ruvector/docs/architecture/coherence-engine-ddd.md

Coherence Engine: Domain-Driven Design

Version: 0.3 Date: 2026-01-22 Status: Draft

---

Strategic Design

Domain Vision

The Coherence Engine provides a continuously updated field of coherence that shows where action is safe and where action must stop. It replaces probabilistic confidence with mathematical witnesses based on sheaf Laplacian theory.

This is not prediction. The system answers: "Does the world still fit together?" not "What will happen?"

The Universal Coherence Object

The power lies in a single underlying coherence object inside ruvector. Once the math is fixed, everything else becomes interpretation:

Domain	Nodes Become	Edges Become	Residual Becomes	Gate Becomes
AI Agents	Facts, hypotheses, beliefs	Citations, logical implication	Contradiction energy	Hallucination refusal
Finance	Trades, positions, signals	Market dependencies, arbitrage	Regime mismatch	Trading throttle
Medical	Vitals, diagnoses, treatments	Physiological causality	Clinical disagreement	Escalation trigger
Robotics	Sensor readings, goals, plans	Physics, kinematics	Motion impossibility	Safety stop
Security	Identities, permissions, actions	Policy rules, trust chains	Authorization violation	Access denial
Science	Hypotheses, observations, models	Experimental evidence	Theory inconsistency	Pruning signal

Same math, different interpretations. Same residual = contradiction energy. Same gate = refusal mechanism with witness.

Core Domain

Coherence Computation - The heart of the system, computing edge residuals and aggregating them into global coherence energy scores. Most updates stay in a low-latency reflex lane; sustained/growing incoherence triggers escalation.

Supporting Domains

1. Knowledge Substrate - Graph state management (nodes, edges, restriction maps)
2. Governance - Policy, witness, and lineage management (signed policy bundles, mandatory witnesses)
3. Action Execution - Gated side effects with mandatory witnesses (refusal with proof)
4. Tile Fabric - 256-tile WASM distributed computation (cognitum-gate-kernel)
5. Neural Gating - Biologically-inspired compute ladder (ruvector-nervous-system)
6. Adaptive Learning - Self-optimizing thresholds from real traces (sona)

Generic Domains

1. Storage - PostgreSQL (transactional authority) + ruvector (high-performance vector/graph)
2. Event Sourcing - Deterministic replay from signed event log
3. Distributed Consensus - Multi-node synchronization (ruvector-raft)

Application Evolution

The universal coherence object enables a clean spectrum of applications without rewriting the core:

Timeline	Applications	Key Capability
Today	Anti-hallucination guards, market regime throttles, audit-ready compliance proofs	Structural proof, not probability
Next (12-24mo)	Safety-first drone autonomy, medical monitoring (sustained disagreement), zero-trust security	Proactive detection before alerts
Future (5-10yr)	Scientific theory pruning, policy stress testing, self-awareness primitive	System knows when it doesn't know

Self-Awareness Primitive: The system eventually knows when it no longer understands itself.

---

Ruvector Ecosystem Integration Map

┌─────────────────────────────────────────────────────────────────────────────────┐
│                              BOUNDED CONTEXTS                                    │
│                                                                                  │
│  ┌─────────────────────────────────────────────────────────────────────────────┐│
│  │                        TILE FABRIC (cognitum-gate-kernel)                   ││
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐        ┌─────────┐                    ││
│  │  │ Tile 0  │ │ Tile 1  │ │ Tile 2  │  ...   │Tile 255 │                    ││
│  │  │ Shard   │ │ Shard   │ │ Shard   │        │ Shard   │                    ││
│  │  │ E-value │ │ E-value │ │ E-value │        │ E-value │                    ││
│  │  │ Witness │ │ Witness │ │ Witness │        │ Witness │                    ││
│  │  └─────────┘ └─────────┘ └─────────┘        └─────────┘                    ││
│  └─────────────────────────────────────────────────────────────────────────────┘│
│                                      │                                          │
│                                      ▼                                          │
│  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐              │
│  │ COHERENCE        │  │ KNOWLEDGE        │  │ NEURAL GATING    │              │
│  │ COMPUTATION      │◀─│ SUBSTRATE        │──│ (nervous-system) │              │
│  │                  │  │                  │  │                  │              │
│  │ • Energy calc    │  │ • SheafGraph     │  │ • CoherenceGated │              │
│  │ • Spectral       │  │ • Learned ρ(GNN) │  │ • GlobalWorkspace│              │
│  │ • Hyperbolic     │  │ • Hyperbolic idx │  │ • HDC witnesses  │              │
│  │ • Attention wgt  │  │ • MinCut isolate │  │ • Dendrites      │              │
│  └──────────────────┘  └──────────────────┘  └──────────────────┘              │
│           │                    │                      │                         │
│           └────────────────────┼──────────────────────┘                         │
│                                ▼                                                │
│  ┌──────────────────────────────────────────────────────────────────────────┐  │
│  │                    ADAPTIVE LEARNING (sona)                               │  │
│  │  Micro-LoRA (instant) │ Base-LoRA (background) │ EWC++ (no forgetting)   │  │
│  │  ReasoningBank        │ Three learning loops    │ Pattern extraction      │  │
│  └──────────────────────────────────────────────────────────────────────────┘  │
│                                │                                                │
│                                ▼                                                │
│  ┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐              │
│  │ GOVERNANCE       │  │ ACTION           │  │ DISTRIBUTED      │              │
│  │                  │  │ EXECUTION        │  │ CONSENSUS (raft) │              │
│  │ • PolicyBundle   │  │ • Gate           │  │ • Leader elect   │              │
│  │ • WitnessRecord  │  │ • ComputeLadder  │  │ • Log replicate  │              │
│  │ • LineageRecord  │  │ • Escalation     │  │ • Global energy  │              │
│  └──────────────────┘  └──────────────────┘  └──────────────────┘              │
│                                                                                  │
└─────────────────────────────────────────────────────────────────────────────────┘

Crate-to-Context Mapping

Bounded Context	Primary Crate	Supporting Crates
Tile Fabric	cognitum-gate-kernel	-
Coherence Computation	ruvector-coherence (new)	ruvector-attention, ruvector-hyperbolic-hnsw
Knowledge Substrate	ruvector-graph	ruvector-gnn, ruvector-mincut, ruvector-core
Neural Gating	ruvector-nervous-system	-
Adaptive Learning	sona	ruvector-gnn (EWC)
Governance	ruvector-coherence (new)	-
Action Execution	ruvector-coherence (new)	ruvector-nervous-system
Distributed Consensus	ruvector-raft	-

---

Bounded Contexts

Context Map

┌─────────────────────────────────────────────────────────────────────────────┐
│                              COHERENCE ENGINE                                │
│                                                                              │
│  ┌──────────────────┐      ┌──────────────────┐      ┌──────────────────┐  │
│  │                  │      │                  │      │                  │  │
│  │  SIGNAL          │─────▶│  KNOWLEDGE       │─────▶│  COHERENCE       │  │
│  │  INGESTION       │      │  SUBSTRATE       │      │  COMPUTATION     │  │
│  │                  │      │                  │      │                  │  │
│  └──────────────────┘      └──────────────────┘      └────────┬─────────┘  │
│           │                         │                          │            │
│           │                         │                          │            │
│           ▼                         ▼                          ▼            │
│  ┌──────────────────────────────────────────────────────────────────────┐  │
│  │                           GOVERNANCE                                  │  │
│  │  Policy Bundles │ Witness Records │ Lineage Records │ Audit Trail    │  │
│  └──────────────────────────────────────────────────────────────────────┘  │
│                                      │                                      │
│                                      ▼                                      │
│  ┌──────────────────────────────────────────────────────────────────────┐  │
│  │                        ACTION EXECUTION                               │  │
│  │  Coherence Gate │ Compute Ladder │ Escalation │ Side Effects         │  │
│  └──────────────────────────────────────────────────────────────────────┘  │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Context Relationships:
─────▶  Upstream/Downstream (Published Language)

---

Bounded Context 0: Tile Fabric (cognitum-gate-kernel)

Purpose

Provides the distributed computation substrate using 256 WASM tiles, each maintaining a local graph shard with evidence accumulation and witness fragments.

