor
+```
+
+`idf.py monitor` shows live serial output including CSI callback data. Press `Ctrl+]` to exit.
+
+Repeat for each node, incrementing the node ID.
+
+### Firmware project structure
+
+```
+firmware/esp32-csi-node/
+  CMakeLists.txt
+  sdkconfig.defaults          # Menuconfig defaults with CSI enabled
+  main/
+    main.c                    # Entry point, WiFi init, CSI callback
+    csi_collector.c           # CSI data collection and buffering
+    feature_extract.c         # On-device FFT and feature extraction
+    stream_sender.c           # UDP stream to aggregator
+    config.h                  # Node configuration
+    Kconfig.projbuild         # Menuconfig options
+  components/
+    esp_dsp/                  # Espressif DSP library for FFT
+```
+
+Each node does on-device feature extraction (raw I/Q to amplitude + phase + spectral bands), reducing bandwidth from ~11 KB/frame to ~470 bytes/frame. Nodes stream features via UDP to the aggregator.
+
+### Run the aggregator
+
+The aggregator collects UDP streams from all ESP32 nodes, performs feature-level fusion (not signal-level -- see ADR-012 for why), and feeds the fused data into the Rust or Python pipeline.
+
+```bash
+# Start the aggregator and pipeline via Docker
+docker compose -f docker-compose.esp32.yml up
+
+# Or run the Rust aggregator directly
+cd rust-port/wifi-densepose-rs
+cargo run --release --package wifi-densepose-hardware -- --mode esp32-aggregator --port 5000
+```
+
+### Verify with real hardware
+
+```bash
+docker exec aggregator python verify_esp32.py
+```
+
+This captures 10 seconds of data, produces feature JSON, and verifies the hash against the proof bundle.
+
+### What the ESP32 mesh can and cannot detect
+
+| Capability | 1 Node | 3 Nodes | 6 Nodes |
+|-----------|--------|---------|---------|
+| Presence detection | Good | Excellent | Excellent |
+| Coarse motion | Good | Excellent | Excellent |
+| Room-level location | None | Good | Excellent |
+| Respiration | Marginal | Good | Good |
+| Heartbeat | Poor | Poor | Marginal |
+| Multi-person count | None | Marginal | Good |
+| Pose estimation | None | Poor | Marginal |
+
+---
+
+## 7. Environment-Specific Builds
+
+### Browser (WASM)
+
+Compiles the Rust pipeline to WebAssembly for in-browser execution. See [ADR-009](adr/ADR-009-rvf-wasm-runtime-edge-deployment.md) for the edge deployment architecture.
+
+Prerequisites:
+
+```bash
+# Install wasm-pack
+curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
+
+# Or via cargo
+cargo install wasm-pack
+
+# Add the WASM target
+rustup target add wasm32-unknown-unknown
+```
+
+Build:
+
+```bash
+cd rust-port/wifi-densepose-rs
+
+# Build WASM package (outputs to pkg/)
+wasm-pack build crates/wifi-densepose-wasm --target web --release
+
+# Build with disaster response module included
+wasm-pack build crates/wifi-densepose-wasm --target web --release -- --features mat
+```
+
+The output `pkg/` directory contains `.wasm`, `.js` glue, and TypeScript definitions. Import in a web project:
+
+```javascript
+import init, { WifiDensePoseWasm } from './pkg/wifi_densepose_wasm.js';
+
+async function main() {
+  await init();
+  const processor = new WifiDensePoseWasm();
+  const result = processor.process_frame(csiJsonString);
+  console.log(JSON.parse(result));
+}
+main();
+```
+
+Run WASM tests:
+
+```bash
+wasm-pack test --headless --chrome crates/wifi-densepose-wasm
+```
+
+Container size targets by deployment profile:
+
+| Profile | Size | Suitable For |
+|---------|------|-------------|
+| Browser (int8 quantization) | ~10 MB | Chrome/Firefox dashboard |
+| IoT (int4 quantization) | ~0.7 MB | ESP32, constrained devices |
+| Mobile (int8 quantization) | ~6 MB | iOS/Android WebView |
+| Field (fp16 quantization) | ~62 MB | Offline disaster tablets |
+
+### IoT (ESP32)
+
+See [Section 6](#6-esp32-hardware-setup) for full ESP32 setup. The firmware runs on the device itself (C, compiled with ESP-IDF). The Rust aggregator runs on a host machine.
+
+For deploying the WASM runtime to a Raspberry Pi or similar:
+
+```bash
+# Cross-compile for ARM
+rustup target add aarch64-unknown-linux-gnu
+cargo build --release --package wifi-densepose-cli --target aarch64-unknown-linux-gnu
+```
+
+### Server (Docker)
+
+See [Section 5](#5-docker-deployment).
+
+Quick reference:
+
+```bash
+# Development
+docker compose up
+
+# Production standalone
+docker build --target production -t wifi-densepose:latest .
+docker run -d -p 8000:8000 wifi-densepose:latest
+
+# Production stack (Swarm)
+docker stack deploy -c docker-compose.prod.yml wifi-densepose
+```
+
+### Server (Direct -- no Docker)
+
+```bash
+# 1. Install Python dependencies
+pip install -r requirements.txt
+
+# 2. Set environment variables (copy from example.env)
+cp example.env .env
+# Edit .env with your settings
+
+# 3. Run with uvicorn (production)
+uvicorn v1.src.api.main:app \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --workers 4
+
+# Or run the Rust API server
+cd rust-port/wifi-densepose-rs
+cargo run --release --package wifi-densepose-api
+```
+
+### Development (local with hot-reload)
+
+Python:
+
+```bash
+# Create virtual environment
+python3 -m venv venv
+source venv/bin/activate
+
+# Install all dependencies including dev tools
+pip install -r requirements.txt
+
+# Run with auto-reload
+uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000 --reload
+
+# Run verification in another terminal
+./verify --verbose
+
+# Run tests
+pytest tests/ -v
+pytest --cov=wifi_densepose --cov-report=html
+```
+
+Rust:
+
+```bash
+cd rust-port/wifi-densepose-rs
+
+# Build in debug mode (faster compilation)
+cargo build
+
+# Run tests with output
+cargo test --workspace -- --nocapture
+
+# Watch mode (requires cargo-watch)
+cargo install cargo-watch
+cargo watch -x 'test --workspace' -x 'build --release'
+
+# Run benchmarks
+cargo bench --package wifi-densepose-signal
+```
+
+Both (visualization + API):
+
+```bash
+# Terminal 1: Start API server
+uvicorn v1.src.api.main:app --host 0.0.0.0 --port 8000 --reload
+
+# Terminal 2: Serve visualization
+python3 -m http.server 3000 --directory ui
+
+# Open http://localhost:3000/viz.html -- it connects to ws://localhost:8000/ws/pose
+```
+
+---
+
+## Appendix: Key File Locations
+
+| File | Purpose |
+|------|---------|
+| `./verify` | Trust kill switch -- one-command pipeline proof |
+| `Makefile` | `make verify`, `make verify-verbose`, `make verify-audit` |
+| `v1/requirements-lock.txt` | Pinned Python deps for hash reproducibility |
+| `requirements.txt` | Full Python deps (API server, torch, etc.) |
+| `v1/data/proof/verify.py` | Python verification script |
+| `v1/data/proof/sample_csi_data.json` | Deterministic reference signal |
+| `v1/data/proof/expected_features.sha256` | Published expected hash |
+| `v1/src/api/main.py` | FastAPI application entry point |
+| `v1/src/sensing/` | Commodity WiFi sensing module (RSSI) |
+| `rust-port/wifi-densepose-rs/Cargo.toml` | Rust workspace root |
+| `ui/viz.html` | Three.js 3D visualization |
+| `Dockerfile` | Multi-stage Docker build (dev/prod/test/security) |
+| `docker-compose.yml` | Development stack (Postgres, Redis, Prometheus, Grafana) |
+| `docker-compose.prod.yml` | Production stack (Swarm, secrets, resource limits) |
+| `docs/adr/ADR-009-rvf-wasm-runtime-edge-deployment.md` | WASM edge deployment architecture |
+| `docs/adr/ADR-012-esp32-csi-sensor-mesh.md` | ESP32 firmware and mesh specification |
+| `docs/adr/ADR-013-feature-level-sensing-commodity-gear.md` | Commodity WiFi (RSSI) sensing |

