wifi-densepose/docs/dag/12-MILESTONES.md

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

// 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

// 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

// 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

-- 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

// 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

// 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