Ubiquitous Language

Term	Definition
Tile	A ~64KB WASM kernel instance processing a graph shard
Delta	Incremental update (edge add/remove, observation, weight change)
E-Value	Evidence value for sequential hypothesis testing
Witness Fragment	Local contribution to global min-cut witness
Tick	One deterministic processing cycle of a tile

Aggregates (from cognitum-gate-kernel)

TileState (Aggregate Root)

// Directly from cognitum-gate-kernel
use cognitum_gate_kernel::{TileState, Delta, WitnessFragment, TileReport};

/// Adapter for coherence engine integration
pub struct CoherenceTile {
    /// The underlying tile state
    inner: TileState,
    /// Mapping to global node IDs
    node_map: HashMap<LocalNodeId, GlobalNodeId>,
}

impl CoherenceTile {
    /// Create a new coherence tile
    pub fn new(tile_id: u8) -> Self {
        Self {
            inner: TileState::new(tile_id),
            node_map: HashMap::new(),
        }
    }

    /// Ingest a sheaf graph update as a tile delta
    pub fn ingest_node_update(&mut self, node_id: GlobalNodeId, state: &[f32]) -> bool {
        let local_id = self.node_map.entry(node_id)
            .or_insert_with(|| self.allocate_local_id());

        let delta = Delta::observation(Observation::state_update(*local_id, state));
        self.inner.ingest_delta(&delta)
    }

    /// Execute tick and return report
    pub fn tick(&mut self, tick_number: u32) -> TileReport {
        self.inner.tick(tick_number)
    }

    /// Get witness fragment for aggregation
    pub fn witness_fragment(&self) -> WitnessFragment {
        self.inner.get_witness_fragment()
    }

    /// Get current e-value (evidence against coherence hypothesis)
    pub fn e_value(&self) -> f64 {
        self.inner.evidence.global_e_value()
    }
}

TileFabric (Domain Service)

/// Orchestrates 256 tiles for distributed coherence
pub struct TileFabric {
    tiles: Vec<CoherenceTile>,
    shard_strategy: ShardStrategy,
}

impl TileFabric {
    /// Create fabric with 256 tiles
    pub fn new(strategy: ShardStrategy) -> Self {
        Self {
            tiles: (0..256).map(|i| CoherenceTile::new(i as u8)).collect(),
            shard_strategy: strategy,
        }
    }

    /// Distribute node to appropriate tile based on sharding strategy
    pub fn route_node(&self, node_id: GlobalNodeId) -> u8 {
        self.shard_strategy.tile_for(node_id)
    }

    /// Parallel tick across all tiles
    pub fn tick_all(&mut self, tick_number: u32) -> FabricReport {
        let reports: Vec<TileReport> = self.tiles
            .par_iter_mut()
            .map(|tile| tile.tick(tick_number))
            .collect();

        FabricReport::aggregate(reports)
    }

    /// Collect all witness fragments
    pub fn collect_witnesses(&self) -> Vec<WitnessFragment> {
        self.tiles.iter()
            .map(|t| t.witness_fragment())
            .collect()
    }
}

Domain Events

Event	Trigger	Consumers
DeltaIngested	Tile receives update	Tile processing
TickCompleted	Tile finishes tick	Fabric aggregation
WitnessGenerated	Tick produces witness	Global aggregation
EvidenceThresholdCrossed	E-value exceeds limit	Escalation

---

Bounded Context 0.5: Adaptive Learning (sona)

Purpose

Provides self-optimizing threshold tuning using SONA's three learning loops, with EWC++ to prevent catastrophic forgetting when adapting to new operational regimes.

Ubiquitous Language

Term	Definition
Trajectory	A sequence of energy observations during an operational regime
Micro-LoRA	Ultra-low rank (1-2) adaptation for instant learning (<0.05ms)
Base-LoRA	Standard LoRA for background learning
EWC++	Elastic Weight Consolidation preventing catastrophic forgetting
ReasoningBank	Pattern storage for past successful configurations

Aggregates

ThresholdLearner (Aggregate Root)

use sona::{
    SonaEngine, SonaConfig, TrajectoryBuilder, TrajectoryStep,
    MicroLoRA, BaseLoRA, EwcPlusPlus, ReasoningBank,
};

/// Adaptive threshold learning using SONA
pub struct ThresholdLearner {
    /// SONA engine
    engine: SonaEngine,
    /// Current thresholds
    thresholds: ThresholdConfig,
    /// Active trajectory (if any)
    active_trajectory: Option<TrajectoryBuilder>,
    /// Pattern bank for successful configurations
    patterns: ReasoningBank,
}

impl ThresholdLearner {
    pub fn new(hidden_dim: usize) -> Self {
        let config = SonaConfig {
            hidden_dim,
            embedding_dim: hidden_dim,
            ..Default::default()
        };

        Self {
            engine: SonaEngine::new(config),
            thresholds: ThresholdConfig::default(),
            active_trajectory: None,
            patterns: ReasoningBank::new(PatternConfig::default()),
        }
    }

    /// Start learning when entering new regime
    pub fn begin_regime(&mut self, initial_energy: Vec<f32>) {
        self.active_trajectory = Some(self.engine.begin_trajectory(initial_energy));
    }

    /// Record an observation during the regime
    pub fn observe(&mut self, energy: Vec<f32>, action_taken: Vec<f32>, quality: f32) {
        if let Some(ref mut traj) = self.active_trajectory {
            traj.add_step(energy, action_taken, quality);
        }
    }

    /// End regime and learn from outcome
    pub fn end_regime(&mut self, final_quality: f32) -> DomainEvent {
        if let Some(traj) = self.active_trajectory.take() {
            // Triggers Micro-LoRA instant adaptation
            self.engine.end_trajectory(traj, final_quality);

            if final_quality > 0.8 {
                // Store successful pattern
                self.patterns.store(
                    PatternType::Threshold,
                    &self.thresholds.to_embedding(),
                );
                return DomainEvent::PatternLearned { quality: final_quality };
            }
        }
        DomainEvent::RegimeEnded { quality: final_quality }
    }

    /// Find similar past regime
    pub fn recall_similar(&self, current_energy: &[f32]) -> Option<ThresholdConfig> {
        self.patterns.query(current_energy, 5)
            .first()
            .map(|p| ThresholdConfig::from_embedding(&p.embedding))
    }

    /// Consolidate to prevent forgetting
    pub fn consolidate(&mut self) {
        // EWC++ preserves important weights
        self.engine.consolidate_ewc();
    }

    /// Apply instant adaptation (Micro-LoRA)
    pub fn instant_adapt(&mut self, energy_spike: f32) -> ThresholdAdjustment {
        let input = vec![energy_spike; self.engine.config().hidden_dim];
        let mut output = vec![0.0; self.engine.config().hidden_dim];

        self.engine.apply_micro_lora(&input, &mut output);

        ThresholdAdjustment::from_embedding(&output)
    }
}

Domain Events

Event	Trigger	Consumers
RegimeStarted	New operational regime detected	TrajectoryBuilder
ObservationRecorded	Energy observation added	Active trajectory
RegimeEnded	Regime completed	Learning consolidation
PatternLearned	Successful pattern stored	ReasoningBank
ThresholdAdapted	Micro-LoRA adaptation	Gate configuration

---

Bounded Context 0.7: Neural Gating (ruvector-nervous-system)

Purpose

Provides biologically-inspired gating using the existing CoherenceGatedSystem, GlobalWorkspace for conscious access, and HDC for compact witness encoding.