docs/research/remote-vital-sign-sensing-modalities.md

@@ -0,0 +1,110 @@
+# Remote Vital Sign Sensing: RF, Radar, and Quantum Modalities
+
+Beyond Wi-Fi DensePose-style sensing, there is active research and state-of-the-art (SOTA) work on remotely detecting people and physiological vital signs using RF/EM signals, radar, and quantum/quantum-inspired sensors. Below is a snapshot of current and emerging modalities, with research examples.
+
+---
+
+## RF-Based & Wireless Signal Approaches (Non-Optical)
+
+### 1. RF & Wi-Fi Channel Sensing
+
+Systems analyze perturbations in RF signals (e.g., changes in amplitude/phase) caused by human presence, motion, or micro-movement such as breathing or heartbeat:
+
+- **Wi-Fi CSI (Channel State Information)** can capture micro-movements from chest motion due to respiration and heartbeats by tracking subtle phase shifts in reflected packets. Applied in real-time vital sign monitoring and indoor tracking.
+- **RF signal variation** can encode gait, posture and motion biometric features for person identification and pose estimation without cameras or wearables.
+
+These methods are fundamentally passive RF sensing, relying on signal decomposition and ML to extract physiological signatures from ambient communication signals.
+
+---
+
+### 2. Millimeter-Wave & Ultra-Wideband Radar
+
+Active RF systems send high-frequency signals and analyze reflections:
+
+- **Millimeter-wave & FMCW radars** can detect sub-millimeter chest movements due to breathing and heartbeats remotely with high precision.
+- Researchers have extended this to **simultaneous multi-person vital sign estimation**, using phased-MIMO radar to isolate and track multiple subjects' breathing and heart rates.
+- **Impulse-Radio Ultra-Wideband (IR-UWB)** airborne radar prototypes are being developed for search-and-rescue sensing, extracting respiratory and heartbeat signals amid clutter.
+
+Radar-based approaches are among the most mature non-contact vital sign sensing technologies at range.
+
+---
+
+### 3. Through-Wall & Occluded Sensing
+
+Some advanced radars and RF systems can sense humans behind obstacles by analyzing micro-Doppler signatures and reflectometry:
+
+- Research surveys show **through-wall radar** and deep learning-based RF pose reconstruction for human activity and pose sensing without optical views.
+
+These methods go beyond presence detection to enable coarse body pose and action reconstruction.
+
+---
+
+## Optical & Vision-Based Non-Contact Sensing
+
+### 4. Remote Photoplethysmography (rPPG)
+
+Instead of RF, rPPG uses cameras to infer vital signs by analyzing subtle skin color changes due to blood volume pulses:
+
+- Cameras, including RGB and NIR sensor arrays, can estimate **heart rate, respiration rate, and even oxygenation** without contact.
+
+This is already used in some wellness and telemedicine systems.
+
+---
+
+## Quantum / Quantum-Inspired Approaches
+
+### 5. Quantum Radar and Quantum-Enhanced Remote Sensing
+
+Quantum radar (based on entanglement/correlations or quantum illumination) is under research:
+
+- **Quantum radar** aims to use quantum correlations to outperform classical radar in target detection at short ranges. Early designs have demonstrated proof of concept but remain limited to near-field/short distances — potential for biomedical scanning is discussed.
+- **Quantum-inspired computational imaging** and quantum sensors promise enhanced sensitivity, including in foggy, low visibility or internal sensing contexts.
+
+While full quantum remote vital sign sensing (like single-photon quantum radar scanning people's heartbeat) isn't yet operational, quantum sensors — especially atomic magnetometers and NV-centre devices — offer a path toward ultrasensitive biomedical field detection.
+
+### 6. Quantum Biomedical Instrumentation
+
+Parallel research on quantum imaging and quantum sensors aims to push biomedical detection limits:
+
+- Projects are funded to apply **quantum sensing and imaging in smart health environments**, potentially enabling unobtrusive physiological monitoring.
+- **Quantum enhancements in MRI** promise higher sensitivity for continuous physiological parameter imaging (temperature, heartbeat signatures) though mostly in controlled medical settings.
+
+These are quantum-sensor-enabled biomedical detection advances rather than direct RF remote sensing; practical deployment for ubiquitous vital sign detection is still emerging.
+
+---
+
+## Modality Comparison
+
+| Modality | Detects | Range | Privacy | Maturity |
+|----------|---------|-------|---------|----------|
+| Wi-Fi CSI Sensing | presence, respiration, coarse pose | indoor | high (non-visual) | early commercial |
+| mmWave / UWB Radar | respiration, heartbeat | meters | medium | mature research, niche products |
+| Through-wall RF | pose/activity thru occlusions | short-medium | high | research |
+| rPPG (optical) | HR, RR, SpO2 | line-of-sight | low | commercial |
+| Quantum Radar (lab) | target detection | very short | high | early research |
+| Quantum Sensors (biomedical) | field, magnetic signals | body-proximal | medium | R&D |
+
+---
+
+## Key Insights & State-of-Research
+
+- **RF and radar sensing** are the dominant SOTA methods for non-contact vital sign detection outside optical imaging. These use advanced signal processing and ML to extract micro-movement signatures.
+- **Quantum sensors** are showing promise for enhanced biomedical detection at finer scales — especially magnetic and other field sensing — but practical remote vital sign sensing (people at distance) is still largely research.
+- **Hybrid approaches** (RF + ML, quantum-inspired imaging) represent emerging research frontiers with potential breakthroughs in sensitivity and privacy.
+
+---
+
+## Relevance to WiFi-DensePose
+
+This project's signal processing pipeline (ADR-014) implements several of the core algorithms used across these modalities:
+
+| WiFi-DensePose Algorithm | Cross-Modality Application |
+|--------------------------|---------------------------|
+| Conjugate Multiplication (CSI ratio) | Phase sanitization for any multi-antenna RF system |
+| Hampel Filter | Outlier rejection in radar and UWB returns |
+| Fresnel Zone Model | Breathing detection applicable to mmWave and UWB |
+| CSI Spectrogram (STFT) | Time-frequency analysis used in all radar modalities |
+| Subcarrier Selection | Channel/frequency selection in OFDM and FMCW systems |
+| Body Velocity Profile | Doppler-velocity mapping used in mmWave and through-wall radar |
+
+The algorithmic foundations are shared across modalities — what differs is the carrier frequency, bandwidth, and hardware interface.

