# Implementation Milestones

## Overview

Structured milestone plan for implementing the Neural DAG Learning system with 15-agent swarm coordination.

## Milestone Summary

```
┌─────────────────────────────────────────────────────────────────────────┐
│                    NEURAL DAG LEARNING IMPLEMENTATION                    │
├─────────────────────────────────────────────────────────────────────────┤
│ M1: Foundation        ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  15%    │
│ M2: Core Attention    ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░  25%    │
│ M3: SONA Learning     ████████████████░░░░░░░░░░░░░░░░░░░░░░░░  35%    │
│ M4: PostgreSQL        ████████████████████████░░░░░░░░░░░░░░░░  55%    │
│ M5: Self-Healing      ████████████████████████████░░░░░░░░░░░░  65%    │
│ M6: QuDAG Integration ████████████████████████████████░░░░░░░░  80%    │
│ M7: Testing           ████████████████████████████████████░░░░  90%    │
│ M8: Production Ready  ████████████████████████████████████████ 100%    │
└─────────────────────────────────────────────────────────────────────────┘
```

---

## Milestone 1: Foundation

**Status**: Not Started
**Priority**: Critical
**Agents**: #1, #14, #15

### Objectives

- [ ] Establish core DAG data structures
- [ ] Create test fixture infrastructure
- [ ] Initialize documentation structure

### Deliverables

| Deliverable | Agent | Status | Notes |
|-------------|-------|--------|-------|
| `QueryDag` struct | #1 | Pending | Node/edge management |
| `OperatorNode` enum | #1 | Pending | All 15+ operator types |
| Topological sort | #1 | Pending | O(V+E) implementation |
| Cycle detection | #1 | Pending | For validation |
| DAG serialization | #1 | Pending | JSON + binary formats |
| Test DAG generator | #14 | Pending | All complexity levels |
| Mock fixtures | #14 | Pending | Sample data |
| Doc skeleton | #15 | Pending | README + guides |

### Acceptance Criteria

```rust
// Core functionality must work
let mut dag = QueryDag::new();
dag.add_node(0, OperatorNode::SeqScan { table: "users".into() });
dag.add_node(1, OperatorNode::Filter { predicate: "id > 0".into() });
dag.add_edge(0, 1).unwrap();

let sorted = dag.topological_sort().unwrap();
assert_eq!(sorted, vec![0, 1]);

let json = dag.to_json().unwrap();
let restored = QueryDag::from_json(&json).unwrap();
assert_eq!(dag, restored);
```

### Files Created

```
ruvector-dag/
├── Cargo.toml
├── src/
│   ├── lib.rs
│   └── dag/
│       ├── mod.rs
│       ├── query_dag.rs
│       ├── operator_node.rs
│       ├── traversal.rs
│       └── serialization.rs
└── tests/
    └── fixtures/
        ├── dag_generator.rs
        └── sample_dags.json
```

### Exit Criteria

- [ ] All unit tests pass for DAG module
- [ ] Benchmark: create 1000-node DAG in <10ms
- [ ] Documentation: rustdoc for all public items
- [ ] Code review approved by Queen agent

---

## Milestone 2: Core Attention Mechanisms

**Status**: Not Started
**Priority**: Critical
**Agents**: #2, #3

### Objectives

- [ ] Implement all 7 attention mechanisms
- [ ] Implement attention selector (UCB bandit)
- [ ] Achieve performance targets

### Deliverables

| Deliverable | Agent | Status | Target |
|-------------|-------|--------|--------|
| `DagAttention` trait | #2 | Pending | - |
| `TopologicalAttention` | #2 | Pending | <50μs/100 nodes |
| `CausalConeAttention` | #2 | Pending | <100μs/100 nodes |
| `CriticalPathAttention` | #2 | Pending | <75μs/100 nodes |
| `MinCutGatedAttention` | #2 | Pending | <200μs/100 nodes |
| `HierarchicalLorentzAttention` | #3 | Pending | <150μs/100 nodes |
| `ParallelBranchAttention` | #3 | Pending | <100μs/100 nodes |
| `TemporalBTSPAttention` | #3 | Pending | <120μs/100 nodes |
| `AttentionSelector` | #3 | Pending | UCB regret O(√T) |
| Attention cache | #3 | Pending | 10K entry LRU |