Ubiquitous Language

Term	Definition
CoherenceGatedSystem	Pre-existing neural gating from ruvector-nervous-system
GlobalWorkspace	Conscious broadcast mechanism for significant decisions
Hypervector	10K-dimensional binary vector for similarity-preserving encoding
Dendrite	Coincidence detector requiring multiple inputs within time window
Plateau Potential	Threshold firing when dendritic conditions met

Aggregates

NeuralGate (Adapter to existing CoherenceGatedSystem)

use ruvector_nervous_system::{
    CoherenceGatedSystem, GlobalWorkspace, HysteresisTracker,
    OscillatoryRouter, Dendrite, DendriticTree,
    HdcMemory, Hypervector,
};

/// Neural gating using existing ruvector-nervous-system
pub struct NeuralGate {
    /// The existing coherence-gated system
    system: CoherenceGatedSystem,
    /// Global workspace for broadcast
    workspace: GlobalWorkspace,
    /// Hysteresis to prevent oscillation
    hysteresis: HysteresisTracker,
    /// Dendritic coincidence detection
    dendrites: DendriticTree,
    /// HDC memory for witnesses
    hdc: HdcMemory,
}

impl NeuralGate {
    /// Evaluate with biological gating
    pub fn evaluate(&mut self, energy: f32, evidence: &[EvidenceSource]) -> NeuralDecision {
        // Feed evidence to dendrites
        for (i, src) in evidence.iter().enumerate() {
            if src.is_active() {
                self.dendrites.receive_spike(i, src.timestamp);
            }
        }

        // Check for plateau potential (coincidence detection)
        let plateau = self.dendrites.update(evidence[0].timestamp, 1.0);

        // Apply hysteresis to prevent oscillation
        let decision = self.hysteresis.filter(energy, plateau);

        // Broadcast significant decisions
        if decision.is_significant() {
            self.workspace.broadcast(decision.clone());
        }

        decision
    }

    /// Encode witness as hypervector
    pub fn encode_witness(&mut self, witness: &WitnessRecord) -> WitnessHypervector {
        let energy_hv = Hypervector::from_scalar(witness.energy_snapshot.total_energy);
        let decision_hv = Hypervector::random(); // Seed from decision
        let policy_hv = Hypervector::from_bytes(&witness.policy_bundle_ref.as_bytes());

        let encoded = energy_hv.bind(&decision_hv).bind(&policy_hv);

        self.hdc.store(&witness.id.to_string(), encoded.clone());

        WitnessHypervector(encoded)
    }

    /// Find similar past decisions
    pub fn find_similar(&self, query: &Hypervector, threshold: f32) -> Vec<WitnessId> {
        self.hdc.retrieve(query, threshold)
            .into_iter()
            .filter_map(|(id, _)| WitnessId::parse(&id).ok())
            .collect()
    }
}

Domain Events

Event	Trigger	Consumers
PlateauTriggered	Dendritic coincidence	Decision evaluation
DecisionBroadcast	Significant decision	GlobalWorkspace subscribers
WitnessEncoded	HDC encoding complete	Similarity search

---

Bounded Context 1: Signal Ingestion

Purpose

Validates and normalizes incoming events before they enter the knowledge substrate.

Ubiquitous Language

Term	Definition
Signal	Raw incoming event from external system
Normalized Event	Validated, typed event ready for processing
Event Schema	Contract defining valid event structure
Ingestion Pipeline	Sequence of validation and transformation steps

Aggregates

SignalProcessor

/// Root aggregate for signal processing
pub struct SignalProcessor {
    id: ProcessorId,
    schemas: HashMap<EventType, EventSchema>,
    validators: Vec<Box<dyn Validator>>,
    transformers: Vec<Box<dyn Transformer>>,
}

impl SignalProcessor {
    /// Process a raw signal into a normalized event
    pub fn process(&self, signal: RawSignal) -> Result<NormalizedEvent, IngestionError> {
        // Validate against schema
        let schema = self.schemas.get(&signal.event_type)
            .ok_or(IngestionError::UnknownEventType)?;
        schema.validate(&signal)?;

        // Run validators
        for validator in &self.validators {
            validator.validate(&signal)?;
        }

        // Transform to normalized form
        let mut event = NormalizedEvent::from(signal);
        for transformer in &self.transformers {
            event = transformer.transform(event)?;
        }

        Ok(event)
    }
}

Domain Events

Event	Trigger	Consumers
SignalReceived	External signal arrives	SignalProcessor
EventNormalized	Validation passes	Knowledge Substrate
SignalRejected	Validation fails	Monitoring, Alerting

Integration Patterns

• Anti-Corruption Layer: Translates external formats to internal domain model
• Published Language: JSON Schema for event contracts

---

Bounded Context 2: Knowledge Substrate

Purpose

Maintains the sheaf graph representing system state with nodes, edges, and restriction maps. The same substrate serves all domains - nodes can be facts, trades, vitals, devices, hypotheses, or beliefs; edges can encode citations, causality, physiology, policy, or physics.

Ubiquitous Language

Term	Definition
Sheaf Node	Entity with fixed-dimensional state vector (domain-agnostic: facts, trades, vitals, hypotheses, beliefs)
Sheaf Edge	Constraint between two nodes via restriction maps (domain-agnostic: citations, causality, physiology, policy, physics)
Restriction Map	Lightweight linear transformation encoding how one state constrains another
Stalk	The state vector at a node (sheaf terminology)
Section	A consistent assignment of states to nodes
Residual	Contradiction energy - mismatch between projected states

Aggregates

SheafGraph (Aggregate Root)

/// The sheaf graph representing system state
pub struct SheafGraph {
    id: GraphId,
    nodes: HashMap<NodeId, SheafNode>,
    edges: HashMap<EdgeId, SheafEdge>,
    namespaces: HashMap<NamespaceId, NodeSet>,
    version: Version,
    fingerprint: Hash,
}

impl SheafGraph {
    /// Add or update a node's state
    pub fn upsert_node(&mut self, node: SheafNode) -> DomainEvent {
        let existed = self.nodes.insert(node.id, node.clone()).is_some();
        self.update_fingerprint();

        if existed {
            DomainEvent::NodeUpdated { node_id: node.id }
        } else {
            DomainEvent::NodeCreated { node_id: node.id }
        }
    }

    /// Add an edge with restriction maps
    pub fn add_edge(&mut self, edge: SheafEdge) -> Result<DomainEvent, DomainError> {
        // Validate nodes exist
        if !self.nodes.contains_key(&edge.source) {
            return Err(DomainError::NodeNotFound(edge.source));
        }
        if !self.nodes.contains_key(&edge.target) {
            return Err(DomainError::NodeNotFound(edge.target));
        }

        // Validate dimension compatibility
        let source_dim = self.nodes[&edge.source].state.len();
        let target_dim = self.nodes[&edge.target].state.len();

        if edge.rho_source.input_dim() != source_dim {
            return Err(DomainError::DimensionMismatch);
        }
        if edge.rho_target.input_dim() != target_dim {
            return Err(DomainError::DimensionMismatch);
        }

        self.edges.insert(edge.id, edge.clone());
        self.update_fingerprint();

        Ok(DomainEvent::EdgeCreated { edge_id: edge.id })
    }

    /// Get subgraph for localized computation
    pub fn subgraph(&self, center: NodeId, hops: usize) -> SheafSubgraph {
        let node_ids = self.bfs_neighbors(center, hops);
        SheafSubgraph {
            nodes: node_ids.iter()
                .filter_map(|id| self.nodes.get(id).cloned())
                .collect(),
            edges: self.edges.values()
                .filter(|e| node_ids.contains(&e.source) && node_ids.contains(&e.target))
                .cloned()
                .collect(),
        }
    }
}

SheafNode (Entity)