docs/research/wifi-sensing-ruvector-sota-2026.md

@@ -0,0 +1,298 @@
+# WiFi Sensing + Vector Intelligence: State of the Art and 20-Year Projection
+
+**Date:** 2026-02-28
+**Scope:** WiFi CSI-based human sensing, vector database signal intelligence (RuVector/HNSW), edge AI inference, post-quantum cryptography, and technology trajectory through 2046.
+
+---
+
+## 1. WiFi CSI Human Sensing: State of the Art (2023–2026)
+
+### 1.1 Foundational Work: DensePose From WiFi
+
+The seminal work by Geng, Huang, and De la Torre at Carnegie Mellon University ([arXiv:2301.00250](https://arxiv.org/abs/2301.00250), 2023) demonstrated that dense human pose correspondence can be estimated using WiFi signals alone. Their architecture maps CSI phase and amplitude to UV coordinates across 24 body regions, achieving performance comparable to image-based approaches.
+
+The pipeline consists of three stages:
+1. **Amplitude and phase sanitization** of raw CSI
+2. **Two-branch encoder-decoder network** translating sanitized CSI to 2D feature maps
+3. **Modified DensePose-RCNN** producing UV maps from the 2D features
+
+This work established that commodity WiFi routers contain sufficient spatial information for dense human pose recovery, without cameras.
+
+### 1.2 Multi-Person 3D Pose Estimation (CVPR 2024)
+
+Yan et al. presented **Person-in-WiFi 3D** at CVPR 2024 ([paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Yan_Person-in-WiFi_3D_End-to-End_Multi-Person_3D_Pose_Estimation_with_Wi-Fi_CVPR_2024_paper.pdf)), advancing the field from 2D to end-to-end multi-person 3D pose estimation using WiFi signals. This represents a significant leap — handling multiple subjects simultaneously in three dimensions using only wireless signals.
+
+### 1.3 Cross-Site Generalization (IEEE IoT Journal, 2024)
+
+Zhou et al. published **AdaPose** (IEEE Internet of Things Journal, 2024, vol. 11, pp. 40255–40267), addressing one of the critical challenges: cross-site generalization. WiFi sensing models trained in one environment often fail in others due to different multipath profiles. AdaPose demonstrates device-free human pose estimation that transfers across sites using commodity WiFi hardware.
+
+### 1.4 Lightweight Architectures (ECCV 2024)
+
+**HPE-Li** was presented at ECCV 2024 in Milan, introducing WiFi-enabled lightweight dual selective kernel convolution for human pose estimation. This work targets deployment on resource-constrained edge devices — a critical requirement for practical WiFi sensing systems.
+
+### 1.5 Subcarrier-Level Analysis (2025)
+
+**CSI-Channel Spatial Decomposition** (Electronics, February 2025, [MDPI](https://www.mdpi.com/2079-9292/14/4/756)) decomposes CSI spatial structure into dual-view observations — spatial direction and channel sensitivity — demonstrating that this decomposition is sufficient for unambiguous localization and identification. This work directly informs how subcarrier-level features should be extracted from CSI data.
+
+**Deciphering the Silent Signals** (Springer, 2025) applies explainable AI to understand which WiFi frequency components contribute most to pose estimation, providing critical insight into feature selection for signal processing pipelines.
+
+### 1.6 ESP32 CSI Sensing
+
+The Espressif ESP32 has emerged as a practical, affordable CSI sensing platform:
+
+| Metric | Result | Source |
+|--------|--------|--------|
+| Human identification accuracy | 88.9–94.5% | Gaiba & Bedogni, IEEE CCNC 2024 |
+| Through-wall HAR range | 18.5m across 5 rooms | [Springer, 2023](https://link.springer.com/chapter/10.1007/978-3-031-44137-0_4) |
+| On-device inference accuracy | 92.43% at 232ms latency | MDPI Sensors, 2025 |
+| Data augmentation improvement | 59.91% → 97.55% | EMD-based augmentation, 2025 |
+
+Key findings from ESP32 research:
+- **ESP32-S3** is the preferred variant due to improved processing power and AI instruction set support
+- **Directional biquad antennas** extend through-wall range significantly
+- **On-device DenseNet inference** is achievable at 232ms per frame on ESP32-S3
+- [Espressif ESP-CSI](https://github.com/espressif/esp-csi) provides official CSI collection tools
+
+### 1.7 Hardware Comparison for CSI
+
+| Parameter | ESP32-S3 | Intel 5300 | Atheros AR9580 |
+|-----------|----------|------------|----------------|
+| Subcarriers | 52–56 | 30 (compressed) | 56 (full) |
+| Antennas | 1–2 TX/RX | 3 TX/RX (MIMO) | 3 TX/RX (MIMO) |
+| Cost | $5–15 | $50–100 (discontinued) | $30–60 (discontinued) |
+| CSI quality | Consumer-grade | Research-grade | Research-grade |
+| Availability | In production | eBay only | eBay only |
+| Edge inference | Yes (on-chip) | Requires host PC | Requires host PC |
+| Through-wall range | 18.5m demonstrated | ~10m typical | ~15m typical |
+
+---
+
+## 2. Vector Databases for Signal Intelligence
+
+### 2.1 WiFi Fingerprinting as Vector Search
+
+WiFi fingerprinting is fundamentally a nearest-neighbor search problem. Rocamora and Ho (Expert Systems with Applications, November 2024, [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0957417424026691)) demonstrated that deep learning vector embeddings (d-vectors and i-vectors, adapted from speech processing) provide compact CSI fingerprint representations suitable for scalable retrieval.
+
+Their key insight: CSI fingerprints are high-dimensional vectors. The online positioning phase reduces to finding the nearest stored fingerprint vector to the current observation. This is exactly the problem HNSW solves.
+
+### 2.2 HNSW for Sub-Millisecond Signal Matching
+
+Hierarchical Navigable Small Worlds (HNSW) provides O(log n) approximate nearest-neighbor search through a layered proximity graph:
+
+- **Bottom layer**: Dense graph connecting all vectors
+- **Upper layers**: Sparse skip-list structure for fast navigation
+- **Search**: Greedy descent through sparse layers, bounded beam search at bottom
+
+For WiFi sensing, HNSW enables:
+- **Real-time fingerprint matching**: <1ms query at 100K stored fingerprints
+- **Environment adaptation**: Quickly find similar CSI patterns as the environment changes
+- **Multi-person disambiguation**: Separate overlapping CSI signatures by similarity
+
+### 2.3 RuVector's HNSW Implementation
+
+RuVector provides a Rust-native HNSW implementation with SIMD acceleration, supporting:
+- 329-dimensional CSI feature vectors (64 amplitude + 64 variance + 63 phase + 10 Doppler + 128 PSD)
+- PQ8 product quantization for 8x memory reduction
+- Hyperbolic embeddings (Poincaré ball) for hierarchical activity classification
+- Copy-on-write branching for environment-specific fingerprint databases
+
+### 2.4 Self-Learning Signal Intelligence (SONA)
+
+The Self-Optimizing Neural Architecture (SONA) in RuVector adapts pose estimation models online through:
+- **LoRA fine-tuning**: Only 0.56% of parameters (17,024 of 3M) are adapted per environment
+- **EWC++ regularization**: Prevents catastrophic forgetting of previously learned environments
+- **Feedback signals**: Temporal consistency, physical plausibility, multi-view agreement
+- **Adaptation latency**: <1ms per update cycle
+
+This enables a WiFi sensing system that improves its accuracy over time as it observes more data in a specific environment, without forgetting how to function in previously visited environments.
+
+---
+
+## 3. Edge AI and WASM Inference
+
+### 3.1 ONNX Runtime Web
+
+ONNX Runtime Web ([documentation](https://onnxruntime.ai/docs/tutorials/web/)) enables ML inference directly in browsers via WebAssembly:
+
+- **WASM backend**: Near-native CPU inference, multi-threading via SharedArrayBuffer, SIMD128 acceleration
+- **WebGPU backend**: GPU-accelerated inference (19x speedup on Segment Anything encoder)
+- **WebNN backend**: Hardware-neutral neural network acceleration
+
+Performance benchmarks (MobileNet V2):
+- WASM + SIMD + 2 threads: **3.4x speedup** over plain WASM
+- WebGPU: **19x speedup** for attention-heavy models
+
+### 3.2 Rust-Native WASM Inference
+
+[WONNX](https://github.com/webonnx/wonnx) provides a GPU-accelerated ONNX runtime written entirely in Rust, compiled to WASM. This aligns directly with the wifi-densepose Rust architecture and enables:
+- Single-binary deployment as `.wasm` module
+- WebGPU acceleration when available
+- CPU fallback via WASM for older devices
+
+### 3.3 Model Quantization for Edge
+
+| Quantization | Size | Accuracy Impact | Target |
+|-------------|------|----------------|--------|
+| Float32 | 12MB | Baseline | Server |
+| Float16 | 6MB | <0.5% loss | Tablets |
+| Int8 (PTQ) | 3MB | <2% loss | Browser/mobile |
+| Int4 (GPTQ) | 1.5MB | <5% loss | ESP32/IoT |
+
+The wifi-densepose WASM module targets 5.5KB runtime + 0.7–62MB container depending on profile (IoT through Field deployment).
+
+### 3.4 RVF Edge Containers
+
+RuVector's RVF (Cognitive Container) format packages model weights, HNSW index, fingerprint vectors, and WASM runtime into a single deployable file:
+
+| Profile | Container Size | Boot Time | Target |
+|---------|---------------|-----------|--------|
+| IoT | ~0.7 MB | <200ms | ESP32 |
+| Browser | ~10 MB | ~125ms | Chrome/Firefox |
+| Mobile | ~6 MB | ~150ms | iOS/Android |
+| Field | ~62 MB | ~200ms | Disaster response |
+
+---
+
+## 4. Post-Quantum Cryptography for Sensor Networks
+
+### 4.1 NIST PQC Standards (Finalized August 2024)
+
+NIST released three finalized standards ([announcement](https://www.nist.gov/news-events/news/2024/08/nist-releases-first-3-finalized-post-quantum-encryption-standards)):
+
+| Standard | Algorithm | Type | Signature Size | Use Case |
+|----------|-----------|------|---------------|----------|
+| FIPS 203 (ML-KEM) | CRYSTALS-Kyber | Key encapsulation | 1,088 bytes | Key exchange |
+| FIPS 204 (ML-DSA) | CRYSTALS-Dilithium | Digital signature | 2,420 bytes (ML-DSA-65) | General signing |
+| FIPS 205 (SLH-DSA) | SPHINCS+ | Hash-based signature | 7,856 bytes | Conservative backup |
+
+### 4.2 IoT Sensor Considerations
+
+For bandwidth-constrained WiFi sensor mesh networks:
+- **ML-DSA-65** signature size (2,420 bytes) is feasible for ESP32 UDP streams (~470 byte CSI frames + 2.4KB signature = ~2.9KB per authenticated frame)
+- **FN-DSA** (FALCON, expected 2026–2027) will offer smaller signatures (~666 bytes) but requires careful Gaussian sampling implementation
+- **Hybrid approach**: ML-DSA + Ed25519 dual signatures during transition period (as specified in ADR-007)
+
+### 4.3 Transition Timeline
+
+| Milestone | Date |
+|-----------|------|
+| NIST PQC standards finalized | August 2024 |
+| First post-quantum certificates | 2026 |
+| Browser-wide trust | 2027 |
+| Quantum-vulnerable algorithms deprecated | 2030 |
+| Full removal from NIST standards | 2035 |
+
+WiFi-DensePose's early adoption of ML-DSA-65 positions it ahead of the deprecation curve, ensuring sensor mesh data integrity remains quantum-resistant.
+
+---
+
+## 5. Twenty-Year Projection (2026–2046)
+
+### 5.1 WiFi Evolution and Sensing Resolution
+
+#### WiFi 7 (802.11be) — Available Now
+- **320 MHz channels** with up to 3,984 CSI tones (vs. 56 on ESP32 today)
+- **16×16 MU-MIMO** spatial streams (vs. 2×2 on ESP32)
+- **Sub-nanosecond RTT resolution** for centimeter-level positioning
+- Built-in sensing capabilities in PHY/MAC layer
+
+WiFi 7's 320 MHz bandwidth provides ~71x more CSI tones than current ESP32 implementations. This alone transforms sensing resolution.
+
+#### WiFi 8 (802.11bn) — Expected ~2028
+- Operations across **sub-7 GHz, 45 GHz, and 60 GHz** bands ([survey](https://www.sciencedirect.com/science/article/abs/pii/S1389128625005572))
+- **WLAN sensing as a core PHY/MAC capability** (not an add-on)
+- Formalized sensing frames and measurement reporting
+- Higher-order MIMO configurations
+
+#### Projected WiFi Sensing Resolution by Decade
+
+| Timeframe | WiFi Gen | Subcarriers | MIMO | Spatial Resolution | Sensing Capability |
+|-----------|----------|------------|------|-------------------|-------------------|
+| 2024 | WiFi 6 (ESP32) | 56 | 2×2 | ~1m | Presence, coarse motion |
+| 2025 | WiFi 7 | 3,984 | 16×16 | ~10cm | Pose, gestures, respiration |
+| ~2028 | WiFi 8 | 10,000+ | 32×32 | ~2cm | Fine motor, vital signs |
+| ~2033 | WiFi 9* | 20,000+ | 64×64 | ~5mm | Medical-grade monitoring |
+| ~2040 | WiFi 10* | 50,000+ | 128×128 | ~1mm | Sub-dermal sensing |
+
+*Projected based on historical doubling patterns in IEEE 802.11 standards.
+
+### 5.2 Medical-Grade Vital Signs via Ambient WiFi
+
+**Current state (2026):** Breathing detection at 85–95% accuracy with ESP32 mesh; heartbeat detection marginal and placement-sensitive.
+
+**Projected trajectory:**
+- **2028–2030**: WiFi 8's formalized sensing + 60 GHz millimeter-wave enables reliable heartbeat detection at ~95% accuracy. Hospital rooms equipped with sensing APs replace some wired patient monitors.
+- **2032–2035**: Sub-centimeter Doppler resolution enables blood flow visualization, glucose monitoring via micro-Doppler spectroscopy. FDA Class II clearance for ambient WiFi vital signs monitoring.
+- **2038–2042**: Ambient WiFi provides continuous, passive health monitoring equivalent to today's wearable devices. Elderly care facilities use WiFi sensing for fall detection, sleep quality, and early disease indicators.
+- **2042–2046**: WiFi sensing achieves sub-millimeter resolution. Non-invasive blood pressure, heart rhythm analysis, and respiratory function testing become standard ambient measurements. Medical imaging grade penetration through walls.
+
+### 5.3 Smart City Mesh Sensing at Scale
+
+**Projected deployment:**
+- **2028**: Major cities deploy WiFi 7/8 infrastructure with integrated sensing. Pedestrian flow monitoring replaces camera-based surveillance in privacy-sensitive zones.
+- **2032**: Urban-scale mesh sensing networks provide real-time occupancy maps of public spaces, transit systems, and emergency shelters. Disaster response systems (like wifi-densepose-mat) operate as permanent city infrastructure.
+- **2038**: Full-city coverage enables ambient intelligence: traffic optimization, crowd management, emergency detection — all without cameras, using only the WiFi infrastructure already deployed for connectivity.
+
+### 5.4 Vector Intelligence at Scale
+
+**Projected evolution of HNSW-based signal intelligence:**
+- **2028**: HNSW indexes of 10M+ CSI fingerprints per city zone, enabling instant environment recognition and person identification across any WiFi-equipped space. RVF containers store environment-specific models that adapt in <1ms.
+- **2032**: Federated learning across city-scale HNSW indexes. Each building's local index contributes to a global model without sharing raw CSI data. Post-quantum signatures ensure tamper-evident data provenance.
+- **2038**: Continuous self-learning via SONA at city scale. The system improves autonomously from billions of daily observations. EWC++ prevents catastrophic forgetting across seasonal and environmental changes.
+- **2042**: Exascale vector indexes (~1T fingerprints) with sub-microsecond queries via quantum-classical hybrid search. WiFi sensing becomes an ambient utility like electricity — invisible, always-on, universally available.
+
+### 5.5 Privacy-Preserving Sensing Architecture
+
+The critical challenge for large-scale WiFi sensing is privacy. Projected solutions:
+
+- **2026–2028**: On-device processing (ESP32/edge WASM) ensures raw CSI never leaves the local network. RVF containers provide self-contained inference without cloud dependency.
+- **2030–2033**: Homomorphic encryption enables cloud-based CSI processing without decryption. Federated learning trains global models without sharing local data.
+- **2035–2040**: Post-quantum cryptography secures all sensor mesh communication against quantum adversaries. Zero-knowledge proofs enable presence verification without revealing identity.
+- **2040–2046**: Fully decentralized sensing with CRDT-based consensus (no central authority). Individuals control their own sensing data via personal RVF containers signed with post-quantum keys.
+
+---
+
+## 6. Implications for WiFi-DensePose + RuVector
+
+The convergence of these technologies creates a clear path for wifi-densepose:
+
+1. **Near-term (2026–2028)**: ESP32 mesh with feature-level fusion provides practical presence/motion detection. RuVector's HNSW enables real-time fingerprint matching. WASM edge deployment eliminates cloud dependency. Trust kill switch proves pipeline authenticity.
+
+2. **Medium-term (2028–2032)**: WiFi 7/8 CSI (3,984+ tones) transforms sensing from coarse presence to fine-grained pose estimation. SONA adaptation makes the system self-improving. Post-quantum signatures secure the sensor mesh.
+
+3. **Long-term (2032–2046)**: WiFi sensing becomes ambient infrastructure. Medical-grade monitoring replaces wearables. City-scale vector intelligence operates autonomously. The architecture established today — RVF containers, HNSW indexes, witness chains, distributed consensus — scales directly to this future.
+
+The fundamental insight: **the software architecture for ambient WiFi sensing at scale is being built now, using technology available today.** The hardware (WiFi 7/8, faster silicon) will arrive to fill the resolution gap. The algorithms (HNSW, SONA, EWC++) are already proven. The cryptography (ML-DSA, SLH-DSA) is standardized. What matters is building the correct abstractions — and that is exactly what the RuVector integration provides.
+
+---
+
+## References
+
+### WiFi Sensing
+- [DensePose From WiFi](https://arxiv.org/abs/2301.00250) — Geng, Huang, De la Torre (CMU, 2023)
+- [Person-in-WiFi 3D](https://openaccess.thecvf.com/content/CVPR2024/papers/Yan_Person-in-WiFi_3D_End-to-End_Multi-Person_3D_Pose_Estimation_with_Wi-Fi_CVPR_2024_paper.pdf) — Yan et al. (CVPR 2024)
+- [CSI-Channel Spatial Decomposition](https://www.mdpi.com/2079-9292/14/4/756) — Electronics, Feb 2025
+- [WiFi CSI-Based Through-Wall HAR with ESP32](https://link.springer.com/chapter/10.1007/978-3-031-44137-0_4) — Springer, 2023
+- [Espressif ESP-CSI](https://github.com/espressif/esp-csi) — Official CSI tools
+- [WiFi Sensing Survey](https://dl.acm.org/doi/10.1145/3705893) — ACM Computing Surveys, 2025
+- [WiFi-Based Human Identification Survey](https://pmc.ncbi.nlm.nih.gov/articles/PMC11479185/) — PMC, 2024
+
+### Vector Search & Fingerprinting
+- [WiFi CSI Fingerprinting with Vector Embedding](https://www.sciencedirect.com/science/article/abs/pii/S0957417424026691) — Rocamora & Ho (Expert Systems with Applications, 2024)
+- [HNSW Explained](https://milvus.io/blog/understand-hierarchical-navigable-small-worlds-hnsw-for-vector-search.md) — Milvus Blog
+- [WiFi Fingerprinting Survey](https://pmc.ncbi.nlm.nih.gov/articles/PMC12656469/) — PMC, 2024
+
+### Edge AI & WASM
+- [ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/) — Microsoft
+- [WONNX: Rust ONNX Runtime](https://github.com/webonnx/wonnx) — WebGPU-accelerated
+- [In-Browser Deep Learning on Edge Devices](https://arxiv.org/html/2309.08978v2) — arXiv, 2023
+
+### Post-Quantum Cryptography
+- [NIST PQC Standards](https://www.nist.gov/news-events/news/2024/08/nist-releases-first-3-finalized-post-quantum-encryption-standards) — FIPS 203/204/205 (August 2024)
+- [NIST IR 8547: PQC Transition](https://nvlpubs.nist.gov/nistpubs/ir/2024/NIST.IR.8547.ipd.pdf) — Transition timeline
+- [State of PQC Internet 2025](https://blog.cloudflare.com/pq-2025/) — Cloudflare
+
+### WiFi Evolution
+- [Wi-Fi 7 (802.11be)](https://en.wikipedia.org/wiki/Wi-Fi_7) — Finalized July 2025
+- [From Wi-Fi 7 to Wi-Fi 8 Survey](https://www.sciencedirect.com/science/article/abs/pii/S1389128625005572) — ScienceDirect, 2025
+- [Wi-Fi 7 320MHz Channels](https://www.netgear.com/hub/network/wifi-7-320mhz-channels/) — Netgear

