# RuVector V2: Implementation Roadmap

## From Today's Crates to 2075

> *Every journey of a thousand miles begins with a `cargo build`.*

---

## Guiding Principle

This roadmap follows a strict rule: **each phase delivers production value while laying foundations for the next**. No speculative R&D without shipping. Every milestone is a product.

---

## Phase 1: Foundation (2025-2028)

### Goal: Coherence-Gated AI Agents

Ship the first production systems where AI agents refuse to act when their outputs are structurally inconsistent.

### 1.1 Coherence SDK (Year 1)

**Ship:** `prime-radiant` as a standalone coherence-as-a-service SDK.

| Deliverable | Crate | Status |
|---|---|---|
| Sheaf Laplacian residual computation | `prime-radiant/coherence` | Implemented |
| 4-lane coherence gating | `prime-radiant/execution` | Implemented |
| Witness chain audit trail | `cognitum-gate-tilezero` | Implemented |
| 256-tile WASM fabric | `cognitum-gate-kernel` | Implemented |
| REST/gRPC API | `mcp-brain-server` | Implemented |
| MCP tool integration | `npm/packages/ruvector` (91 tools) | Implemented |

**New work:**
- Coherence SDK packaging (API keys, rate limiting, dashboard)
- Domain-specific interpreters (AI safety, finance, medical — config files, not new math)
- Cloud deployment templates (already on Cloud Run as π.ruv.io)

```rust
// Year 1 API — already possible with current crates
use prime_radiant::coherence::CoherenceEngine;
use prime_radiant::execution::CoherenceGate;

let engine = CoherenceEngine::new(config);
let gate = CoherenceGate::new(engine, thresholds);

// Agent submits action for coherence check
let verdict = gate.evaluate(action, knowledge_graph).await;
match verdict.lane {
    Lane::Reflex => { /* <1ms cached safety check */ },
    Lane::Retrieval => { /* knowledge graph lookup */ },
    Lane::Heavy => { /* full Laplacian computation */ },
    Lane::Human => { /* escalate to human oversight */ },
}
```

### 1.2 Agent Coherence Integration (Year 1-2)

**Ship:** rvAgent with built-in coherence middleware.

| Deliverable | Crate | Status |
|---|---|---|
| Agent framework | `rvAgent` (8 crates) | Implemented |
| Witness middleware | `rvagent-middleware` | Implemented |
| RVF bridge | `rvagent-core/rvf_bridge` | Implemented |
| MCP bridge middleware | `rvagent-middleware` | Implemented |

**New work:**
- `CoherenceMiddleware` — drop-in middleware that checks every tool call against coherence gate
- Agent-to-agent coherence propagation via subagent orchestrator
- Coherence-aware prompt caching (invalidate cache when coherence state changes)

### 1.3 Hyperbolic Knowledge Graphs (Year 2-3)

**Ship:** Enterprise knowledge graph with hierarchy-native search.

| Deliverable | Crate | Status |
|---|---|---|
| Hyperbolic HNSW | `ruvector-hyperbolic-hnsw` | Implemented |
| Per-shard curvature learning | `ruvector-hyperbolic-hnsw` | Implemented |
| Dual-space indexing | `ruvector-hyperbolic-hnsw` | Implemented |
| Vector DB core | `ruvector-core` | Implemented |
| Graph database | `ruvector-graph` | Implemented |
| Graph transformer | `ruvector-graph-transformer` | Implemented |

**New work:**
- Unified hyperbolic knowledge graph API (combine graph + vector + coherence)
- Enterprise connectors (Postgres, S3, Kafka)
- Coherence-indexed retrieval (retrieve only coherent subgraphs)

---

## Phase 2: Nervous Systems (2028-2035)

### Goal: Infrastructure That Thinks

Ship systems where buildings, factories, and cities have nervous systems that sense, learn, and adapt.

### 2.1 Digital Nervous System Platform (Year 3-5)

**Ship:** IoT + edge platform using biological computing principles.

| Deliverable | Crate | Status |
|---|---|---|
| Dendritic coincidence detection | `ruvector-nervous-system` | Implemented |
| HDC memory | `ruvector-nervous-system/hdc` | Implemented |
| Global workspace | `ruvector-nervous-system/routing/workspace` | Implemented |
| Circadian routing | `ruvector-nervous-system/routing/circadian` | Implemented |
| Predictive routing | `ruvector-nervous-system/routing/predictive` | Implemented |
| Pattern separation | `ruvector-nervous-system/separate` | Implemented |
| Edge deployment | `agentic-robotics-embedded` | Implemented |
| Real-time execution | `agentic-robotics-rt` | Implemented |
| Sparse inference | `ruvector-sparse-inference` | Implemented |

