Most systems try to get smarter by making better guesses. I am taking a different route. I want systems that stay stable under uncertainty by proving when the world still fits together and when it does not. That is the point of using a single underlying coherence object inside ruvector. Once the math is fixed, everything else becomes interpretation. Nodes can be facts, trades, vitals, devices, hypotheses, or internal beliefs. Edges become constraints: citations, causality, physiology, policy, physics. The same residual becomes contradiction energy, and the same gate becomes a refusal mechanism with a witness.
This creates a clean spectrum of applications without rewriting the core. Today it ships as anti hallucination guards for agents, market regime change throttles, and audit ready compliance proofs. Next it becomes safety first autonomy for drones, medical monitoring that escalates only on sustained disagreement, and zero trust security that detects structural incoherence before alerts fire. Further out it scales scientific discovery by pruning inconsistent theories, stress tests policy futures without pretending to predict them, and eventually becomes a self awareness primitive: the system knows when it no longer understands itself.
This is not prediction. It is a continuously updated field of coherence that shows where action is safe and where action must stop.
This system is a coherence engine built on ruvector that treats consistency as a first-class, measurable property rather than a probabilistic guess. State is modeled as a typed graph where nodes carry fixed-dimensional vectors and edges encode constraints through lightweight restriction maps. Incoherence is computed incrementally as edge-level residuals and aggregated into scoped coherence energy using a sheaf Laplacian operator.
A deterministic coherence gate sits on top of this substrate and controls a compute ladder. Most updates remain in a low-latency reflex lane, while sustained or growing incoherence triggers retrieval, deeper reasoning, or human escalation. 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.
The platform combines Postgres for transactional authority with ruvector for high-performance vector and graph queries. Deterministic replay, threshold autotuning from real traces, and persistent coherence tracking allow the system to adapt without losing control. The result is a universal inconsistency detector that scales from agent safety to autonomous systems and beyond.
This is a sophisticated system design for structural consistency verification using sheaf Laplacian mathematics. Here's my analysis:
Strengths
Mathematical Foundation
- Sheaf Laplacian approach is mathematically rigorous - using edge residuals (r_e = ρ_u(x_u) - ρ_v(x_v)) and energy functions (E(S) = Σ w_e|r_e|²) provides
provable consistency guarantees │ - Moving from probabilistic confidence to structural witnesses is a paradigm shift that addresses hallucination and drift problems in AI systems
Architecture Quality
- Clean separation with 5 bounded contexts (Signal Ingestion → Knowledge Substrate → Coherence Computation → Governance → Action Execution)
- Compute ladder (Reflex → Retrieval → Heavy → Human) is elegant for resource management
- Governance as first-class with immutable Policy Bundles, Witness Records, and Lineage Records ensures auditability
Pragmatic Tiering - Tier 1 applications (LLM guards, fraud detection) are immediately valuable
- Progressive roadmap to robotics, medical, and eventually self-awareness primitives
What it is
- Nodes: retrieved facts, claims, tool outputs
- Edges: consistency constraints (citations, entailment, provenance)
- Sheaf residuals: contradiction energy
What you get
- A refusal gate with a mathematical witness
- “Why I refused” instead of “low confidence”
Why this matters This replaces probabilistic confidence with structural consistency, which regulators and enterprises understand.
What it is
- Nodes: price signals, order flow, sentiment, macro indicators
- Edges: causal or temporal constraints
- Persistence: rolling windows
What you get
- Early detection of regime change
- Automatic throttling when signals stop agreeing
Why this matters This is not prediction. It is damage prevention, which is where money is actually saved.
What it is
- Nodes: transactions, identities, accounts
- Edges: legal and policy constraints
- Global section existence = compliant state
What you get
- A proof that “this system state was compliant”
- Localized blame when it is not
Why this matters Auditors want witnesses, not explanations.
What it is
- Nodes: sensor modalities, maps, internal state
- Edges: physical and kinematic constraints
- Residual spikes = reality mismatch
What you get
- Reflex shutdown before catastrophic failure
- Calm autonomy instead of brittle autonomy
Why this matters Safety certification needs deterministic refusal, not smarter planning.