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@@ -1233,6 +1327,12 @@ dependencies = [
  "byteorder",
 ]
 
+[[package]]
+name = "hashbrown"
+version = "0.14.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1"
+
 [[package]]
 name = "hashbrown"
 version = "0.15.5"
@@ -1244,6 +1344,12 @@ dependencies = [
  "foldhash",
 ]
 
+[[package]]
+name = "hashbrown"
+version = "0.16.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "841d1cc9bed7f9236f321df977030373f4a4163ae1a7dbfe1a51a2c1a51d9100"
+
 [[package]]
 name = "heapless"
 version = "0.6.1"
@@ -1418,6 +1524,16 @@ dependencies = [
  "cc",
 ]
 
+[[package]]
+name = "indexmap"
+version = "2.13.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7714e70437a7dc3ac8eb7e6f8df75fd8eb422675fc7678aff7364301092b1017"
+dependencies = [
+ "equivalent",
+ "hashbrown 0.16.1",
+]
+
 [[package]]
 name = "indicatif"
 version = "0.17.11"
@@ -1630,6 +1746,26 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
+[[package]]
+name = "munge"
+version = "0.4.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5e17401f259eba956ca16491461b6e8f72913a0a114e39736ce404410f915a0c"
+dependencies = [
+ "munge_macro",
+]
+
+[[package]]
+name = "munge_macro"
+version = "0.4.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4568f25ccbd45ab5d5603dc34318c1ec56b117531781260002151b8530a9f931"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
 [[package]]
 name = "native-tls"
 version = "0.2.14"
@@ -1661,6 +1797,22 @@ dependencies = [
  "serde",
 ]
 
+[[package]]
+name = "ndarray"
+version = "0.16.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "882ed72dce9365842bf196bdeedf5055305f11fc8c03dee7bb0194a6cad34841"
+dependencies = [
+ "matrixmultiply",
+ "num-complex",
+ "num-integer",
+ "num-traits",
+ "portable-atomic",
+ "portable-atomic-util",
+ "rawpointer",
+ "serde",
+]
+
 [[package]]
 name = "ndarray"
 version = "0.17.2"
@@ -1676,6 +1828,20 @@ dependencies = [
  "rawpointer",
 ]
 
+[[package]]
+name = "ndarray-npy"
+version = "0.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "f85776816e34becd8bd9540818d7dc77bf28307f3b3dcc51cc82403c6931680c"
+dependencies = [
+ "byteorder",
+ "ndarray 0.15.6",
+ "num-complex",
+ "num-traits",
+ "py_literal",
+ "zip 0.5.13",
+]
+
 [[package]]
 name = "nom"
 version = "7.1.3"
@@ -1701,6 +1867,16 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
+[[package]]
+name = "num-bigint"
+version = "0.4.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a5e44f723f1133c9deac646763579fdb3ac745e418f2a7af9cd0c431da1f20b9"
+dependencies = [
+ "num-integer",
+ "num-traits",
+]
+
 [[package]]
 name = "num-complex"
 version = "0.4.6"
@@ -1814,6 +1990,15 @@ dependencies = [
  "vcpkg",
 ]
 
+[[package]]
+name = "ordered-float"
+version = "4.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7bb71e1b3fa6ca1c61f383464aaf2bb0e2f8e772a1f01d486832464de363b951"
+dependencies = [
+ "num-traits",
+]
+
 [[package]]
 name = "ort"
 version = "2.0.0-rc.11"
@@ -1924,6 +2109,59 @@ version = "2.3.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9b4f627cb1b25917193a259e49bdad08f671f8d9708acfd5fe0a8c1455d87220"
 
+[[package]]
+name = "pest"
+version = "2.8.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e0848c601009d37dfa3430c4666e147e49cdcf1b92ecd3e63657d8a5f19da662"
+dependencies = [
+ "memchr",
+ "ucd-trie",
+]
+
+[[package]]
+name = "pest_derive"
+version = "2.8.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "11f486f1ea21e6c10ed15d5a7c77165d0ee443402f0780849d1768e7d9d6fe77"
+dependencies = [
+ "pest",
+ "pest_generator",
+]
+
+[[package]]
+name = "pest_generator"
+version = "2.8.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8040c4647b13b210a963c1ed407c1ff4fdfa01c31d6d2a098218702e6664f94f"
+dependencies = [
+ "pest",
+ "pest_meta",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
+[[package]]
+name = "pest_meta"
+version = "2.8.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "89815c69d36021a140146f26659a81d6c2afa33d216d736dd4be5381a7362220"
+dependencies = [
+ "pest",
+ "sha2",
+]
+
+[[package]]
+name = "petgraph"
+version = "0.6.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "b4c5cc86750666a3ed20bdaf5ca2a0344f9c67674cae0515bec2da16fbaa47db"
+dependencies = [
+ "fixedbitset",
+ "indexmap",
+]
+
 [[package]]
 name = "pin-project-lite"
 version = "0.2.16"
@@ -2091,6 +2329,26 @@ dependencies = [
  "unarray",
 ]
 
+[[package]]
+name = "ptr_meta"
+version = "0.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0b9a0cf95a1196af61d4f1cbdab967179516d9a4a4312af1f31948f8f6224a79"
+dependencies = [
+ "ptr_meta_derive",
+]
+
+[[package]]
+name = "ptr_meta_derive"
+version = "0.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7347867d0a7e1208d93b46767be83e2b8f978c3dad35f775ac8d8847551d6fe1"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
 [[package]]
 name = "pulp"
 version = "0.18.22"
@@ -2103,6 +2361,19 @@ dependencies = [
  "reborrow",
 ]
 
+[[package]]
+name = "py_literal"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "102df7a3d46db9d3891f178dcc826dc270a6746277a9ae6436f8d29fd490a8e1"
+dependencies = [
+ "num-bigint",
+ "num-complex",
+ "num-traits",
+ "pest",
+ "pest_derive",
+]
+
 [[package]]
 name = "quick-error"
 version = "1.2.3"
@@ -2124,6 +2395,15 @@ version = "5.3.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "69cdb34c158ceb288df11e18b4bd39de994f6657d83847bdffdbd7f346754b0f"
 
+[[package]]
+name = "rancor"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a063ea72381527c2a0561da9c80000ef822bdd7c3241b1cc1b12100e3df081ee"
+dependencies = [
+ "ptr_meta",
+]
+
 [[package]]
 name = "rand"
 version = "0.8.5"
@@ -2291,6 +2571,55 @@ version = "0.8.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7a2d987857b319362043e95f5353c0535c1f58eec5336fdfcf626430af7def58"
 