### Acceptance Criteria

```rust
// All mechanisms implement trait
let mechanisms: Vec<Box<dyn DagAttention>> = vec![
    Box::new(TopologicalAttention::new(config)),
    Box::new(CausalConeAttention::new(config)),
    Box::new(CriticalPathAttention::new(config)),
    Box::new(MinCutGatedAttention::new(config)),
    Box::new(HierarchicalLorentzAttention::new(config)),
    Box::new(ParallelBranchAttention::new(config)),
    Box::new(TemporalBTSPAttention::new(config)),
];

for mechanism in mechanisms {
    let scores = mechanism.forward(&dag).unwrap();

    // Scores sum to ~1.0
    let sum: f32 = scores.values().sum();
    assert!((sum - 1.0).abs() < 0.001);

    // All scores in [0, 1]
    assert!(scores.values().all(|&s| s >= 0.0 && s <= 1.0));
}

// Selector chooses based on history
let mut selector = AttentionSelector::new(mechanisms.len());
for _ in 0..100 {
    let chosen = selector.select();
    let reward = simulate_query_improvement();
    selector.update(chosen, reward);
}
```

### Performance Benchmarks

| Mechanism | 10 nodes | 100 nodes | 500 nodes | 1000 nodes |
|-----------|----------|-----------|-----------|------------|
| Topological | <5μs | <50μs | <200μs | <500μs |
| CausalCone | <10μs | <100μs | <400μs | <1ms |
| CriticalPath | <8μs | <75μs | <300μs | <700μs |
| MinCutGated | <20μs | <200μs | <800μs | <2ms |
| HierarchicalLorentz | <15μs | <150μs | <600μs | <1.5ms |
| ParallelBranch | <10μs | <100μs | <400μs | <1ms |
| TemporalBTSP | <12μs | <120μs | <500μs | <1.2ms |

### Files Created

```
ruvector-dag/src/attention/
├── mod.rs
├── traits.rs
├── topological.rs
├── causal_cone.rs
├── critical_path.rs
├── mincut_gated.rs
├── hierarchical_lorentz.rs
├── parallel_branch.rs
├── temporal_btsp.rs
├── selector.rs
└── cache.rs
```

### Exit Criteria

- [ ] All 7 mechanisms pass unit tests
- [ ] All performance benchmarks met
- [ ] Property tests pass (1000 cases each)
- [ ] Selector converges to best mechanism in tests
- [ ] Code review approved

---

## Milestone 3: SONA Learning System

**Status**: Not Started
**Priority**: Critical
**Agents**: #4, #5

### Objectives

- [ ] Implement SONA engine with two-tier learning
- [ ] Implement MinCut optimization engine
- [ ] Achieve subpolynomial update complexity

### Deliverables

| Deliverable | Agent | Status | Target |
|-------------|-------|--------|--------|
| `DagSonaEngine` | #4 | Pending | Orchestration |
| `MicroLoRA` | #4 | Pending | <100μs adapt |
| `DagTrajectoryBuffer` | #4 | Pending | Lock-free, 1K cap |
| `DagReasoningBank` | #4 | Pending | 100 clusters, <2ms search |
| `EwcPlusPlus` | #4 | Pending | λ=5000 default |
| `DagMinCutEngine` | #5 | Pending | - |
| `LocalKCut` oracle | #5 | Pending | Local approximation |
| Dynamic updates | #5 | Pending | O(n^0.12) amortized |
| Bottleneck detection | #5 | Pending | - |

### Acceptance Criteria

```rust
// SONA instant loop
let mut sona = DagSonaEngine::new(config);
let dag = create_query_dag();

let start = Instant::now();
let enhanced = sona.pre_query(&dag).unwrap();
assert!(start.elapsed() < Duration::from_micros(100));

// Learning from trajectory
sona.post_query(&dag, &execution_metrics);

// Verify learning happened
let patterns = sona.reasoning_bank.query_similar(&dag.embedding(), 1);
assert!(!patterns.is_empty());

// MinCut dynamic updates
let mut mincut = DagMinCutEngine::new();
mincut.build_from_dag(&large_dag);

let timings: Vec<Duration> = (0..1000)
    .map(|_| {
        let start = Instant::now();
        mincut.update_edge(rand_u(), rand_v(), rand_weight());
        start.elapsed()
    })
    .collect();

let amortized = timings.iter().sum::<Duration>() / 1000;
// Verify subpolynomial: amortized << O(n)
```

### Files Created

```
ruvector-dag/src/
├── sona/
│   ├── mod.rs
│   ├── engine.rs
│   ├── micro_lora.rs
│   ├── trajectory.rs
│   ├── reasoning_bank.rs
│   └── ewc.rs
└── mincut/
    ├── mod.rs
    ├── engine.rs
    ├── local_kcut.rs
    ├── dynamic_updates.rs
    ├── bottleneck.rs
    └── redundancy.rs
```