/// A node in the sheaf graph
pub struct SheafNode {
    id: NodeId,
    state: Vec<f32>,
    metadata: NodeMetadata,
    namespace: NamespaceId,
    created_at: Timestamp,
    updated_at: Timestamp,
}

impl SheafNode {
    /// Invariant: state dimension is fixed after creation
    pub fn update_state(&mut self, new_state: Vec<f32>) -> Result<(), DomainError> {
        if new_state.len() != self.state.len() {
            return Err(DomainError::DimensionMismatch);
        }
        self.state = new_state;
        self.updated_at = Timestamp::now();
        Ok(())
    }
}

RestrictionMap (Value Object)

/// Linear restriction map: y = Ax + b
#[derive(Clone, PartialEq)]
pub struct RestrictionMap {
    matrix: Matrix,  // m x n where n = input_dim, m = output_dim
    bias: Vec<f32>,  // m-dimensional
}

impl RestrictionMap {
    pub fn new(matrix: Matrix, bias: Vec<f32>) -> Result<Self, DomainError> {
        if matrix.nrows() != bias.len() {
            return Err(DomainError::DimensionMismatch);
        }
        Ok(Self { matrix, bias })
    }

    pub fn apply(&self, input: &[f32]) -> Vec<f32> {
        let result = &self.matrix * &DVector::from_slice(input);
        result.iter()
            .zip(self.bias.iter())
            .map(|(a, b)| a + b)
            .collect()
    }

    pub fn input_dim(&self) -> usize { self.matrix.ncols() }
    pub fn output_dim(&self) -> usize { self.matrix.nrows() }
}

Domain Events

Event	Trigger	Consumers
NodeCreated	New node added	Coherence Computation
NodeUpdated	Node state changed	Coherence Computation (incremental)
EdgeCreated	New constraint added	Coherence Computation
SubgraphExtracted	Localized computation needed	Coherence Computation

Repository Interface

#[async_trait]
pub trait SheafGraphRepository {
    async fn find_by_id(&self, id: GraphId) -> Option<SheafGraph>;
    async fn save(&self, graph: &SheafGraph) -> Result<(), PersistenceError>;
    async fn find_nodes_by_namespace(&self, ns: NamespaceId) -> Vec<SheafNode>;
    async fn find_similar_nodes(&self, state: &[f32], k: usize) -> Vec<(NodeId, f32)>;
}

---

Bounded Context 3: Coherence Computation

Purpose

Computes edge residuals, aggregates energy, and detects structural inconsistencies. This maintains a continuously updated field of coherence that shows where action is safe and where action must stop.

Key Principle: Incoherence is computed incrementally as edge-level residuals and aggregated into scoped coherence energy using a sheaf Laplacian operator.

Ubiquitous Language

Term	Definition
Residual	Contradiction energy at an edge: r_e = ρ_u(x_u) - ρ_v(x_v)
Energy	Global incoherence measure: E = Σ w_e|r_e|² (lower = more coherent)
Coherence Field	Continuously updated map showing safe vs. unsafe action regions
Coherence	Inverse of energy; high coherence = low energy
Fingerprint	Hash summarizing graph state for change detection
Spectral Drift	Change in eigenvalue distribution indicating structural shift

Aggregates

CoherenceEngine (Aggregate Root)

/// The coherence computation engine
pub struct CoherenceEngine {
    id: EngineId,
    /// Cached residuals for incremental computation
    residual_cache: HashMap<EdgeId, Vec<f32>>,
    /// Subgraph energy summaries
    summary_cache: HashMap<SubgraphId, f32>,
    /// Global fingerprint
    fingerprint: Hash,
    /// Configuration
    config: CoherenceConfig,
}

impl CoherenceEngine {
    /// Compute full coherence energy for a graph
    pub fn compute_energy(&mut self, graph: &SheafGraph) -> CoherenceEnergy {
        // Check if we can use incremental computation
        if self.fingerprint == graph.fingerprint {
            return self.cached_energy();
        }

        // Full recomputation
        let edge_energies: HashMap<EdgeId, f32> = graph.edges
            .par_iter()
            .map(|(id, edge)| {
                let residual = self.compute_residual(graph, edge);
                let energy = edge.weight * residual.iter().map(|x| x * x).sum::<f32>();
                self.residual_cache.insert(*id, residual);
                (*id, energy)
            })
            .collect();

        let total = edge_energies.values().sum();
        self.fingerprint = graph.fingerprint;

        CoherenceEnergy {
            total_energy: total,
            edge_energies,
            scope_energies: self.aggregate_by_scope(graph, &edge_energies),
            fingerprint: self.fingerprint,
            computed_at: Timestamp::now(),
        }
    }

    /// Incremental update when a single node changes
    pub fn update_node(&mut self, graph: &SheafGraph, node_id: NodeId) -> CoherenceEnergy {
        let affected_edges = graph.edges_incident_to(node_id);

        for edge_id in &affected_edges {
            let edge = &graph.edges[edge_id];
            let residual = self.compute_residual(graph, edge);
            self.residual_cache.insert(*edge_id, residual);
        }

        self.recompute_from_cache(graph)
    }

    fn compute_residual(&self, graph: &SheafGraph, edge: &SheafEdge) -> Vec<f32> {
        let source_state = &graph.nodes[&edge.source].state;
        let target_state = &graph.nodes[&edge.target].state;

        let projected_source = edge.rho_source.apply(source_state);
        let projected_target = edge.rho_target.apply(target_state);

        projected_source.iter()
            .zip(projected_target.iter())
            .map(|(a, b)| a - b)
            .collect()
    }
}

CoherenceEnergy (Value Object)

/// Immutable snapshot of coherence energy
#[derive(Clone)]
pub struct CoherenceEnergy {
    pub total_energy: f32,
    pub edge_energies: HashMap<EdgeId, f32>,
    pub scope_energies: HashMap<ScopeId, f32>,
    pub fingerprint: Hash,
    pub computed_at: Timestamp,
}

impl CoherenceEnergy {
    /// Get energy for a specific scope
    pub fn scope_energy(&self, scope: &ScopeId) -> f32 {
        self.scope_energies.get(scope).copied().unwrap_or(0.0)
    }

    /// Find edges with highest energy (most incoherent)
    pub fn hotspots(&self, k: usize) -> Vec<(EdgeId, f32)> {
        let mut sorted: Vec<_> = self.edge_energies.iter().collect();
        sorted.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
        sorted.into_iter().take(k).map(|(id, e)| (*id, *e)).collect()
    }
}

Domain Services

SpectralAnalyzer

/// Detects structural drift via eigenvalue analysis
pub struct SpectralAnalyzer {
    history: VecDeque<EigenvalueSnapshot>,
    drift_threshold: f32,
    window_size: usize,
}

impl SpectralAnalyzer {
    /// Analyze eigenvalues for drift detection
    pub fn analyze(&mut self, laplacian: &SheafLaplacian) -> SpectralAnalysis {
        let eigenvalues = laplacian.compute_top_eigenvalues(10);
        let snapshot = EigenvalueSnapshot::new(eigenvalues.clone());

        let drift = if let Some(prev) = self.history.back() {
            self.wasserstein_distance(&snapshot.eigenvalues, &prev.eigenvalues)
        } else {
            0.0
        };

        self.history.push_back(snapshot);
        if self.history.len() > self.window_size {
            self.history.pop_front();
        }

        SpectralAnalysis {
            eigenvalues,
            drift_magnitude: drift,
            is_drifting: drift > self.drift_threshold,
            timestamp: Timestamp::now(),
        }
    }
}

Domain Events

Event	Trigger	Consumers
EnergyComputed	Full computation completes	Governance, Gate
EnergyUpdated	Incremental update completes	Governance, Gate
DriftDetected	Spectral drift exceeds threshold	Alerting, Escalation
HotspotIdentified	Edge energy exceeds threshold	Debugging, Monitoring

---

Bounded Context 4: Governance

Purpose

Manages policy bundles, witness records, and lineage tracking for auditability.