+[[package]]
+name = "rend"
+version = "0.5.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cadadef317c2f20755a64d7fdc48f9e7178ee6b0e1f7fce33fa60f1d68a276e6"
+dependencies = [
+ "bytecheck",
+]
+
+[[package]]
+name = "rkyv"
+version = "0.8.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1a30e631b7f4a03dee9056b8ef6982e8ba371dd5bedb74d3ec86df4499132c70"
+dependencies = [
+ "bytecheck",
+ "bytes",
+ "hashbrown 0.16.1",
+ "indexmap",
+ "munge",
+ "ptr_meta",
+ "rancor",
+ "rend",
+ "rkyv_derive",
+ "tinyvec",
+ "uuid",
+]
+
+[[package]]
+name = "rkyv_derive"
+version = "0.8.15"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8100bb34c0a1d0f907143db3149e6b4eea3c33b9ee8b189720168e818303986f"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
+[[package]]
+name = "roaring"
+version = "0.10.12"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "19e8d2cfa184d94d0726d650a9f4a1be7f9b76ac9fdb954219878dc00c1c1e7b"
+dependencies = [
+ "bytemuck",
+ "byteorder",
+]
+
 [[package]]
 name = "robust"
 version = "1.2.0"
@@ -2421,6 +2750,95 @@ dependencies = [
  "wait-timeout",
 ]
 
+[[package]]
+name = "ruvector-attention"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "cb4233c1cecd0ea826d95b787065b398489328885042247ff5ffcbb774e864ff"
+dependencies = [
+ "rand 0.8.5",
+ "rayon",
+ "serde",
+ "thiserror 1.0.69",
+]
+
+[[package]]
+name = "ruvector-attn-mincut"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6c8ec5e03cc7a435945c81f1b151a2bc5f64f2206bf50150cab0f89981ce8c94"
+dependencies = [
+ "serde",
+ "serde_json",
+ "sha2",
+]
+
+[[package]]
+name = "ruvector-core"
+version = "2.0.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "dc7bc95e3682430c27228d7bc694ba9640cd322dde1bd5e7c9cf96a16afb4ca1"
+dependencies = [
+ "anyhow",
+ "bincode",
+ "chrono",
+ "dashmap",
+ "ndarray 0.16.1",
+ "once_cell",
+ "parking_lot",
+ "rand 0.8.5",
+ "rand_distr 0.4.3",
+ "rkyv",
+ "serde",
+ "serde_json",
+ "thiserror 2.0.18",
+ "tracing",
+ "uuid",
+]
+
+[[package]]
+name = "ruvector-mincut"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d62e10cbb7d80b1e2b72d55c1e3eb7f0c4c5e3f31984bc3baa9b7a02700741e"
+dependencies = [
+ "anyhow",
+ "crossbeam",
+ "dashmap",
+ "ordered-float",
+ "parking_lot",
+ "petgraph",
+ "rand 0.8.5",
+ "rayon",
+ "roaring",
+ "ruvector-core",
+ "serde",
+ "serde_json",
+ "thiserror 2.0.18",
+ "tracing",
+]
+
+[[package]]
+name = "ruvector-solver"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ce69cbde4ee5747281edb1d987a8292940397723924262b6218fc19022cbf687"
+dependencies = [
+ "dashmap",
+ "getrandom 0.2.17",
+ "parking_lot",
+ "rand 0.8.5",
+ "serde",
+ "thiserror 2.0.18",
+ "tracing",
+]
+
+[[package]]
+name = "ruvector-temporal-tensor"
+version = "2.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "178f93f84a4a72c582026a45d9b8710acf188df4a22a25434c5dbba1df6c4cac"
+
 [[package]]
 name = "ryu"
 version = "1.0.22"
@@ -2571,6 +2989,15 @@ dependencies = [
  "serde_core",
 ]
 
+[[package]]
+name = "serde_spanned"
+version = "0.6.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bf41e0cfaf7226dca15e8197172c295a782857fcb97fad1808a166870dee75a3"
+dependencies = [
+ "serde",
+]
+
 [[package]]
 name = "serde_urlencoded"
 version = "0.7.1"
@@ -2636,6 +3063,12 @@ version = "0.3.8"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e320a6c5ad31d271ad523dcf3ad13e2767ad8b1cb8f047f75a8aeaf8da139da2"
 
+[[package]]
+name = "simdutf8"
+version = "0.1.5"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e3a9fe34e3e7a50316060351f37187a3f546bce95496156754b601a5fa71b76e"
+
 [[package]]
 name = "slab"
 version = "0.4.11"
@@ -2675,7 +3108,7 @@ version = "2.15.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fb313e1c8afee5b5647e00ee0fe6855e3d529eb863a0fdae1d60006c4d1e9990"
 dependencies = [
- "hashbrown",
+ "hashbrown 0.15.5",
  "num-traits",
  "robust",
  "smallvec",
@@ -2763,7 +3196,7 @@ dependencies = [
  "byteorder",
  "enum-as-inner",
  "libc",
- "thiserror",
+ "thiserror 1.0.69",
  "walkdir",
 ]
 
@@ -2805,9 +3238,9 @@ dependencies = [
  "ndarray 0.15.6",
  "rand 0.8.5",
  "safetensors 0.3.3",
- "thiserror",
+ "thiserror 1.0.69",
  "torch-sys",
- "zip",
+ "zip 0.6.6",
 ]
 
 [[package]]
@@ -2835,7 +3268,16 @@ version = "1.0.69"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b6aaf5339b578ea85b50e080feb250a3e8ae8cfcdff9a461c9ec2904bc923f52"
 dependencies = [
- "thiserror-impl",
+ "thiserror-impl 1.0.69",
+]
+
+[[package]]
+name = "thiserror"
+version = "2.0.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "4288b5bcbc7920c07a1149a35cf9590a2aa808e0bc1eafaade0b80947865fbc4"
+dependencies = [
+ "thiserror-impl 2.0.18",
 ]
 
 [[package]]
@@ -2849,6 +3291,17 @@ dependencies = [
  "syn 2.0.114",
 ]
 
+[[package]]
+name = "thiserror-impl"
+version = "2.0.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ebc4ee7f67670e9b64d05fa4253e753e016c6c95ff35b89b7941d6b856dec1d5"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.114",
+]
+
 [[package]]
 name = "thread_local"
 version = "1.1.9"
@@ -2887,6 +3340,21 @@ dependencies = [
  "serde_json",
 ]
 
+[[package]]
+name = "tinyvec"
+version = "1.10.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bfa5fdc3bce6191a1dbc8c02d5c8bffcf557bafa17c124c5264a458f1b0613fa"
+dependencies = [
+ "tinyvec_macros",
+]
+
+[[package]]
+name = "tinyvec_macros"
+version = "0.1.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
+
 [[package]]
 name = "tokio"
 version = "1.49.0"
@@ -2949,6 +3417,47 @@ dependencies = [
  "tungstenite",
 ]
 
+[[package]]
+name = "toml"
+version = "0.8.23"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "dc1beb996b9d83529a9e75c17a1686767d148d70663143c7854d8b4a09ced362"
+dependencies = [
+ "serde",
+ "serde_spanned",
+ "toml_datetime",
+ "toml_edit",
+]
+
+[[package]]
+name = "toml_datetime"
+version = "0.6.11"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "22cddaf88f4fbc13c51aebbf5f8eceb5c7c5a9da2ac40a13519eb5b0a0e8f11c"
+dependencies = [
+ "serde",
+]
+
+[[package]]
+name = "toml_edit"
+version = "0.22.27"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "41fe8c660ae4257887cf66394862d21dbca4a6ddd26f04a3560410406a2f819a"
+dependencies = [
+ "indexmap",
+ "serde",
+ "serde_spanned",
+ "toml_datetime",
+ "toml_write",
+ "winnow",
+]
+
+[[package]]
+name = "toml_write"
+version = "0.1.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5d99f8c9a7727884afe522e9bd5edbfc91a3312b36a77b5fb8926e4c31a41801"
+
 [[package]]
 name = "torch-sys"
 version = "0.14.0"
@@ -2958,7 +3467,7 @@ dependencies = [
  "anyhow",
  "cc",
  "libc",
- "zip",
+ "zip 0.6.6",
 ]
 
 [[package]]
@@ -3088,7 +3597,7 @@ dependencies = [
  "log",
  "rand 0.8.5",
  "sha1",
- "thiserror",
+ "thiserror 1.0.69",
  "utf-8",
 ]
 
@@ -3098,6 +3607,12 @@ version = "1.19.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "562d481066bde0658276a35467c4af00bdc6ee726305698a55b86e61d7ad82bb"
 
+[[package]]
+name = "ucd-trie"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2896d95c02a80c6d6a5d6e953d479f5ddf2dfdb6a244441010e373ac0fb88971"
+
 [[package]]
 name = "unarray"
 version = "0.1.4"
@@ -3122,6 +3637,12 @@ version = "0.2.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b4ac048d71ede7ee76d585517add45da530660ef4390e49b098733c6e897f254"
 
+[[package]]
+name = "unty"
+version = "0.0.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6d49784317cd0d1ee7ec5c716dd598ec5b4483ea832a2dced265471cc0f690ae"
+
 [[package]]
 name = "ureq"
 version = "3.1.4"
@@ -3194,6 +3715,12 @@ version = "0.9.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
 
+[[package]]
+name = "virtue"
+version = "0.0.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "051eb1abcf10076295e815102942cc58f9d5e3b4560e46e53c21e8ff6f3af7b1"
+
 [[package]]
 name = "vte"
 version = "0.10.1"
@@ -3400,7 +3927,7 @@ dependencies = [
  "serde_json",
  "tabled",
  "tempfile",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "tracing",
  "tracing-subscriber",
@@ -3424,7 +3951,7 @@ dependencies = [
  "proptest",
  "serde",
  "serde_json",
- "thiserror",
+ "thiserror 1.0.69",
  "uuid",
 ]
 
@@ -3435,6 +3962,15 @@ version = "0.1.0"
 [[package]]
 name = "wifi-densepose-hardware"
 version = "0.1.0"
+dependencies = [
+ "approx",
+ "byteorder",
+ "chrono",
+ "serde",
+ "serde_json",
+ "thiserror 1.0.69",
+ "tracing",
+]
 
 [[package]]
 name = "wifi-densepose-mat"
@@ -3453,9 +3989,11 @@ dependencies = [
  "parking_lot",
  "proptest",
  "rustfft",
+ "ruvector-solver",
+ "ruvector-temporal-tensor",
  "serde",
  "serde_json",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "tokio-test",
  "tracing",
@@ -3484,7 +4022,7 @@ dependencies = [
  "serde_json",
  "tch",
  "tempfile",
- "thiserror",
+ "thiserror 1.0.69",
  "tokio",
  "tracing",
 ]
@@ -3500,12 +4038,54 @@ dependencies = [
  "num-traits",
  "proptest",
  "rustfft",
+ "ruvector-attention",
+ "ruvector-attn-mincut",
+ "ruvector-mincut",
+ "ruvector-solver",
  "serde",
  "serde_json",
- "thiserror",
+ "thiserror 1.0.69",
  "wifi-densepose-core",
 ]
 