### Exit Criteria

- [ ] MicroLoRA adapts in <100μs
- [ ] Pattern search in <2ms for 10K patterns
- [ ] EWC prevents catastrophic forgetting (>80% task retention)
- [ ] MinCut updates are O(n^0.12) amortized
- [ ] All tests pass

---

## Milestone 4: PostgreSQL Integration

**Status**: Not Started
**Priority**: Critical
**Agents**: #6, #7, #8

### Objectives

- [ ] Create functional PostgreSQL extension
- [ ] Implement all SQL functions
- [ ] Hook into query execution pipeline

### Deliverables

| Deliverable | Agent | Status | Notes |
|-------------|-------|--------|-------|
| pgrx extension setup | #6 | Pending | Extension skeleton |
| GUC variables | #6 | Pending | All config vars |
| Global state | #6 | Pending | DashMap-based |
| Planner hook | #6 | Pending | DAG analysis |
| Executor hooks | #6 | Pending | Trajectory capture |
| Background worker | #6 | Pending | Learning loop |
| Config SQL funcs | #7 | Pending | 5 functions |
| Analysis SQL funcs | #7 | Pending | 6 functions |
| Attention SQL funcs | #7 | Pending | 3 functions |
| Pattern SQL funcs | #8 | Pending | 4 functions |
| Trajectory SQL funcs | #8 | Pending | 3 functions |
| Learning SQL funcs | #8 | Pending | 4 functions |

### Acceptance Criteria

```sql
-- Extension loads successfully
CREATE EXTENSION ruvector_dag CASCADE;

-- Configuration works
SELECT ruvector.dag_set_enabled(true);
SELECT ruvector.dag_set_attention('auto');

-- Query analysis works
SELECT * FROM ruvector.dag_analyze_plan($$
    SELECT * FROM vectors
    WHERE embedding <-> '[0.1,0.2,0.3]' < 0.5
    LIMIT 10
$$);

-- Patterns are stored
INSERT INTO test_vectors SELECT generate_series(1,1000), random_vector(128);
SELECT COUNT(*) FROM ruvector.dag_pattern_clusters();  -- Should have clusters

-- Learning improves over time
DO $$
DECLARE
    initial_time FLOAT8;
    final_time FLOAT8;
BEGIN
    -- Run workload
    FOR i IN 1..100 LOOP
        PERFORM * FROM test_vectors ORDER BY embedding <-> random_vector(128) LIMIT 10;
    END LOOP;

    -- Check improvement
    SELECT avg_improvement INTO final_time FROM ruvector.dag_status();
    RAISE NOTICE 'Improvement ratio: %', final_time;
END $$;
```

### Files Created

```
ruvector-postgres/src/dag/
├── mod.rs
├── extension.rs
├── guc.rs
├── state.rs
├── hooks.rs
├── worker.rs
└── functions/
    ├── mod.rs
    ├── config.rs
    ├── analysis.rs
    ├── attention.rs
    ├── patterns.rs
    ├── trajectories.rs
    └── learning.rs
```

### Exit Criteria

- [ ] Extension creates without errors
- [ ] All 25+ SQL functions work
- [ ] Query hooks capture execution data
- [ ] Background worker runs learning loop
- [ ] Integration tests pass

---

## Milestone 5: Self-Healing System

**Status**: Not Started
**Priority**: High
**Agents**: #9

### Objectives

- [ ] Implement autonomous anomaly detection
- [ ] Implement automatic repair strategies
- [ ] Integrate with healing SQL functions

### Deliverables

| Deliverable | Status | Notes |
|-------------|--------|-------|
| `AnomalyDetector` | Pending | Z-score based |
| `IndexHealthChecker` | Pending | HNSW/IVFFlat |
| `LearningDriftDetector` | Pending | Pattern quality trends |
| `RepairStrategy` trait | Pending | Strategy interface |
| `IndexRebalanceStrategy` | Pending | Rebalance indexes |
| `PatternResetStrategy` | Pending | Reset bad patterns |
| `HealingOrchestrator` | Pending | Main loop |

### Acceptance Criteria

```rust
// Anomaly detection
let mut detector = AnomalyDetector::new(AnomalyConfig {
    z_threshold: 3.0,
    window_size: 100,
});

// Inject anomaly
for _ in 0..99 {
    detector.observe(1.0);  // Normal
}
detector.observe(100.0);  // Anomaly

let anomalies = detector.detect();
assert!(!anomalies.is_empty());
assert!(anomalies[0].z_score > 3.0);

// Self-healing
let orchestrator = HealingOrchestrator::new(config);
orchestrator.run_cycle().unwrap();

// Verify repairs applied
let health = orchestrator.health_report();
assert!(health.overall_score > 0.8);
```