Ubiquitous Language

Term	Definition
Policy Bundle	Versioned, signed collection of threshold configurations
Witness Record	Immutable proof of a gate decision
Lineage Record	Provenance chain for authoritative writes
Approver	Entity authorized to sign policy bundles
Threshold	Energy level triggering escalation

Aggregates

PolicyBundle (Aggregate Root)

/// Versioned, multi-sig policy configuration
pub struct PolicyBundle {
    id: PolicyBundleId,
    version: SemanticVersion,
    thresholds: HashMap<ScopePattern, ThresholdConfig>,
    escalation_rules: Vec<EscalationRule>,
    signatures: Vec<(ApproverId, Signature)>,
    required_approvals: usize,
    status: PolicyStatus,
    created_at: Timestamp,
    activated_at: Option<Timestamp>,
}

impl PolicyBundle {
    /// Invariant: cannot modify after activation
    pub fn add_threshold(&mut self, scope: ScopePattern, config: ThresholdConfig) -> Result<(), DomainError> {
        if self.status != PolicyStatus::Draft {
            return Err(DomainError::PolicyAlreadyActivated);
        }
        self.thresholds.insert(scope, config);
        Ok(())
    }

    /// Add approver signature
    pub fn sign(&mut self, approver: ApproverId, signature: Signature) -> Result<DomainEvent, DomainError> {
        if self.status != PolicyStatus::Draft {
            return Err(DomainError::PolicyAlreadyActivated);
        }

        // Verify signature
        let content_hash = self.content_hash();
        if !signature.verify(&content_hash, &approver) {
            return Err(DomainError::InvalidSignature);
        }

        self.signatures.push((approver, signature));

        // Check if enough signatures
        if self.signatures.len() >= self.required_approvals {
            self.status = PolicyStatus::Approved;
            return Ok(DomainEvent::PolicyApproved { bundle_id: self.id });
        }

        Ok(DomainEvent::PolicySigned { bundle_id: self.id, approver })
    }

    /// Activate the policy (makes it immutable)
    pub fn activate(&mut self) -> Result<DomainEvent, DomainError> {
        if self.status != PolicyStatus::Approved {
            return Err(DomainError::PolicyNotApproved);
        }

        self.status = PolicyStatus::Active;
        self.activated_at = Some(Timestamp::now());

        Ok(DomainEvent::PolicyActivated { bundle_id: self.id })
    }
}

WitnessRecord (Entity)

/// Immutable record of a gate decision
pub struct WitnessRecord {
    id: WitnessId,
    action_hash: Hash,
    energy_snapshot: CoherenceEnergy,
    decision: GateDecision,
    policy_bundle_ref: PolicyBundleRef,
    timestamp: Timestamp,
    previous_witness: Option<WitnessId>,
    content_hash: Hash,
}

impl WitnessRecord {
    pub fn new(
        action: &Action,
        energy: &CoherenceEnergy,
        decision: GateDecision,
        policy_ref: PolicyBundleRef,
        previous: Option<WitnessId>,
    ) -> Self {
        let mut record = Self {
            id: WitnessId::new(),
            action_hash: action.content_hash(),
            energy_snapshot: energy.clone(),
            decision,
            policy_bundle_ref: policy_ref,
            timestamp: Timestamp::now(),
            previous_witness: previous,
            content_hash: Hash::default(),
        };
        record.content_hash = record.compute_content_hash();
        record
    }

    /// Content hash for integrity verification
    fn compute_content_hash(&self) -> Hash {
        let mut hasher = Blake3::new();
        hasher.update(&self.action_hash);
        hasher.update(&self.energy_snapshot.fingerprint);
        hasher.update(&bincode::serialize(&self.decision).unwrap());
        hasher.update(&self.policy_bundle_ref.as_bytes());
        if let Some(prev) = &self.previous_witness {
            hasher.update(&prev.as_bytes());
        }
        hasher.finalize().into()
    }

    /// Verify integrity
    pub fn verify(&self) -> bool {
        self.content_hash == self.compute_content_hash()
    }
}

LineageRecord (Entity)

/// Provenance tracking for writes
pub struct LineageRecord {
    id: LineageId,
    entity_ref: EntityRef,
    operation: Operation,
    dependencies: Vec<LineageId>,
    authorizing_witness: WitnessId,
    actor: ActorId,
    timestamp: Timestamp,
}

impl LineageRecord {
    /// Invariant: must have authorizing witness
    pub fn new(
        entity: EntityRef,
        operation: Operation,
        witness: WitnessId,
        actor: ActorId,
        dependencies: Vec<LineageId>,
    ) -> Self {
        Self {
            id: LineageId::new(),
            entity_ref: entity,
            operation,
            dependencies,
            authorizing_witness: witness,
            actor,
            timestamp: Timestamp::now(),
        }
    }
}

Domain Events

Event	Trigger	Consumers
PolicyCreated	New bundle drafted	Approvers
PolicySigned	Approver signs	Policy lifecycle
PolicyApproved	Enough signatures	Activation
PolicyActivated	Bundle goes live	Gate
WitnessCreated	Gate decision made	Audit, Lineage
LineageCreated	Write authorized	Audit

Invariants

1. No action without witness: Every external action must have a witness_id
2. No write without lineage: Every authoritative write must have a lineage_id
3. Policy immutability: Active policies cannot be modified
4. Signature validity: All policy signatures must verify against content hash
5. Witness chain: Each witness references its predecessor (except first)

---

Bounded Context 5: Action Execution

Purpose

Executes gated side effects with mandatory witness and lineage creation. A deterministic coherence gate controls a compute ladder where most updates stay in a low-latency reflex lane, while sustained/growing incoherence triggers retrieval, deeper reasoning, or human escalation.

Key Principle: All decisions and external side effects are governed by signed policy bundles and produce mandatory witness and lineage records, making every action auditable and replayable.

Ubiquitous Language

Term	Definition
Action	External side effect to be executed
Compute Lane	Escalation level (Reflex → Retrieval → Heavy → Human)
Reflex Lane	THE DEFAULT - most updates stay here (<1ms)
Gate Decision	Allow/deny with required lane and witness
Escalation	Promotion to higher compute lane (triggered by sustained incoherence)
Refusal	Action denied due to incoherence - refusal mechanism with witness
Witness	Mandatory proof of every gate decision

Aggregates

CoherenceGate (Aggregate Root)

/// Gate controlling action execution
pub struct CoherenceGate {
    id: GateId,
    policy_bundle: PolicyBundle,
    energy_history: EnergyHistory,
    pending_escalations: HashMap<ActionId, Escalation>,
}

impl CoherenceGate {
    /// Evaluate whether an action should proceed
    pub fn evaluate(&mut self, action: &Action, energy: &CoherenceEnergy) -> GateDecision {
        let scope = action.scope();
        let current_energy = energy.scope_energy(&scope);

        // Get thresholds from policy
        let thresholds = self.policy_bundle.thresholds_for(&scope);

        // Determine required lane
        let lane = self.determine_lane(current_energy, &thresholds);

        // Check persistence
        let persistent = self.energy_history.is_persistently_above(
            &scope,
            thresholds.retrieval,
            thresholds.persistence_window,
        );

        // Escalate if persistent incoherence
        let final_lane = if persistent && lane < ComputeLane::Heavy {
            ComputeLane::Heavy
        } else {
            lane
        };

        // Record in history
        self.energy_history.record(scope.clone(), current_energy);

        GateDecision {
            allow: final_lane < ComputeLane::Human,
            lane: final_lane,
            reason: self.decision_reason(current_energy, &thresholds, persistent),
        }
    }

    fn determine_lane(&self, energy: f32, thresholds: &ThresholdConfig) -> ComputeLane {
        if energy < thresholds.reflex {
            ComputeLane::Reflex
        } else if energy < thresholds.retrieval {
            ComputeLane::Retrieval
        } else if energy < thresholds.heavy {
            ComputeLane::Heavy
        } else {
            ComputeLane::Human
        }
    }
}