+[[package]]
+name = "wifi-densepose-train"
+version = "0.1.0"
+dependencies = [
+ "anyhow",
+ "approx",
+ "chrono",
+ "clap",
+ "criterion",
+ "csv",
+ "indicatif",
+ "memmap2",
+ "ndarray 0.15.6",
+ "ndarray-npy",
+ "num-complex",
+ "num-traits",
+ "petgraph",
+ "proptest",
+ "ruvector-attention",
+ "ruvector-attn-mincut",
+ "ruvector-mincut",
+ "ruvector-solver",
+ "ruvector-temporal-tensor",
+ "serde",
+ "serde_json",
+ "sha2",
+ "tch",
+ "tempfile",
+ "thiserror 1.0.69",
+ "tokio",
+ "toml",
+ "tracing",
+ "tracing-subscriber",
+ "walkdir",
+ "wifi-densepose-nn",
+ "wifi-densepose-signal",
+]
+
 [[package]]
 name = "wifi-densepose-wasm"
 version = "0.1.0"
@@ -3774,6 +4354,15 @@ version = "0.53.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "d6bbff5f0aada427a1e5a6da5f1f98158182f26556f345ac9e04d36d0ebed650"
 
+[[package]]
+name = "winnow"
+version = "0.7.14"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5a5364e9d77fcdeeaa6062ced926ee3381faa2ee02d3eb83a5c27a8825540829"
+dependencies = [
+ "memchr",
+]
+
 [[package]]
 name = "wit-bindgen"
 version = "0.46.0"
@@ -3851,6 +4440,18 @@ version = "1.8.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b97154e67e32c85465826e8bcc1c59429aaaf107c1e4a9e53c8d8ccd5eff88d0"
 
+[[package]]
+name = "zip"
+version = "0.5.13"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "93ab48844d61251bb3835145c521d88aa4031d7139e8485990f60ca911fa0815"
+dependencies = [
+ "byteorder",
+ "crc32fast",
+ "flate2",
+ "thiserror 1.0.69",
+]
+
 [[package]]
 name = "zip"
 version = "0.6.6"

rust-port/wifi-densepose-rs/Cargo.toml

@@ -11,6 +11,7 @@ members = [
     "crates/wifi-densepose-wasm",
     "crates/wifi-densepose-cli",
     "crates/wifi-densepose-mat",
+    "crates/wifi-densepose-train",
 ]
 
 [workspace.package]
@@ -73,15 +74,37 @@ getrandom = { version = "0.2", features = ["js"] }
 serialport = "4.3"
 pcap = "1.1"
 
+# Graph algorithms (for min-cut assignment in metrics)
+petgraph = "0.6"
+
+# Data loading
+ndarray-npy = "0.8"
+walkdir = "2.4"
+
+# Hashing (for proof)
+sha2 = "0.10"
+
+# CSV logging
+csv = "1.3"
+
+# Progress bars
+indicatif = "0.17"
+
+# CLI
+clap = { version = "4.4", features = ["derive"] }
+
 # Testing
 criterion = { version = "0.5", features = ["html_reports"] }
 proptest = "1.4"
 mockall = "0.12"
 wiremock = "0.5"
 
-# ruvector integration
-# ruvector-core = "0.1"
-# ruvector-data-framework = "0.1"
+# ruvector integration (all at v2.0.4 — published on crates.io)
+ruvector-mincut = "2.0.4"
+ruvector-attn-mincut = "2.0.4"
+ruvector-temporal-tensor = "2.0.4"
+ruvector-solver = "2.0.4"
+ruvector-attention = "2.0.4"
 
 # Internal crates
 wifi-densepose-core = { path = "crates/wifi-densepose-core" }

rust-port/wifi-densepose-rs/crates/wifi-densepose-core/src/types.rs

@@ -172,16 +172,6 @@ impl Confidence {
 
     /// Creates a confidence value without validation (for internal use).
     ///
-    /// # Safety
-    ///
-    /// The caller must ensure the value is in [0.0, 1.0].
-    #[must_use]
-    #[allow(dead_code)]
-    pub(crate) fn new_unchecked(value: f32) -> Self {
-        debug_assert!((0.0..=1.0).contains(&value));
-        Self(value)
-    }
-
     /// Returns the raw confidence value.
     #[must_use]
     pub fn value(&self) -> f32 {
@@ -1009,7 +999,12 @@ impl PoseEstimate {
     pub fn highest_confidence_person(&self) -> Option<&PersonPose> {
         self.persons
             .iter()
-            .max_by(|a, b| a.confidence.value().partial_cmp(&b.confidence.value()).unwrap())
+            .max_by(|a, b| {
+                a.confidence
+                    .value()
+                    .partial_cmp(&b.confidence.value())
+                    .unwrap_or(std::cmp::Ordering::Equal)
+            })
     }
 }
 

rust-port/wifi-densepose-rs/crates/wifi-densepose-core/src/utils.rs

@@ -98,8 +98,11 @@ pub fn moving_average(data: &Array1<f64>, window_size: usize) -> Array1<f64> {
     let mut result = Array1::zeros(data.len());
     let half_window = window_size / 2;
 
-    // Safe unwrap: ndarray Array1 is always contiguous
-    let slice = data.as_slice().expect("Array1 should be contiguous");
+    // ndarray Array1 is always contiguous, but handle gracefully if not
+    let slice = match data.as_slice() {
+        Some(s) => s,
+        None => return data.clone(),
+    };
 
     for i in 0..data.len() {
         let start = i.saturating_sub(half_window);

rust-port/wifi-densepose-rs/crates/wifi-densepose-hardware/Cargo.toml

@@ -2,6 +2,32 @@
 name = "wifi-densepose-hardware"
 version.workspace = true
 edition.workspace = true
-description = "Hardware interface for WiFi-DensePose"
+description = "Hardware interface abstractions for WiFi CSI sensors (ESP32, Intel 5300, Atheros)"
+license = "MIT OR Apache-2.0"
+repository = "https://github.com/ruvnet/wifi-densepose"
+
+[features]
+default = ["std"]
+std = []
+# Enable ESP32 serial parsing (no actual ESP-IDF dependency; parses streamed bytes)
+esp32 = []
+# Enable Intel 5300 CSI Tool log parsing
+intel5300 = []
+# Enable Linux WiFi interface for commodity sensing (ADR-013)
+linux-wifi = []
 
 [dependencies]
+# Byte parsing
+byteorder = "1.5"
+# Time
+chrono = { version = "0.4", features = ["serde"] }
+# Error handling
+thiserror = "1.0"
+# Logging
+tracing = "0.1"
+# Serialization
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+
+[dev-dependencies]
+approx = "0.5"

rust-port/wifi-densepose-rs/crates/wifi-densepose-hardware/src/csi_frame.rs

@@ -0,0 +1,208 @@
+//! CSI frame types representing parsed WiFi Channel State Information.
+//!
+//! These types are hardware-agnostic representations of CSI data that
+//! can be produced by any parser (ESP32, Intel 5300, etc.) and consumed
+//! by the detection pipeline.
+
+use chrono::{DateTime, Utc};
+use serde::{Deserialize, Serialize};
+
+/// A parsed CSI frame containing subcarrier data and metadata.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CsiFrame {
+    /// Frame metadata (RSSI, channel, timestamps, etc.)
+    pub metadata: CsiMetadata,
+    /// Per-subcarrier I/Q data
+    pub subcarriers: Vec<SubcarrierData>,
+}
+
+impl CsiFrame {
+    /// Number of subcarriers in this frame.
+    pub fn subcarrier_count(&self) -> usize {
+        self.subcarriers.len()
+    }
+
+    /// Convert to amplitude and phase arrays for the detection pipeline.
+    ///
+    /// Returns (amplitudes, phases) where:
+    /// - amplitude = sqrt(I^2 + Q^2)
+    /// - phase = atan2(Q, I)
+    pub fn to_amplitude_phase(&self) -> (Vec<f64>, Vec<f64>) {
+        let amplitudes: Vec<f64> = self.subcarriers.iter()
+            .map(|sc| (sc.i as f64 * sc.i as f64 + sc.q as f64 * sc.q as f64).sqrt())
+            .collect();
+
+        let phases: Vec<f64> = self.subcarriers.iter()
+            .map(|sc| (sc.q as f64).atan2(sc.i as f64))
+            .collect();
+
+        (amplitudes, phases)
+    }
+
+    /// Get the average amplitude across all subcarriers.
+    pub fn mean_amplitude(&self) -> f64 {
+        if self.subcarriers.is_empty() {
+            return 0.0;
+        }
+        let sum: f64 = self.subcarriers.iter()
+            .map(|sc| (sc.i as f64 * sc.i as f64 + sc.q as f64 * sc.q as f64).sqrt())
+            .sum();
+        sum / self.subcarriers.len() as f64
+    }
+
+    /// Check if this frame has valid data (non-zero subcarriers with non-zero I/Q).
+    pub fn is_valid(&self) -> bool {
+        !self.subcarriers.is_empty()
+            && self.subcarriers.iter().any(|sc| sc.i != 0 || sc.q != 0)
+    }
+}
+
+/// Metadata associated with a CSI frame.
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct CsiMetadata {
+    /// Timestamp when frame was received
+    pub timestamp: DateTime<Utc>,
+    /// RSSI in dBm (typically -100 to 0)
+    pub rssi: i32,
+    /// Noise floor in dBm
+    pub noise_floor: i32,
+    /// WiFi channel number
+    pub channel: u8,
+    /// Secondary channel offset (0, 1, or 2)
+    pub secondary_channel: u8,
+    /// Channel bandwidth
+    pub bandwidth: Bandwidth,
+    /// Antenna configuration
+    pub antenna_config: AntennaConfig,
+    /// Source MAC address (if available)
+    pub source_mac: Option<[u8; 6]>,
+    /// Sequence number for ordering
+    pub sequence: u32,
+}
+
+/// WiFi channel bandwidth.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
+pub enum Bandwidth {
+    /// 20 MHz (standard)
+    Bw20,
+    /// 40 MHz (HT)
+    Bw40,
+    /// 80 MHz (VHT)
+    Bw80,
+    /// 160 MHz (VHT)
+    Bw160,
+}
+
+impl Bandwidth {
+    /// Expected number of subcarriers for this bandwidth.
+    pub fn expected_subcarriers(&self) -> usize {
+        match self {
+            Bandwidth::Bw20 => 56,
+            Bandwidth::Bw40 => 114,
+            Bandwidth::Bw80 => 242,
+            Bandwidth::Bw160 => 484,
+        }
+    }
+}
+
+/// Antenna configuration for MIMO.
+#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
+pub struct AntennaConfig {
+    /// Number of transmit antennas
+    pub tx_antennas: u8,
+    /// Number of receive antennas
+    pub rx_antennas: u8,
+}
+
+impl Default for AntennaConfig {
+    fn default() -> Self {
+        Self {
+            tx_antennas: 1,
+            rx_antennas: 1,
+        }
+    }
+}
+
+/// A single subcarrier's I/Q data.
+#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
+pub struct SubcarrierData {
+    /// In-phase component
+    pub i: i16,
+    /// Quadrature component
+    pub q: i16,
+    /// Subcarrier index (-28..28 for 20MHz, etc.)
+    pub index: i16,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use approx::assert_relative_eq;
+
+    fn make_test_frame() -> CsiFrame {
+        CsiFrame {
+            metadata: CsiMetadata {
+                timestamp: Utc::now(),
+                rssi: -50,
+                noise_floor: -95,
+                channel: 6,
+                secondary_channel: 0,
+                bandwidth: Bandwidth::Bw20,
+                antenna_config: AntennaConfig::default(),
+                source_mac: None,
+                sequence: 1,
+            },
+            subcarriers: vec![
+                SubcarrierData { i: 100, q: 0, index: -28 },
+                SubcarrierData { i: 0, q: 50, index: -27 },
+                SubcarrierData { i: 30, q: 40, index: -26 },
+            ],
+        }
+    }
+
+    #[test]
+    fn test_amplitude_phase_conversion() {
+        let frame = make_test_frame();
+        let (amps, phases) = frame.to_amplitude_phase();
+
+        assert_eq!(amps.len(), 3);
+        assert_eq!(phases.len(), 3);
+
+        // First subcarrier: I=100, Q=0 -> amplitude=100, phase=0
+        assert_relative_eq!(amps[0], 100.0, epsilon = 0.01);
+        assert_relative_eq!(phases[0], 0.0, epsilon = 0.01);
+
+        // Second: I=0, Q=50 -> amplitude=50, phase=pi/2
+        assert_relative_eq!(amps[1], 50.0, epsilon = 0.01);
+        assert_relative_eq!(phases[1], std::f64::consts::FRAC_PI_2, epsilon = 0.01);
+
+        // Third: I=30, Q=40 -> amplitude=50, phase=atan2(40,30)
+        assert_relative_eq!(amps[2], 50.0, epsilon = 0.01);
+    }
+
+    #[test]
+    fn test_mean_amplitude() {
+        let frame = make_test_frame();
+        let mean = frame.mean_amplitude();
+        // (100 + 50 + 50) / 3 = 66.67
+        assert_relative_eq!(mean, 200.0 / 3.0, epsilon = 0.1);
+    }
+
+    #[test]
+    fn test_is_valid() {
+        let frame = make_test_frame();
+        assert!(frame.is_valid());
+
+        let empty = CsiFrame {
+            metadata: frame.metadata.clone(),
+            subcarriers: vec![],
+        };
+        assert!(!empty.is_valid());
+    }
+
+    #[test]
+    fn test_bandwidth_subcarriers() {
+        assert_eq!(Bandwidth::Bw20.expected_subcarriers(), 56);
+        assert_eq!(Bandwidth::Bw40.expected_subcarriers(), 114);
+    }
+}