### Files Created

```
ruvector-dag/src/healing/
├── mod.rs
├── anomaly.rs
├── index_health.rs
├── drift_detector.rs
├── strategies.rs
└── orchestrator.rs

ruvector-postgres/src/dag/functions/
└── healing.rs
```

### Exit Criteria

- [ ] Anomalies detected within 1s
- [ ] Repairs applied automatically
- [ ] No false positives in 24h test
- [ ] SQL healing functions work
- [ ] Integration tests pass

---

## Milestone 6: QuDAG Integration

**Status**: Not Started
**Priority**: High
**Agents**: #10, #11, #12

### Objectives

- [ ] Connect to QuDAG network
- [ ] Implement quantum-resistant crypto
- [ ] Enable distributed pattern learning

### Deliverables

| Deliverable | Agent | Status | Notes |
|-------------|-------|--------|-------|
| `QuDagClient` | #10 | Pending | Network client |
| Pattern proposal | #10 | Pending | Submit patterns |
| Pattern sync | #10 | Pending | Receive patterns |
| Consensus validation | #10 | Pending | Track votes |
| ML-KEM-768 | #11 | Pending | Encryption |
| ML-DSA | #11 | Pending | Signatures |
| Identity management | #11 | Pending | Key generation |
| Differential privacy | #11 | Pending | Pattern noise |
| Staking interface | #12 | Pending | Token staking |
| Reward claiming | #12 | Pending | Earn rUv |
| QuDAG SQL funcs | #12 | Pending | SQL interface |

### Acceptance Criteria

```rust
// Connect to network
let client = QuDagClient::connect("https://qudag.example.com:8443").await?;
assert!(client.is_connected());

// Propose pattern with DP
let pattern = PatternProposal {
    vector: pattern_vector,
    metadata: metadata,
    noise: laplace_noise(epsilon),
};
let proposal_id = client.propose_pattern(pattern).await?;

// Wait for consensus
let status = client.wait_for_consensus(&proposal_id, timeout).await?;
assert!(matches!(status, ConsensusStatus::Finalized));

// Sync patterns
let new_patterns = client.sync_patterns(since_round).await?;
for pattern in new_patterns {
    reasoning_bank.import_pattern(pattern);
}

// Token operations
let balance = client.get_balance().await?;
client.stake(100.0).await?;
let rewards = client.claim_rewards().await?;
```

### Files Created

```
ruvector-dag/src/qudag/
├── mod.rs
├── client.rs
├── network.rs
├── proposal.rs
├── consensus.rs
├── sync.rs
├── crypto/
│   ├── mod.rs
│   ├── ml_kem.rs
│   ├── ml_dsa.rs
│   ├── identity.rs
│   ├── keystore.rs
│   └── differential_privacy.rs
└── tokens/
    ├── mod.rs
    ├── staking.rs
    ├── rewards.rs
    └── governance.rs

ruvector-postgres/src/dag/functions/
└── qudag.rs
```

### Exit Criteria

- [ ] Connect to test QuDAG network
- [ ] Pattern proposals finalize
- [ ] Pattern sync works bidirectionally
- [ ] ML-KEM/ML-DSA operations work
- [ ] Token operations succeed
- [ ] SQL functions work

---

## Milestone 7: Comprehensive Testing

**Status**: Not Started
**Priority**: High
**Agents**: #13, #14

### Objectives

- [ ] Achieve >80% test coverage
- [ ] All benchmarks meet targets
- [ ] CI/CD pipeline operational

### Deliverables

| Category | Count | Status |
|----------|-------|--------|
| Unit tests (attention) | 50+ | Pending |
| Unit tests (sona) | 40+ | Pending |
| Unit tests (mincut) | 30+ | Pending |
| Unit tests (healing) | 25+ | Pending |
| Unit tests (qudag) | 35+ | Pending |
| Integration tests (postgres) | 20+ | Pending |
| Integration tests (qudag) | 15+ | Pending |
| Property tests | 20+ | Pending |
| Benchmarks | 15+ | Pending |

### Performance Verification

| Component | Target | Test |
|-----------|--------|------|
| Topological attention | <50μs/100 nodes | Benchmark |
| MicroLoRA | <100μs | Benchmark |
| Pattern search | <2ms/10K | Benchmark |
| MinCut update | O(n^0.12) | Benchmark |
| Query analysis | <5ms | Integration |
| Full learning cycle | <100ms | Integration |