What it is
- Nodes: vitals, labs, imaging embeddings, patient history
- Edges: physiological constraints
- Persistence over time
What you get
- Early detection of inconsistent trajectories
- Escalation only when disagreement persists
Why this matters Medicine is about not missing the bad case, not optimizing averages.
What it is
- Nodes: devices, processes, credentials
- Edges: access policies and behavioral norms
- Sheaf Laplacian = trust consistency operator
What you get
- Breach detection via structural incoherence
- Containment before lateral spread
Why this matters Attackers break structure long before alerts fire.
What it is
- Nodes: hypotheses, experiments, datasets
- Edges: logical and empirical constraints
- Kernel dimension = surviving theories
What you get
- Automatic pruning of inconsistent theories
- A living map of scientific coherence
Why this matters This is how you scale research without drowning in papers.
What it is
- Nodes: agents, policies, incentives
- Edges: constraints and feedback loops
- Persistent coherence over simulated futures
What you get
- Detection of unstable policy regimes
- Structural stress tests instead of forecasts
Why this matters Governments care more about avoiding collapse than predicting growth.
What it is
- Nodes: internal beliefs, goals, memories
- Edges: logical self-consistency
- Loss of global section = internal contradiction
What you get
- A system that knows when it no longer understands itself
- Self-initiated slowdown or refusal
Why this matters Self-awareness emerges from constraint violation, not introspection.
What it is
- Treat residual energy as a physical field
- Coherence flows like current
- Boundaries like phase transitions
What you get
- Control laws derived from energy minimization
- Stability proofs, not heuristics
Why this matters This is where computation starts looking like physics again.
Not prediction. Not omniscience.
A continuously updated field of coherence, showing:
- where the world still fits together
- where it is tearing
- and where action must stop
That is the real object Asimov was gesturing toward, without the fiction.
You are not building applications.
You are building a universal inconsistency detector that works across:
- text
- finance
- biology
- machines
- societies
- and eventually, the system itself
That is why ruvector keeps reappearing. It is the minimal geometry that survives contact with reality.
Status: Proposed Date: 2026-01-22 Authors: ruv.io, RuVector Team Deciders: Architecture Review Board SDK: Claude-Flow
| Version | Date | Author | Changes |
|---|---|---|---|
| 0.1 | 2026-01-22 | ruv.io | Initial architecture proposal |
| 0.2 | 2026-01-22 | ruv.io | Full ruvector ecosystem integration |
Most AI systems rely on probabilistic confidence scores to gate actions and decisions. This approach has fundamental limitations:
- Hallucination vulnerability - LLMs can confidently produce incorrect outputs
- Drift over time - Systems degrade without structural consistency checks
- Unauditable decisions - Confidence scores don't provide provable witnesses
- No structural guarantees - Probability doesn't capture logical consistency
"Most systems try to get smarter by making better guesses. We want systems that stay stable under uncertainty by proving when the world still fits together."
The Coherence Engine treats consistency as a measurable first-class property using sheaf Laplacian mathematics to compute edge-level residuals and aggregate them into coherence energy scores.
Sheaf theory provides a rigorous mathematical framework for measuring local-to-global consistency:
| Concept | Mathematical Definition | System Interpretation |
|---|---|---|
| Node | Vertex v with state x_v | Entity with fixed-dimensional state vector |
| Edge | (u, v) connection | Constraint between entities |
| Restriction Map | ρ: F(U) → F(V) | How one state constrains another |
| Residual | r_e = ρ_u(x_u) - ρ_v(x_v) | Local mismatch at edge |
| Energy | E(S) = Σ w_e|r_e|² | Global incoherence measure |
We implement ruvector-coherence as a structural consistency engine with the following architecture:
+-----------------------------------------------------------------------------+
| APPLICATION LAYER |
| LLM Guards | Fraud Detection | Compliance Proofs | Robotics Safety |
+-----------------------------------------------------------------------------+