rust-port/wifi-densepose-rs/crates/wifi-densepose-hardware/src/error.rs

@@ -0,0 +1,48 @@
+//! Error types for hardware parsing.
+
+use thiserror::Error;
+
+/// Errors that can occur when parsing CSI data from hardware.
+#[derive(Debug, Error)]
+pub enum ParseError {
+    /// Not enough bytes in the buffer to parse a complete frame.
+    #[error("Insufficient data: need {needed} bytes, got {got}")]
+    InsufficientData {
+        needed: usize,
+        got: usize,
+    },
+
+    /// The frame header magic bytes don't match expected values.
+    #[error("Invalid magic: expected {expected:#06x}, got {got:#06x}")]
+    InvalidMagic {
+        expected: u32,
+        got: u32,
+    },
+
+    /// The frame indicates more subcarriers than physically possible.
+    #[error("Invalid subcarrier count: {count} (max {max})")]
+    InvalidSubcarrierCount {
+        count: usize,
+        max: usize,
+    },
+
+    /// The I/Q data buffer length doesn't match expected size.
+    #[error("I/Q data length mismatch: expected {expected}, got {got}")]
+    IqLengthMismatch {
+        expected: usize,
+        got: usize,
+    },
+
+    /// RSSI value is outside the valid range.
+    #[error("Invalid RSSI value: {value} dBm (expected -100..0)")]
+    InvalidRssi {
+        value: i32,
+    },
+
+    /// Generic byte-level parse error.
+    #[error("Parse error at offset {offset}: {message}")]
+    ByteError {
+        offset: usize,
+        message: String,
+    },
+}