### Coverage Targets

```
Overall:     >80%
attention/:  >90%
sona/:       >85%
mincut/:     >85%
healing/:    >80%
qudag/:      >75%
functions/:  >85%
```

### Files Created

```
ruvector-dag/
├── tests/
│   ├── unit/
│   │   ├── attention/
│   │   ├── sona/
│   │   ├── mincut/
│   │   ├── healing/
│   │   └── qudag/
│   ├── integration/
│   │   ├── postgres/
│   │   └── qudag/
│   ├── property/
│   └── fixtures/
├── benches/
│   ├── attention_bench.rs
│   ├── sona_bench.rs
│   └── mincut_bench.rs

.github/workflows/
├── dag-tests.yml
└── dag-benchmarks.yml
```

### Exit Criteria

- [ ] Coverage >80%
- [ ] All tests pass
- [ ] All benchmarks meet targets
- [ ] CI pipeline green
- [ ] No critical issues

---

## Milestone 8: Production Ready

**Status**: Not Started
**Priority**: Critical
**Agents**: All

### Objectives

- [ ] Complete documentation
- [ ] Performance optimization
- [ ] Security audit
- [ ] Release preparation

### Deliverables

| Deliverable | Status |
|-------------|--------|
| Complete rustdoc | Pending |
| SQL API docs | Pending |
| Usage examples | Pending |
| Integration guide | Pending |
| Troubleshooting guide | Pending |
| Performance tuning guide | Pending |
| Security review | Pending |
| CHANGELOG | Pending |
| Release notes | Pending |

### Security Checklist

- [ ] No secret exposure
- [ ] Input validation on all SQL functions
- [ ] Safe memory handling (no leaks)
- [ ] Cryptographic review (ML-KEM/ML-DSA)
- [ ] Differential privacy parameters validated
- [ ] No SQL injection vectors
- [ ] Resource limits enforced

### Performance Optimization

- [ ] Profile and optimize hot paths
- [ ] Memory usage optimization
- [ ] Cache tuning
- [ ] Query plan caching
- [ ] Background worker tuning

### Release Checklist

- [ ] Version bump
- [ ] CHANGELOG updated
- [ ] All tests pass
- [ ] Benchmarks verified
- [ ] Documentation complete
- [ ] Examples tested
- [ ] Binary artifacts built
- [ ] crates.io ready (if applicable)

### Exit Criteria

- [ ] All previous milestones complete
- [ ] Documentation complete
- [ ] Security review passed
- [ ] Performance targets met
- [ ] Ready for production deployment

---

## Risk Register

| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| MinCut complexity target not achievable | High | Medium | Fall back to O(√n) algorithm |
| PostgreSQL hook instability | High | Low | Extensive testing, fallback modes |
| QuDAG network unavailable | Medium | Medium | Local-only fallback mode |
| Performance regression | Medium | Medium | Continuous benchmarking in CI |
| Memory leaks | High | Low | Valgrind/miri testing |
| Cross-agent coordination failures | Medium | Medium | Queen agent mediation |

## Dependencies

### External Dependencies

| Dependency | Version | Purpose |
|------------|---------|---------|
| pgrx | ^0.11 | PostgreSQL extension |
| tokio | ^1.0 | Async runtime |
| dashmap | ^5.0 | Concurrent hashmap |
| crossbeam | ^0.8 | Lock-free structures |
| ndarray | ^0.15 | Numeric arrays |
| ml-kem | TBD | ML-KEM-768 |
| ml-dsa | TBD | ML-DSA signatures |

### Internal Dependencies

- `ruvector-core`: Vector operations, SONA base
- `ruvector-graph`: GNN, attention base
- `ruvector-postgres`: Extension infrastructure

---

## Completion Tracking

| Milestone | Weight | Status | Completion |
|-----------|--------|--------|------------|
| M1: Foundation | 15% | Not Started | 0% |
| M2: Core Attention | 10% | Not Started | 0% |
| M3: SONA Learning | 10% | Not Started | 0% |
| M4: PostgreSQL | 20% | Not Started | 0% |
| M5: Self-Healing | 10% | Not Started | 0% |
| M6: QuDAG | 15% | Not Started | 0% |
| M7: Testing | 10% | Not Started | 0% |
| M8: Production | 10% | Not Started | 0% |
| **TOTAL** | **100%** | - | **0%** |

---

*Document: 12-MILESTONES.md | Version: 1.0 | Last Updated: 2025-01-XX*