ActionExecutor (Domain Service)

/// Executes actions with governance enforcement
pub struct ActionExecutor {
    gate: CoherenceGate,
    witness_repository: Arc<dyn WitnessRepository>,
    lineage_repository: Arc<dyn LineageRepository>,
}

impl ActionExecutor {
    /// Execute an action with full governance
    pub async fn execute<A: Action>(
        &mut self,
        action: A,
        energy: &CoherenceEnergy,
    ) -> Result<ExecutionResult<A::Output>, ExecutionError> {
        // Evaluate gate
        let decision = self.gate.evaluate(&action, energy);

        // Create witness (always, even for denials)
        let witness = WitnessRecord::new(
            &action,
            energy,
            decision.clone(),
            self.gate.policy_bundle.reference(),
            self.get_previous_witness().await,
        );
        self.witness_repository.save(&witness).await?;

        // Check if allowed
        if !decision.allow {
            return Err(ExecutionError::Denied {
                witness_id: witness.id,
                reason: decision.reason,
            });
        }

        // Execute the action
        let output = action.execute().await?;

        // Create lineage record for any writes
        if let Some(writes) = output.writes() {
            for write in writes {
                let lineage = LineageRecord::new(
                    write.entity_ref(),
                    write.operation(),
                    witness.id,
                    action.actor(),
                    write.dependencies(),
                );
                self.lineage_repository.save(&lineage).await?;
            }
        }

        Ok(ExecutionResult {
            output,
            witness_id: witness.id,
        })
    }
}

Domain Events

Event	Trigger	Consumers
ActionAllowed	Gate allows action	Executor, Monitoring
ActionDenied	Gate denies action	Alerting, Audit
ActionExecuted	Execution completes	Lineage, Monitoring
EscalationTriggered	Persistent incoherence	Human operators

---

Bounded Context 6: Learned Restriction Maps (ruvector-gnn)

Purpose

Enables learning restriction maps (ρ) from data using GNN layers, with EWC to prevent catastrophic forgetting across training epochs.

Ubiquitous Language

Term	Definition
RuvectorLayer	GNN layer implementing learned linear transformation
Replay Buffer	Experience replay for stable training
Fisher Information	Importance weight for EWC regularization

Aggregates

LearnedRestriction (Aggregate Root)

use ruvector_gnn::{
    RuvectorLayer, ElasticWeightConsolidation, ReplayBuffer,
    Optimizer, OptimizerType, LearningRateScheduler, SchedulerType,
    info_nce_loss, local_contrastive_loss,
};

/// Learned restriction map using GNN
pub struct LearnedRestriction {
    /// Neural network layer
    layer: RuvectorLayer,
    /// EWC for forgetting prevention
    ewc: ElasticWeightConsolidation,
    /// Experience replay
    replay: ReplayBuffer,
    /// Adam optimizer
    optimizer: Optimizer,
}

impl LearnedRestriction {
    /// Apply learned restriction
    pub fn apply(&self, state: &[f32]) -> Vec<f32> {
        self.layer.forward(state)
    }

    /// Train on coherent example pair
    pub fn train(&mut self, source: &[f32], target: &[f32], label: CoherenceLabel) {
        // Add to replay buffer
        self.replay.add(ReplayEntry {
            source: source.to_vec(),
            target: target.to_vec(),
            label,
        });

        // Sample batch
        let batch = self.replay.sample(32);

        // Compute contrastive loss
        let loss = local_contrastive_loss(&batch.embeddings(), &batch.labels(), 0.07);

        // Add EWC regularization
        let ewc_loss = self.ewc.compute_ewc_loss(&self.layer);
        let total_loss = loss + 0.4 * ewc_loss;

        // Update
        self.optimizer.step(&mut self.layer, total_loss);
    }

    /// Consolidate after epoch
    pub fn consolidate(&mut self, importance: f32) {
        self.ewc.update_fisher(&self.layer, importance);
    }
}

---

Bounded Context 7: Hyperbolic Coherence (ruvector-hyperbolic-hnsw)

Purpose

Provides hierarchy-aware energy computation where deeper nodes (further from origin in Poincaré ball) have higher coherence expectations.

Ubiquitous Language

Term	Definition
Poincaré Ball	Hyperbolic space model where distance increases toward boundary
Depth	Distance from origin; deeper = further from origin
Curvature	Negative curvature parameter (typically -1.0)
Tangent Space	Local Euclidean approximation for fast pruning

Aggregates

HyperbolicGraph (Aggregate Root)

use ruvector_hyperbolic_hnsw::{
    HyperbolicHnsw, HyperbolicHnswConfig, ShardedHyperbolicHnsw,
    poincare_distance, project_to_ball, log_map, exp_map,
    HierarchyMetrics, TangentCache,
};

/// Hyperbolic coherence with hierarchy awareness
pub struct HyperbolicGraph {
    /// Hyperbolic index
    index: ShardedHyperbolicHnsw,
    /// Curvature
    curvature: f32,
    /// Tangent cache for fast pruning
    tangent_cache: TangentCache,
}

impl HyperbolicGraph {
    /// Insert node with automatic depth assignment
    pub fn insert(&mut self, node_id: NodeId, state: Vec<f32>, hierarchy_depth: Option<usize>) {
        let projected = project_to_ball(&state, self.curvature);
        self.index.insert(projected, hierarchy_depth).unwrap();
    }

    /// Compute depth-weighted residual energy
    pub fn weighted_residual(&self, edge: &SheafEdge, residual: &[f32]) -> f32 {
        let source_depth = self.depth(&edge.source);
        let target_depth = self.depth(&edge.target);

        // Deeper nodes should be MORE coherent, so weight violations higher
        let depth_weight = 1.0 + ((source_depth + target_depth) / 2.0).ln().max(0.0);

        let norm_sq: f32 = residual.iter().map(|x| x * x).sum();
        edge.weight * norm_sq * depth_weight
    }

    /// Compute node depth (distance from origin)
    fn depth(&self, node_id: &NodeId) -> f32 {
        let state = self.index.get(node_id);
        let origin = vec![0.0; state.len()];
        poincare_distance(&state, &origin, self.curvature)
    }

    /// Build tangent cache for fast neighbor search
    pub fn build_tangent_cache(&mut self) {
        self.index.build_tangent_cache().unwrap();
    }
}

---

Bounded Context 8: Incoherence Isolation (ruvector-mincut)

Purpose

Efficiently isolates incoherent subgraphs using subpolynomial n^o(1) dynamic min-cut algorithms.