**New work:**
- Nervous System SDK — package dendrites + HDC + routing for IoT deployment
- FPGA bitstreams for dendritic computation (`ruvector-fpga-transformer` extended)
- Coherence-gated sensor fusion (dendrite temporal windows + coherence gate)

```rust
// Building nervous system — extend existing APIs
use ruvector_nervous_system::dendrite::DendriticTree;
use ruvector_nervous_system::routing::circadian::CircadianRouter;
use ruvector_nervous_system::hdc::HdcMemory;

// Sensor fusion via dendritic coincidence
let tree = DendriticTree::new(sensor_count, window_ms: 20.0);
for sensor_event in events {
    tree.receive_spike(sensor_event.id, sensor_event.timestamp);
}
let fused_signal = tree.update(now, dt);

// Circadian scheduling — infrastructure sleeps at night
let router = CircadianRouter::new(timezone, load_profile);
let route = router.route(task, current_time);
// Low-load: run GC, defragment, consolidate memories
// High-load: route to fast paths only
```

### 2.2 Continual Learning Infrastructure (Year 4-6)

**Ship:** ML systems that learn continuously without forgetting.

| Deliverable | Crate | Status |
|---|---|---|
| GNN with EWC | `ruvector-gnn` | Implemented |
| Replay buffer | `ruvector-gnn` | Implemented |
| Learning rate scheduling | `ruvector-gnn` | Implemented |
| Mmap gradient accumulation | `ruvector-gnn` | Implemented |
| Tensor compression | `ruvector-gnn` | Implemented |
| SONA self-organizing | `sona` | Implemented |
| 18+ attention mechanisms | `ruvector-attention` | Implemented |

**New work:**
- Federated EWC — continual learning across distributed nodes
- Coherence-validated model updates (reject updates that break consistency)
- Attention routing — MoE attention to select optimal attention per input

### 2.3 Self-Healing Networks (Year 5-7)

**Ship:** Infrastructure that detects and repairs its own failures.

| Deliverable | Crate | Status |
|---|---|---|
| Dynamic min-cut | `ruvector-mincut` | Implemented |
| Self-healing via edge updates | `ruvector-mincut` | Implemented |
| Delta consensus | `ruvector-delta-consensus` | Implemented |
| Raft consensus | `ruvector-raft` | Implemented |
| Replication | `ruvector-replication` | Implemented |
| Snapshot/restore | `ruvector-snapshot` | Implemented |

**New work:**
- Min-cut + coherence integration (detect structural breaks in coherence graph)
- Automated failover with witness audit trail
- Cross-region replication with delta compression

---

## Phase 3: Planetary Scale (2035-2050)

### Goal: Continental Coherence Fabrics

### 3.1 Tile Fabric Scaling (Year 10-15)

Scale `cognitum-gate-kernel` from 256 tiles to millions:

- Hierarchical tile organization (city → region → continent)
- Per-tile curvature learning from `ruvector-hyperbolic-hnsw`
- Delta consensus for inter-tile synchronization
- Tile migration for load balancing

### 3.2 Quantum-Classical Hybrid (Year 10-15)

| Deliverable | Crate | Status |
|---|---|---|
| Quantum circuit simulation | `ruqu-core` | Implemented |
| Quantum algorithms | `ruqu-algorithms` | Implemented |
| Exotic quantum | `ruqu-exotic` | Implemented |
| WASM quantum | `ruqu-wasm` | Implemented |

**New work:**
- Quantum coherence verification (use quantum circuits to validate classical coherence)
- Hybrid solvers (quantum for hard subproblems, `ruvector-solver` for the rest)
- Quantum-safe witness chains (post-quantum signatures already in roadmap)

### 3.3 Autonomous Robot Fleets (Year 10-20)

| Deliverable | Crate | Status |
|---|---|---|
| Robotics platform | `ruvector-robotics` | Implemented |
| Full robotics stack | `agentic-robotics-*` (5 crates) | Implemented |
| Domain expansion | `ruvector-domain-expansion` | Implemented |
| Behavior trees | `ruvector-robotics` | Implemented |

**New work:**
- Coherence-gated behavior trees (refuse unsafe actions)
- Fleet-wide continual learning (GNN + EWC + federated)
- Space-grade FPGA deployment (`ruvector-fpga-transformer` + radiation hardening)

---

## Phase 4: Civilization Infrastructure (2050-2065)

### Goal: Planetary Defense and Governance

- **Climate coherence mesh** — millions of sensor tiles, coherence-gated climate models
- **AI safety framework** — mandatory coherence gates on all autonomous systems
- **Governance fabric** — tilezero decision/merge/permit for transparent democratic processes
- **Scientific coherence** — automated paradigm shift detection in research literature