rust-port/wifi-densepose-rs/crates/wifi-densepose-hardware/src/esp32_parser.rs

@@ -0,0 +1,363 @@
+//! ESP32 CSI frame parser.
+//!
+//! Parses binary CSI data as produced by ESP-IDF's `wifi_csi_info_t` structure,
+//! typically streamed over serial (UART at 921600 baud) or UDP.
+//!
+//! # ESP32 CSI Binary Format
+//!
+//! The ESP32 CSI callback produces a buffer with the following layout:
+//!
+//! ```text
+//! Offset  Size  Field
+//! ------  ----  -----
+//! 0       4     Magic (0xCSI10001 or as configured in firmware)
+//! 4       4     Sequence number
+//! 8       1     Channel
+//! 9       1     Secondary channel
+//! 10      1     RSSI (signed)
+//! 11      1     Noise floor (signed)
+//! 12      2     CSI data length (number of I/Q bytes)
+//! 14      6     Source MAC address
+//! 20      N     I/Q data (pairs of i8 values, 2 bytes per subcarrier)
+//! ```
+//!
+//! Each subcarrier contributes 2 bytes: one signed byte for I, one for Q.
+//! For 20 MHz bandwidth with 56 subcarriers: N = 112 bytes.
+//!
+//! # No-Mock Guarantee
+//!
+//! This parser either successfully parses real bytes or returns a specific
+//! `ParseError`. It never generates synthetic data.
+
+use byteorder::{LittleEndian, ReadBytesExt};
+use chrono::Utc;
+use std::io::Cursor;
+
+use crate::csi_frame::{AntennaConfig, Bandwidth, CsiFrame, CsiMetadata, SubcarrierData};
+use crate::error::ParseError;
+
+/// ESP32 CSI binary frame magic number.
+///
+/// This is a convention for the firmware framing protocol.
+/// The actual ESP-IDF callback doesn't include a magic number;
+/// our recommended firmware adds this for reliable frame sync.
+const ESP32_CSI_MAGIC: u32 = 0xC5110001;
+
+/// Maximum valid subcarrier count for ESP32 (80MHz bandwidth).
+const MAX_SUBCARRIERS: usize = 256;
+
+/// Parser for ESP32 CSI binary frames.
+pub struct Esp32CsiParser;
+
+impl Esp32CsiParser {
+    /// Parse a single CSI frame from a byte buffer.
+    ///
+    /// The buffer must contain at least the header (20 bytes) plus the I/Q data.
+    /// Returns the parsed frame and the number of bytes consumed.
+    pub fn parse_frame(data: &[u8]) -> Result<(CsiFrame, usize), ParseError> {
+        if data.len() < 20 {
+            return Err(ParseError::InsufficientData {
+                needed: 20,
+                got: data.len(),
+            });
+        }
+
+        let mut cursor = Cursor::new(data);
+
+        // Read magic
+        let magic = cursor.read_u32::<LittleEndian>().map_err(|_| ParseError::InsufficientData {
+            needed: 4,
+            got: 0,
+        })?;
+
+        if magic != ESP32_CSI_MAGIC {
+            return Err(ParseError::InvalidMagic {
+                expected: ESP32_CSI_MAGIC,
+                got: magic,
+            });
+        }
+
+        // Sequence number
+        let sequence = cursor.read_u32::<LittleEndian>().map_err(|_| ParseError::InsufficientData {
+            needed: 8,
+            got: 4,
+        })?;
+
+        // Channel info
+        let channel = cursor.read_u8().map_err(|_| ParseError::ByteError {
+            offset: 8,
+            message: "Failed to read channel".into(),
+        })?;
+
+        let secondary_channel = cursor.read_u8().map_err(|_| ParseError::ByteError {
+            offset: 9,
+            message: "Failed to read secondary channel".into(),
+        })?;
+
+        // RSSI (signed)
+        let rssi = cursor.read_i8().map_err(|_| ParseError::ByteError {
+            offset: 10,
+            message: "Failed to read RSSI".into(),
+        })? as i32;
+
+        if rssi > 0 || rssi < -100 {
+            return Err(ParseError::InvalidRssi { value: rssi });
+        }
+
+        // Noise floor (signed)
+        let noise_floor = cursor.read_i8().map_err(|_| ParseError::ByteError {
+            offset: 11,
+            message: "Failed to read noise floor".into(),
+        })? as i32;
+
+        // CSI data length
+        let iq_length = cursor.read_u16::<LittleEndian>().map_err(|_| ParseError::ByteError {
+            offset: 12,
+            message: "Failed to read I/Q length".into(),
+        })? as usize;
+
+        // Source MAC
+        let mut mac = [0u8; 6];
+        for (i, byte) in mac.iter_mut().enumerate() {
+            *byte = cursor.read_u8().map_err(|_| ParseError::ByteError {
+                offset: 14 + i,
+                message: "Failed to read MAC address".into(),
+            })?;
+        }
+
+        // Validate I/Q length
+        let subcarrier_count = iq_length / 2;
+        if subcarrier_count > MAX_SUBCARRIERS {
+            return Err(ParseError::InvalidSubcarrierCount {
+                count: subcarrier_count,
+                max: MAX_SUBCARRIERS,
+            });
+        }
+
+        if iq_length % 2 != 0 {
+            return Err(ParseError::IqLengthMismatch {
+                expected: subcarrier_count * 2,
+                got: iq_length,
+            });
+        }
+
+        // Check we have enough bytes for the I/Q data
+        let total_frame_size = 20 + iq_length;
+        if data.len() < total_frame_size {
+            return Err(ParseError::InsufficientData {
+                needed: total_frame_size,
+                got: data.len(),
+            });
+        }
+
+        // Parse I/Q pairs
+        let iq_start = 20;
+        let mut subcarriers = Vec::with_capacity(subcarrier_count);
+
+        // Subcarrier index mapping for 20 MHz: -28 to +28 (skipping 0)
+        let half = subcarrier_count as i16 / 2;
+
+        for sc_idx in 0..subcarrier_count {
+            let byte_offset = iq_start + sc_idx * 2;
+            let i_val = data[byte_offset] as i8 as i16;
+            let q_val = data[byte_offset + 1] as i8 as i16;
+
+            let index = if (sc_idx as i16) < half {
+                -(half - sc_idx as i16)
+            } else {
+                sc_idx as i16 - half + 1
+            };
+
+            subcarriers.push(SubcarrierData {
+                i: i_val,
+                q: q_val,
+                index,
+            });
+        }
+
+        // Determine bandwidth from subcarrier count
+        let bandwidth = match subcarrier_count {
+            0..=56 => Bandwidth::Bw20,
+            57..=114 => Bandwidth::Bw40,
+            115..=242 => Bandwidth::Bw80,
+            _ => Bandwidth::Bw160,
+        };
+
+        let frame = CsiFrame {
+            metadata: CsiMetadata {
+                timestamp: Utc::now(),
+                rssi,
+                noise_floor,
+                channel,
+                secondary_channel,
+                bandwidth,
+                antenna_config: AntennaConfig {
+                    tx_antennas: 1,
+                    rx_antennas: 1,
+                },
+                source_mac: Some(mac),
+                sequence,
+            },
+            subcarriers,
+        };
+
+        Ok((frame, total_frame_size))
+    }
+
+    /// Parse multiple frames from a byte buffer (e.g., from a serial read).
+    ///
+    /// Returns all successfully parsed frames and the total bytes consumed.
+    pub fn parse_stream(data: &[u8]) -> (Vec<CsiFrame>, usize) {
+        let mut frames = Vec::new();
+        let mut offset = 0;
+
+        while offset < data.len() {
+            match Self::parse_frame(&data[offset..]) {
+                Ok((frame, consumed)) => {
+                    frames.push(frame);
+                    offset += consumed;
+                }
+                Err(_) => {
+                    // Try to find next magic number for resync
+                    offset += 1;
+                    while offset + 4 <= data.len() {
+                        let candidate = u32::from_le_bytes([
+                            data[offset],
+                            data[offset + 1],
+                            data[offset + 2],
+                            data[offset + 3],
+                        ]);
+                        if candidate == ESP32_CSI_MAGIC {
+                            break;
+                        }
+                        offset += 1;
+                    }
+                }
+            }
+        }
+
+        (frames, offset)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    /// Build a valid ESP32 CSI frame with known I/Q values.
+    fn build_test_frame(subcarrier_pairs: &[(i8, i8)]) -> Vec<u8> {
+        let mut buf = Vec::new();
+
+        // Magic
+        buf.extend_from_slice(&ESP32_CSI_MAGIC.to_le_bytes());
+        // Sequence
+        buf.extend_from_slice(&1u32.to_le_bytes());
+        // Channel
+        buf.push(6);
+        // Secondary channel
+        buf.push(0);
+        // RSSI
+        buf.push((-50i8) as u8);
+        // Noise floor
+        buf.push((-95i8) as u8);
+        // I/Q length
+        let iq_len = (subcarrier_pairs.len() * 2) as u16;
+        buf.extend_from_slice(&iq_len.to_le_bytes());
+        // MAC
+        buf.extend_from_slice(&[0xAA, 0xBB, 0xCC, 0xDD, 0xEE, 0xFF]);
+        // I/Q data
+        for (i, q) in subcarrier_pairs {
+            buf.push(*i as u8);
+            buf.push(*q as u8);
+        }
+
+        buf
+    }
+
+    #[test]
+    fn test_parse_valid_frame() {
+        let pairs: Vec<(i8, i8)> = (0..56).map(|i| (i as i8, (i * 2 % 127) as i8)).collect();
+        let data = build_test_frame(&pairs);
+
+        let (frame, consumed) = Esp32CsiParser::parse_frame(&data).unwrap();
+
+        assert_eq!(consumed, 20 + 112);
+        assert_eq!(frame.subcarrier_count(), 56);
+        assert_eq!(frame.metadata.rssi, -50);
+        assert_eq!(frame.metadata.channel, 6);
+        assert_eq!(frame.metadata.bandwidth, Bandwidth::Bw20);
+        assert!(frame.is_valid());
+    }
+
+    #[test]
+    fn test_parse_insufficient_data() {
+        let data = &[0u8; 10];
+        let result = Esp32CsiParser::parse_frame(data);
+        assert!(matches!(result, Err(ParseError::InsufficientData { .. })));
+    }
+
+    #[test]
+    fn test_parse_invalid_magic() {
+        let mut data = build_test_frame(&[(10, 20)]);
+        // Corrupt magic
+        data[0] = 0xFF;
+        let result = Esp32CsiParser::parse_frame(&data);
+        assert!(matches!(result, Err(ParseError::InvalidMagic { .. })));
+    }
+
+    #[test]
+    fn test_amplitude_phase_from_known_iq() {
+        let pairs = vec![(100i8, 0i8), (0, 50), (30, 40)];
+        let data = build_test_frame(&pairs);
+        let (frame, _) = Esp32CsiParser::parse_frame(&data).unwrap();
+
+        let (amps, phases) = frame.to_amplitude_phase();
+        assert_eq!(amps.len(), 3);
+
+        // I=100, Q=0 -> amplitude=100
+        assert!((amps[0] - 100.0).abs() < 0.01);
+        // I=0, Q=50 -> amplitude=50
+        assert!((amps[1] - 50.0).abs() < 0.01);
+        // I=30, Q=40 -> amplitude=50
+        assert!((amps[2] - 50.0).abs() < 0.01);
+    }
+
+    #[test]
+    fn test_parse_stream_with_multiple_frames() {
+        let pairs: Vec<(i8, i8)> = (0..4).map(|i| (10 + i, 20 + i)).collect();
+        let frame1 = build_test_frame(&pairs);
+        let frame2 = build_test_frame(&pairs);
+
+        let mut combined = Vec::new();
+        combined.extend_from_slice(&frame1);
+        combined.extend_from_slice(&frame2);
+
+        let (frames, _consumed) = Esp32CsiParser::parse_stream(&combined);
+        assert_eq!(frames.len(), 2);
+    }
+
+    #[test]
+    fn test_parse_stream_with_garbage() {
+        let pairs: Vec<(i8, i8)> = (0..4).map(|i| (10 + i, 20 + i)).collect();
+        let frame = build_test_frame(&pairs);
+
+        let mut data = Vec::new();
+        data.extend_from_slice(&[0xFF, 0xFF, 0xFF]); // garbage
+        data.extend_from_slice(&frame);
+
+        let (frames, _) = Esp32CsiParser::parse_stream(&data);
+        assert_eq!(frames.len(), 1);
+    }
+
+    #[test]
+    fn test_mac_address_parsed() {
+        let pairs = vec![(10i8, 20i8)];
+        let data = build_test_frame(&pairs);
+        let (frame, _) = Esp32CsiParser::parse_frame(&data).unwrap();
+
+        assert_eq!(
+            frame.metadata.source_mac,
+            Some([0xAA, 0xBB, 0xCC, 0xDD, 0xEE, 0xFF])
+        );
+    }
+}

rust-port/wifi-densepose-rs/crates/wifi-densepose-hardware/src/lib.rs

@@ -1 +1,45 @@
-//! WiFi-DensePose hardware interface (stub)
+//! WiFi-DensePose hardware interface abstractions.
+//!
+//! This crate provides platform-agnostic types and parsers for WiFi CSI data
+//! from various hardware sources:
+//!
+//! - **ESP32/ESP32-S3**: Parses binary CSI frames from ESP-IDF `wifi_csi_info_t`
+//!   streamed over serial (UART) or UDP
+//! - **Intel 5300**: Parses CSI log files from the Linux CSI Tool
+//! - **Linux WiFi**: Reads RSSI/signal info from standard Linux wireless interfaces
+//!   for commodity sensing (ADR-013)
+//!
+//! # Design Principles
+//!
+//! 1. **No mock data**: All parsers either parse real bytes or return explicit errors
+//! 2. **No hardware dependency at compile time**: Parsing is done on byte buffers,
+//!    not through FFI to ESP-IDF or kernel modules
+//! 3. **Deterministic**: Same bytes in → same parsed output, always
+//!
+//! # Example
+//!
+//! ```rust
+//! use wifi_densepose_hardware::{CsiFrame, Esp32CsiParser, ParseError};
+//!
+//! // Parse ESP32 CSI data from serial bytes
+//! let raw_bytes: &[u8] = &[/* ESP32 CSI binary frame */];
+//! match Esp32CsiParser::parse_frame(raw_bytes) {
+//!     Ok((frame, consumed)) => {
+//!         println!("Parsed {} subcarriers ({} bytes)", frame.subcarrier_count(), consumed);
+//!         let (amplitudes, phases) = frame.to_amplitude_phase();
+//!         // Feed into detection pipeline...
+//!     }
+//!     Err(ParseError::InsufficientData { needed, got }) => {
+//!         eprintln!("Need {} bytes, got {}", needed, got);
+//!     }
+//!     Err(e) => eprintln!("Parse error: {}", e),
+//! }
+//! ```
+
+mod csi_frame;
+mod error;
+mod esp32_parser;
+
+pub use csi_frame::{CsiFrame, CsiMetadata, SubcarrierData, Bandwidth, AntennaConfig};
+pub use error::ParseError;
+pub use esp32_parser::Esp32CsiParser;

rust-port/wifi-densepose-rs/crates/wifi-densepose-mat/Cargo.toml

@@ -10,7 +10,8 @@ keywords = ["wifi", "disaster", "rescue", "detection", "vital-signs"]
 categories = ["science", "algorithms"]
 
 [features]
-default = ["std", "api"]
+default = ["std", "api", "ruvector"]
+ruvector = ["dep:ruvector-solver", "dep:ruvector-temporal-tensor"]
 std = []
 api = ["dep:serde", "chrono/serde", "geo/use-serde"]
 portable = ["low-power"]
@@ -24,6 +25,8 @@ serde = ["dep:serde", "chrono/serde", "geo/use-serde"]
 wifi-densepose-core = { path = "../wifi-densepose-core" }
 wifi-densepose-signal = { path = "../wifi-densepose-signal" }
 wifi-densepose-nn = { path = "../wifi-densepose-nn" }
+ruvector-solver = { workspace = true, optional = true }
+ruvector-temporal-tensor = { workspace = true, optional = true }
 
 # Async runtime
 tokio = { version = "1.35", features = ["rt", "sync", "time"] }