Ubiquitous Language

Term	Definition
MinCut	Minimum weight edge set whose removal disconnects graph
Subpolynomial	Update time n^o(1), faster than any polynomial
Witness Tree	Proof structure for cut validity
Cognitive Engine	SNN-based optimization for cuts

Aggregates

IncoherenceIsolator (Aggregate Root)

use ruvector_mincut::{
    SubpolynomialMinCut, SubpolyConfig, MinCutResult, MinCutBuilder,
    CognitiveMinCutEngine, EngineConfig, WitnessTree,
    DynamicGraph, VertexId, Weight,
};

/// Isolates incoherent regions with n^o(1) updates
pub struct IncoherenceIsolator {
    /// Subpolynomial mincut
    mincut: SubpolynomialMinCut,
    /// For SNN-based continuous monitoring
    cognitive: Option<CognitiveMinCutEngine>,
}

impl IncoherenceIsolator {
    /// Build graph from high-energy edges
    pub fn from_energy(energy: &CoherenceEnergy, threshold: f32) -> Self {
        let config = SubpolyConfig::default();
        let mut mincut = SubpolynomialMinCut::new(config);

        for (edge_id, edge_energy) in &energy.edge_energies {
            if *edge_energy > threshold {
                mincut.insert_edge(
                    edge_id.source().into(),
                    edge_id.target().into(),
                    *edge_energy as f64,
                ).ok();
            }
        }

        Self { mincut, cognitive: None }
    }

    /// Find isolation cut
    pub fn isolate(&mut self) -> IsolationResult {
        let result = self.mincut.min_cut();

        IsolationResult {
            cut_value: result.value,
            partition: result.partition,
            cut_edges: result.cut_edges,
            is_exact: result.is_exact,
        }
    }

    /// Dynamic update (amortized n^o(1))
    pub fn update_edge(&mut self, source: u64, target: u64, weight: f64) -> f64 {
        self.mincut.insert_edge(source, target, weight)
            .unwrap_or(self.mincut.min_cut_value())
    }

    /// Enable SNN monitoring
    pub fn enable_cognitive(&mut self, graph: DynamicGraph) {
        self.cognitive = Some(CognitiveMinCutEngine::new(graph, EngineConfig::default()));
    }

    /// Run SNN optimization
    pub fn cognitive_optimize(&mut self, ticks: u32) -> Vec<Spike> {
        self.cognitive.as_mut()
            .map(|c| c.run(ticks))
            .unwrap_or_default()
    }
}

---

Bounded Context 9: Attention-Weighted Coherence (ruvector-attention)

Purpose

Weights residuals by structural importance using topology-gated attention, MoE routing, and PDE-based diffusion.

Ubiquitous Language

Term	Definition
TopologyGatedAttention	Attention that considers graph structure
MoE	Mixture of Experts for specialized processing
PDE Attention	Diffusion-based energy propagation
Flash Attention	Memory-efficient attention computation

Aggregates

AttentionWeighter (Aggregate Root)

use ruvector_attention::{
    TopologyGatedAttention, TopologyGatedConfig, AttentionMode,
    MoEAttention, MoEConfig, Expert, TopKRouting,
    DiffusionAttention, DiffusionConfig, GraphLaplacian,
    FlashAttention, AttentionMask,
};

/// Attention-weighted coherence
pub struct AttentionWeighter {
    /// Topology-gated attention
    topo: TopologyGatedAttention,
    /// MoE for specialized weighting
    moe: MoEAttention,
    /// PDE diffusion
    diffusion: DiffusionAttention,
}

impl AttentionWeighter {
    /// Compute attention scores for nodes
    pub fn compute_scores(&self, states: &[&[f32]]) -> HashMap<NodeId, f32> {
        self.topo.compute_scores(states)
    }

    /// Weight residuals by attention
    pub fn weight_residuals(
        &self,
        residuals: &HashMap<EdgeId, Vec<f32>>,
        attention: &HashMap<NodeId, f32>,
    ) -> HashMap<EdgeId, f32> {
        residuals.iter()
            .map(|(edge_id, r)| {
                let src_attn = attention.get(&edge_id.source()).unwrap_or(&1.0);
                let tgt_attn = attention.get(&edge_id.target()).unwrap_or(&1.0);
                let weight = (src_attn + tgt_attn) / 2.0;

                let norm: f32 = r.iter().map(|x| x * x).sum();
                (*edge_id, norm * weight)
            })
            .collect()
    }

    /// Route through MoE
    pub fn moe_process(&self, residual: &[f32]) -> Vec<f32> {
        self.moe.forward(residual)
    }

    /// Diffuse energy across graph
    pub fn diffuse(&self, energy: &mut CoherenceEnergy, steps: usize) {
        self.diffusion.propagate(energy, steps);
    }
}

---

Bounded Context 10: Distributed Consensus (ruvector-raft)

Purpose

Synchronizes sheaf state across multiple nodes using Raft consensus for fault-tolerant distributed coherence.

Ubiquitous Language

Term	Definition
Leader	Node responsible for log replication
Follower	Node receiving replicated entries
Log Entry	Serialized graph update
Commit	Entry replicated to majority

Aggregates

DistributedSheaf (Aggregate Root)

use ruvector_raft::{RaftNode, RaftConfig, LogEntry, ConsensusState};

/// Distributed sheaf graph with Raft consensus
pub struct DistributedSheaf {
    /// Local Raft node
    raft: RaftNode,
    /// Local graph copy
    local: SheafGraph,
}

impl DistributedSheaf {
    /// Propose update to cluster
    pub async fn propose(&mut self, update: GraphUpdate) -> Result<(), ConsensusError> {
        let entry = LogEntry::new(bincode::serialize(&update)?);
        self.raft.propose(entry).await
    }

    /// Apply committed entries
    pub fn apply_committed(&mut self) {
        while let Some(entry) = self.raft.next_committed() {
            let update: GraphUpdate = bincode::deserialize(&entry.data).unwrap();
            self.local.apply(update);
        }
    }

    /// Global energy (leader aggregates)
    pub async fn global_energy(&self) -> Result<f32, ConsensusError> {
        if self.raft.is_leader() {
            let local = self.local.compute_energy().total_energy;
            let remote: f32 = self.raft.collect_from_followers(|n| n.local_energy()).await?
                .into_iter().sum();
            Ok(local + remote)
        } else {
            self.raft.forward_to_leader(Query::GlobalEnergy).await
        }
    }
}

---

Cross-Cutting Concerns

Event Sourcing

All domain events are persisted to the event log for deterministic replay:

pub struct EventLog {
    storage: PostgresEventStore,
}

impl EventLog {
    /// Append event with signature
    pub async fn append(&self, event: DomainEvent, signer: &Signer) -> Result<SequenceId, Error> {
        let payload = bincode::serialize(&event)?;
        let signature = signer.sign(&payload);

        self.storage.insert(EventRecord {
            event_type: event.event_type(),
            payload,
            signature,
            timestamp: Timestamp::now(),
        }).await
    }

    /// Replay events from a sequence point
    pub async fn replay_from(&self, seq: SequenceId) -> impl Stream<Item = DomainEvent> {
        self.storage.stream_from(seq)
            .map(|record| bincode::deserialize(&record.payload).unwrap())
    }
}

Multi-Tenancy

Isolation at data, policy, and execution boundaries:

pub struct TenantContext {
    tenant_id: TenantId,
    namespace_prefix: String,
    policy_bundle: PolicyBundleRef,
    resource_limits: ResourceLimits,
}

impl TenantContext {
    /// Scope a graph query to this tenant
    pub fn scope_query(&self, query: Query) -> Query {
        query.with_namespace_prefix(&self.namespace_prefix)
    }
}

Observability

pub struct CoherenceMetrics {
    /// Energy by scope
    energy_gauge: GaugeVec,
    /// Gate decisions
    gate_decisions: CounterVec,
    /// Computation latency
    compute_latency: HistogramVec,
    /// Witness creation rate
    witness_rate: Counter,
}

impl CoherenceMetrics {
    pub fn record_energy(&self, scope: &ScopeId, energy: f32) {
        self.energy_gauge.with_label_values(&[scope.as_str()]).set(energy as f64);
    }

    pub fn record_gate_decision(&self, lane: ComputeLane, allowed: bool) {
        let labels = [lane.as_str(), if allowed { "allowed" } else { "denied" }];
        self.gate_decisions.with_label_values(&labels).inc();
    }
}