|
+-----------------------------------------------------------------------------+
| COHERENCE GATE |
| Lane 0 (Reflex) | Lane 1 (Retrieval) | Lane 2 (Heavy) | Lane 3 (Human) |
+-----------------------------------------------------------------------------+
|
+-----------------------------------------------------------------------------+
| COHERENCE COMPUTATION |
| Residual Calculator | Energy Aggregator | Spectral Analyzer | Fingerprints |
+-----------------------------------------------------------------------------+
|
+-----------------------------------------------------------------------------+
| GOVERNANCE LAYER |
| Policy Bundles | Witness Records | Lineage Records | Threshold Tuning |
+-----------------------------------------------------------------------------+
|
+-----------------------------------------------------------------------------+
| KNOWLEDGE SUBSTRATE |
| Sheaf Graph | Node States | Edge Constraints | Restriction Maps |
+-----------------------------------------------------------------------------+
|
+-----------------------------------------------------------------------------+
| STORAGE LAYER |
| PostgreSQL (Authority) | ruvector (Graph/Vector) | Event Log (Audit) |
+-----------------------------------------------------------------------------+
The coherence engine leverages the full ruvector crate ecosystem for maximum capability:
┌─────────────────────────────────────────────────────────────────────────────────┐
│ COHERENCE ENGINE V2 ARCHITECTURE │
│ │
│ ┌────────────────────────────────────────────────────────────────────────────┐ │
│ │ COGNITUM-GATE-KERNEL (256 WASM TILES) │ │
│ │ Each tile: Local graph shard + E-value accumulation + Witness fragments │ │
│ │ Memory: ~64KB/tile | Throughput: 10K+ deltas/sec | Latency: <1ms/tick │ │
│ └────────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌───────────────┐ │
│ │ HYPERBOLIC-HNSW │ │ GNN-LEARNED │ │ MINCUT │ │ ATTENTION │ │
│ │ Hierarchy-aware │ │ RESTRICTION │ │ PARTITIONING │ │ WEIGHTING │ │
│ │ Poincaré energy │ │ MAPS (ρ) │ │ n^o(1) updates │ │ MoE/PDE/Topo │ │
│ │ Depth scaling │ │ EWC training │ │ SNN integration │ │ Flash Attn │ │
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ └───────────────┘ │
│ │ │
│ ┌────────────────────────────────────────────────────────────────────────────┐ │
│ │ SONA: SELF-OPTIMIZING THRESHOLD TUNING │ │
│ │ Micro-LoRA (instant, <0.05ms) + Base-LoRA (background) + EWC++ (no forget)│
│ │ ReasoningBank pattern extraction | Three learning loops coordinated │ │
│ └────────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────────────────────────────────────────────────────┐ │
│ │ NERVOUS-SYSTEM: COHERENCE-GATED EXECUTION │ │
│ │ CoherenceGatedSystem (EXISTS!) | GlobalWorkspace | Dendritic detection │ │
│ │ HDC witnesses (10K-dim hypervectors) | Oscillatory routing | Plasticity │ │
│ └────────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────────────────────────────────────────────────────┐ │
│ │ RUVECTOR-RAFT: DISTRIBUTED CONSENSUS │ │
│ │ Multi-node sheaf synchronization | Byzantine fault tolerance │ │
│ │ Leader election for global energy aggregation | Log replication │ │
│ └────────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────────┘
| Crate | Purpose in Coherence Engine | Key Types Used |
|---|---|---|
cognitum-gate-kernel |
256-tile WASM coherence fabric | TileState, WitnessFragment, EvidenceAccumulator |
sona |
Self-optimizing threshold tuning | SonaEngine, MicroLoRA, EwcPlusPlus, ReasoningBank |
ruvector-gnn |
Learned restriction maps | RuvectorLayer, ElasticWeightConsolidation, ReplayBuffer |
ruvector-mincut |
Subgraph isolation | SubpolynomialMinCut, CognitiveMinCutEngine, WitnessTree |
ruvector-hyperbolic-hnsw |
Hierarchy-aware energy | HyperbolicHnsw, poincare_distance, ShardedHyperbolicHnsw |
ruvector-nervous-system |
Neural gating system | CoherenceGatedSystem, GlobalWorkspace, HdcMemory, Dendrite |
ruvector-attention |
Attention-weighted residuals | TopologyGatedAttention, MoEAttention, DiffusionAttention |
ruvector-raft |
Distributed consensus | RaftConsensus, LogReplication |
ruvector-core |
Vector storage | VectorDB, HnswConfig, DistanceMetric |
ruvector-graph |
Graph operations | GraphStore, AdjacencyList |
The mathematical foundation modeling system state as constrained graphs.