### Key Integration Points

```
Climate Sensors → Nervous System → Coherence Gate → Policy Response
  (dendrites)     (HDC encode)    (sheaf verify)   (tilezero permit)
```

---

## Phase 5: Interplanetary (2065-2075)

### Goal: Coherence Across Light-Minutes

- **Light-delay tolerant consensus** — extend delta consensus for 3-22 minute Mars delay
- **Autonomous coherence islands** — each planet/station runs independent coherence fabric
- **Reconciliation protocol** — merge coherence states when communication windows open
- **Quantum relay** — ruqu-based entanglement-assisted verification (experimental)

---

## Crate Evolution Map

| Current Crate | Phase 1 | Phase 2 | Phase 3 | Phase 4+ |
|---|---|---|---|---|
| `prime-radiant` | Coherence SDK | Building nervous systems | Continental fabric | Planetary grid |
| `cognitum-gate-kernel` | 256 tiles | 10K tiles | 1M+ tiles | Interplanetary |
| `ruvector-nervous-system` | Lab demos | Smart buildings | City nervous systems | Planetary NS |
| `ruvector-hyperbolic-hnsw` | Enterprise search | Knowledge graphs | Global taxonomy | Universal knowledge |
| `ruvector-gnn` | ML pipelines | Continual learning | Federated learning | Planetary learning |
| `ruvector-mincut` | Network monitoring | Self-healing infra | Continental resilience | Planetary defense |
| `rvAgent` | AI coding agents | Autonomous workers | Robot fleets | Civilization agents |
| `ruqu-core` | Simulation | Hybrid algorithms | Quantum coherence | Quantum relay |
| `ruvector-robotics` | Lab robots | Factory fleets | Lunar construction | Deep space |
| `neural-trader-*` | Trading bots | Supply chain AI | Resource allocation | Post-scarcity |

---

## Build Order (Next 12 Months)

Priority order for immediate implementation:

| # | Deliverable | Crates Involved | Effort |
|---|---|---|---|
| 1 | Coherence middleware for rvAgent | `rvagent-middleware` + `prime-radiant` | 2 months |
| 2 | Coherence SDK packaging + docs | `prime-radiant` + `mcp-brain-server` | 1 month |
| 3 | Hyperbolic knowledge graph API | `ruvector-hyperbolic-hnsw` + `ruvector-graph` | 3 months |
| 4 | Nervous system IoT SDK | `ruvector-nervous-system` + embedded | 3 months |
| 5 | Self-healing network demo | `ruvector-mincut` + `ruvector-delta-consensus` | 2 months |
| 6 | Federated EWC prototype | `ruvector-gnn` + `ruvector-replication` | 3 months |
| 7 | Quantum-classical hybrid solver | `ruqu-core` + `ruvector-solver` | 4 months |
| 8 | Coherence-gated robotics demo | `ruvector-robotics` + `prime-radiant` | 3 months |

---

## Success Metrics

| Metric | Phase 1 Target | Phase 2 Target | Phase 3 Target |
|---|---|---|---|
| Coherence gate latency (Lane 0) | <1ms | <500μs | <100μs |
| Tile count | 256 | 100,000 | 10,000,000+ |
| Knowledge graph hierarchy depth | 10 levels | 50 levels | Unbounded |
| Continual learning retention | 95% | 99% | 99.9% |
| Self-healing recovery time | <10s | <1s | <100ms |
| Witness chain throughput | 10K/s | 1M/s | 1B/s |

---

## Open Research Questions

1. **Coherence completeness** — Can sheaf Laplacian residuals detect ALL structural inconsistencies, or only certain classes? What is the theoretical coverage?

2. **Curvature dynamics** — How does optimal hyperbolic curvature change as knowledge graphs evolve? Can we learn curvature online?

3. **Biological fidelity** — How closely must dendritic models match biology to capture useful computation? Where can we simplify?

4. **Quantum advantage** — For which coherence computations does quantum acceleration provide provable speedup?

5. **Interplanetary consensus** — What is the minimum communication bandwidth for maintaining coherence across light-minute delays?

6. **Emergent behavior** — At what scale does the nervous system + coherence fabric + agent mesh produce genuinely emergent intelligence?

---

## Conclusion

The roadmap is ambitious but concrete. Phase 1 requires no new mathematics — only packaging, integration, and API design around crates that already exist. Each subsequent phase extends existing foundations rather than replacing them.

The key insight: **we are not building new technology for each phase**. We are scaling the same coherence primitive — from a single agent to a planet — by composing crates that already implement the core algorithms.

The 50-year vision starts with a 12-month sprint.

---

*RuVector V2 Research Series — Document 07 of 07*
*From `cargo build` to civilizational infrastructure*