---

Module Structure

crates/ruvector-coherence/
├── Cargo.toml
├── README.md
├── src/
│   ├── lib.rs                    # Public API exports
│   │
│   ├── tiles/                    # Tile Fabric (cognitum-gate-kernel)
│   │   ├── mod.rs
│   │   ├── fabric.rs             # TileFabric orchestrator
│   │   ├── adapter.rs            # CoherenceTile adapter
│   │   ├── shard.rs              # Sharding strategy
│   │   └── witness_aggregator.rs # Fragment aggregation
│   │
│   ├── sona_tuning/              # Adaptive Learning (sona)
│   │   ├── mod.rs
│   │   ├── learner.rs            # ThresholdLearner aggregate
│   │   ├── coordinator.rs        # Three-loop coordinator
│   │   └── patterns.rs           # Pattern extraction
│   │
│   ├── neural_gate/              # Neural Gating (ruvector-nervous-system)
│   │   ├── mod.rs
│   │   ├── gate.rs               # NeuralGate adapter
│   │   ├── hdc.rs                # HDC witness encoding
│   │   └── dendrite.rs           # Coincidence detection
│   │
│   ├── learned_rho/              # Learned Restriction (ruvector-gnn)
│   │   ├── mod.rs
│   │   ├── restriction.rs        # LearnedRestriction aggregate
│   │   ├── training.rs           # Training pipeline
│   │   └── ewc.rs                # EWC integration
│   │
│   ├── hyperbolic/               # Hyperbolic Coherence (ruvector-hyperbolic-hnsw)
│   │   ├── mod.rs
│   │   ├── graph.rs              # HyperbolicGraph aggregate
│   │   ├── depth.rs              # Depth computation
│   │   └── weighting.rs          # Hierarchy weighting
│   │
│   ├── mincut/                   # Incoherence Isolation (ruvector-mincut)
│   │   ├── mod.rs
│   │   ├── isolator.rs           # IncoherenceIsolator aggregate
│   │   ├── cognitive.rs          # SNN optimization
│   │   └── witness.rs            # Cut witness
│   │
│   ├── attention/                # Attention Weighting (ruvector-attention)
│   │   ├── mod.rs
│   │   ├── weighter.rs           # AttentionWeighter aggregate
│   │   ├── moe.rs                # MoE routing
│   │   └── diffusion.rs          # PDE propagation
│   │
│   ├── distributed/              # Distributed Consensus (ruvector-raft)
│   │   ├── mod.rs
│   │   ├── sheaf.rs              # DistributedSheaf aggregate
│   │   ├── replication.rs        # Log replication
│   │   └── queries.rs            # Global queries
│   │
│   ├── signal/                   # Signal Ingestion context
│   │   ├── mod.rs
│   │   ├── processor.rs          # SignalProcessor aggregate
│   │   ├── schema.rs             # EventSchema value object
│   │   └── validators.rs         # Validation chain
│   │
│   ├── substrate/                # Knowledge Substrate context
│   │   ├── mod.rs
│   │   ├── graph.rs              # SheafGraph aggregate
│   │   ├── node.rs               # SheafNode entity
│   │   ├── edge.rs               # SheafEdge entity
│   │   ├── restriction.rs        # RestrictionMap value object
│   │   └── repository.rs         # Repository trait
│   │
│   ├── coherence/                # Coherence Computation context
│   │   ├── mod.rs
│   │   ├── engine.rs             # CoherenceEngine aggregate
│   │   ├── energy.rs             # CoherenceEnergy value object
│   │   ├── spectral.rs           # SpectralAnalyzer service
│   │   └── incremental.rs        # Incremental computation
│   │
│   ├── governance/               # Governance context
│   │   ├── mod.rs
│   │   ├── policy.rs             # PolicyBundle aggregate
│   │   ├── witness.rs            # WitnessRecord entity
│   │   ├── lineage.rs            # LineageRecord entity
│   │   └── repository.rs         # Repository traits
│   │
│   ├── execution/                # Action Execution context
│   │   ├── mod.rs
│   │   ├── gate.rs               # CoherenceGate aggregate
│   │   ├── executor.rs           # ActionExecutor service
│   │   ├── action.rs             # Action trait
│   │   └── ladder.rs             # ComputeLane enum
│   │
│   ├── storage/                  # Storage infrastructure
│   │   ├── mod.rs
│   │   ├── postgres.rs           # PostgreSQL implementation
│   │   ├── ruvector.rs           # Ruvector integration
│   │   └── event_log.rs          # Event sourcing
│   │
│   ├── events.rs                 # All domain events
│   ├── error.rs                  # Domain errors
│   └── types.rs                  # Shared types (IDs, timestamps)
│
├── tests/
│   ├── integration/
│   │   ├── tiles_tests.rs        # Tile fabric tests
│   │   ├── sona_tests.rs         # Adaptive learning tests
│   │   ├── neural_tests.rs       # Neural gate tests
│   │   ├── graph_tests.rs
│   │   ├── coherence_tests.rs
│   │   └── governance_tests.rs
│   └── property/
│       ├── coherence_properties.rs
│       ├── hyperbolic_properties.rs
│       └── mincut_properties.rs
│
└── benches/
    ├── tile_bench.rs             # 256-tile throughput
    ├── sona_bench.rs             # Micro-LoRA latency
    ├── mincut_bench.rs           # Subpolynomial verification
    ├── residual_bench.rs
    └── energy_bench.rs

Dependency Graph

ruvector-coherence
├── cognitum-gate-kernel     (tiles/)
├── sona                     (sona_tuning/)
├── ruvector-nervous-system  (neural_gate/)
├── ruvector-gnn             (learned_rho/)
├── ruvector-hyperbolic-hnsw (hyperbolic/)
├── ruvector-mincut          (mincut/)
├── ruvector-attention       (attention/)
├── ruvector-raft            (distributed/)
├── ruvector-core            (substrate/, storage/)
└── ruvector-graph           (substrate/)

---

Testing Strategy

Property-Based Tests

#[quickcheck]
fn residual_symmetry(graph: ArbitraryGraph) -> bool {
    // r_e for edge (u,v) should be negation of r_e for edge (v,u)
    // when restriction maps are transposed
    for edge in graph.edges() {
        let r_forward = edge.residual(&graph);
        let r_reverse = edge.reversed().residual(&graph);

        if !r_forward.iter().zip(r_reverse.iter())
            .all(|(a, b)| (a + b).abs() < 1e-6) {
            return false;
        }
    }
    true
}

#[quickcheck]
fn energy_non_negative(graph: ArbitraryGraph) -> bool {
    let energy = graph.compute_energy();
    energy.total_energy >= 0.0
}

#[quickcheck]
fn consistent_section_zero_energy(section: ConsistentSection) -> bool {
    // A consistent section (where all nodes agree) should have zero energy
    let graph = section.to_graph();
    let energy = graph.compute_energy();
    energy.total_energy < 1e-6
}

Replay Determinism

#[test]
fn replay_produces_identical_state() {
    let events = load_event_log("test_events.log");

    // First replay
    let state1 = replay_events(&events);

    // Second replay
    let state2 = replay_events(&events);

    assert_eq!(state1.fingerprint, state2.fingerprint);
    assert_eq!(state1.energy, state2.energy);
}

Chaos Testing

#[test]
fn throttling_under_chaos() {
    let gate = CoherenceGate::new(test_policy());
    let mut rng = rand::thread_rng();

    for _ in 0..10000 {
        // Random energy spikes
        let energy = if rng.gen_bool(0.1) {
            CoherenceEnergy::random_high(&mut rng)
        } else {
            CoherenceEnergy::random_normal(&mut rng)
        };

        let decision = gate.evaluate(&random_action(), &energy);

        // Verify escalation happens for high energy
        if energy.total_energy > gate.heavy_threshold() {
            assert!(decision.lane >= ComputeLane::Heavy);
        }
    }
}

---

References

1. Evans, E. (2003). "Domain-Driven Design: Tackling Complexity in the Heart of Software."
2. Vernon, V. (2013). "Implementing Domain-Driven Design."
3. Hansen, J., & Ghrist, R. (2019). "Toward a spectral theory of cellular sheaves."
4. Original Architecture Gist: https://gist.github.com/ruvnet/e511e4d7015996d11ab1a1ac6d5876c0