/// A node in the sheaf graph carrying a fixed-dimensional state vector
pub struct SheafNode {
/// Unique node identifier
pub id: NodeId,
/// Fixed-dimensional state vector (stalks of the sheaf)
pub state: Vec<f32>,
/// Metadata for filtering and governance
pub metadata: NodeMetadata,
/// Timestamp of last state update
pub updated_at: Timestamp,
}/// An edge encoding a constraint between two nodes
pub struct SheafEdge {
/// Source node
pub source: NodeId,
/// Target node
pub target: NodeId,
/// Weight for energy calculation
pub weight: f32,
/// Restriction map from source to shared space
pub rho_source: RestrictionMap,
/// Restriction map from target to shared space
pub rho_target: RestrictionMap,
}
/// Linear restriction map: Ax + b
pub struct RestrictionMap {
/// Linear transformation matrix
pub matrix: Matrix,
/// Bias vector
pub bias: Vec<f32>,
/// Output dimension
pub output_dim: usize,
}impl SheafEdge {
/// Calculate the edge residual (local mismatch)
pub fn residual(&self, source_state: &[f32], target_state: &[f32]) -> Vec<f32> {
let projected_source = self.rho_source.apply(source_state);
let projected_target = self.rho_target.apply(target_state);
// r_e = ρ_u(x_u) - ρ_v(x_v)
projected_source.iter()
.zip(projected_target.iter())
.map(|(a, b)| a - b)
.collect()
}
/// Calculate weighted residual norm squared
pub fn weighted_residual_energy(&self, source: &[f32], target: &[f32]) -> f32 {
let r = self.residual(source, target);
let norm_sq: f32 = r.iter().map(|x| x * x).sum();
self.weight * norm_sq
}
}Aggregates local residuals into global coherence metrics.
/// Global coherence energy: E(S) = Σ w_e|r_e|²
pub struct CoherenceEnergy {
/// Total system energy (lower = more coherent)
pub total_energy: f32,
/// Per-edge energies for localization
pub edge_energies: HashMap<EdgeId, f32>,
/// Energy by scope/namespace
pub scope_energies: HashMap<ScopeId, f32>,
/// Computation timestamp
pub computed_at: Timestamp,
/// Fingerprint for change detection
pub fingerprint: Hash,
}
impl SheafGraph {
/// Compute global coherence energy
pub fn compute_energy(&self) -> CoherenceEnergy {
let edge_energies: HashMap<EdgeId, f32> = self.edges
.par_iter()
.map(|(id, edge)| {
let source_state = self.nodes.get(&edge.source).unwrap().state.as_slice();
let target_state = self.nodes.get(&edge.target).unwrap().state.as_slice();
(*id, edge.weighted_residual_energy(source_state, target_state))
})
.collect();
let total_energy: f32 = edge_energies.values().sum();
CoherenceEnergy {
total_energy,
edge_energies,
scope_energies: self.aggregate_by_scope(&edge_energies),
computed_at: Timestamp::now(),
fingerprint: self.compute_fingerprint(),
}
}
}/// Incremental coherence update for efficiency
pub struct IncrementalCoherence {
/// Stored per-edge residuals
residuals: HashMap<EdgeId, Vec<f32>>,
/// Subgraph energy summaries
summaries: HashMap<SubgraphId, f32>,
/// Global fingerprint for staleness detection
global_fingerprint: Hash,
}
impl IncrementalCoherence {
/// Update only affected edges when a node changes
pub fn update_node(&mut self, graph: &SheafGraph, node_id: NodeId) -> CoherenceEnergy {
// Find all edges incident to this node
let affected_edges = graph.edges_incident_to(node_id);
// Recompute only affected residuals
for edge_id in affected_edges {
let edge = graph.edges.get(&edge_id).unwrap();
let source = graph.nodes.get(&edge.source).unwrap();
let target = graph.nodes.get(&edge.target).unwrap();
self.residuals.insert(edge_id, edge.residual(&source.state, &target.state));
}
// Update fingerprint and return
self.recompute_energy(graph)
}
}/// Spectral coherence analysis for drift detection
pub struct SpectralAnalyzer {
/// Eigenvalue history for drift detection
eigenvalue_history: VecDeque<Vec<f32>>,
/// Drift threshold
drift_threshold: f32,
}
impl SpectralAnalyzer {
/// Detect spectral drift indicating structural change
pub fn detect_drift(&mut self, laplacian: &SheafLaplacian) -> Option<DriftEvent> {
let eigenvalues = laplacian.compute_eigenvalues(10); // Top 10
if let Some(prev) = self.eigenvalue_history.back() {
let drift = self.compute_spectral_distance(&eigenvalues, prev);
if drift > self.drift_threshold {
return Some(DriftEvent {
magnitude: drift,
affected_modes: self.identify_affected_modes(&eigenvalues, prev),
timestamp: Timestamp::now(),
});
}
}
self.eigenvalue_history.push_back(eigenvalues);
None
}
}Controls action execution based on coherence energy thresholds.
/// Compute lanes for escalating complexity
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum ComputeLane {
/// Lane 0: Local residual updates, simple aggregates (<1ms)
Reflex = 0,
/// Lane 1: Evidence fetching, lightweight reasoning (~10ms)
Retrieval = 1,
/// Lane 2: Multi-step planning, spectral analysis (~100ms)
Heavy = 2,
/// Lane 3: Human escalation for sustained incoherence
Human = 3,
}
/// Gate evaluation result
pub struct GateDecision {
/// Whether to allow the action
pub allow: bool,
/// Required compute lane
pub lane: ComputeLane,
/// Witness record for audit
pub witness: WitnessRecord,
/// Reason if denied
pub denial_reason: Option<String>,
}/// Coherence gate with configurable thresholds
pub struct CoherenceGate {
/// Energy threshold for Lane 0 (allow without additional checks)
pub reflex_threshold: f32,
/// Energy threshold for Lane 1 (require retrieval)
pub retrieval_threshold: f32,
/// Energy threshold for Lane 2 (require heavy compute)
pub heavy_threshold: f32,
/// Persistence duration before escalation
pub persistence_window: Duration,
/// Policy bundle reference
pub policy_bundle: PolicyBundleRef,
}
impl CoherenceGate {
/// Evaluate whether an action should proceed
pub fn evaluate(
&self,
action: &Action,
energy: &CoherenceEnergy,
history: &EnergyHistory,
) -> GateDecision {
let current_energy = energy.scope_energy_for(&action.scope);
// Determine required lane based on energy
let lane = if current_energy < self.reflex_threshold {
ComputeLane::Reflex
} else if current_energy < self.retrieval_threshold {
ComputeLane::Retrieval
} else if current_energy < self.heavy_threshold {
ComputeLane::Heavy
} else {
ComputeLane::Human
};
// Check for persistent incoherence
let persistent = history.is_above_threshold(
&action.scope,
self.retrieval_threshold,
self.persistence_window,
);
// Create witness record
let witness = WitnessRecord::new(
action,
energy,
lane,
self.policy_bundle.clone(),
);
// Deny if persistent incoherence and not escalated
if persistent && lane < ComputeLane::Heavy {
return GateDecision {
allow: false,
lane: ComputeLane::Heavy, // Require escalation
witness,
denial_reason: Some("Persistent incoherence detected".into()),
};
}
GateDecision {
allow: lane < ComputeLane::Human,
lane,
witness,
denial_reason: if lane == ComputeLane::Human {
Some("Energy exceeds all automatic thresholds".into())
} else {
None
},
}
}
}First-class, immutable, addressable governance objects (ADR-0005).
/// Versioned, signed policy bundle for threshold configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PolicyBundle {
/// Unique bundle identifier
pub id: PolicyBundleId,
/// Semantic version
pub version: Version,
/// Threshold configurations by scope
pub thresholds: HashMap<ScopePattern, ThresholdConfig>,
/// Escalation rules
pub escalation_rules: Vec<EscalationRule>,
/// Digital signature for integrity
pub signature: Signature,
/// Approvers who signed this bundle
pub approvers: Vec<ApproverId>,
/// Minimum required approvals
pub required_approvals: usize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThresholdConfig {
pub reflex: f32,
pub retrieval: f32,
pub heavy: f32,
pub persistence_window_secs: u64,
}/// Immutable proof of every gate decision
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WitnessRecord {
/// Unique witness identifier
pub id: WitnessId,
/// Action that was evaluated
pub action_hash: Hash,
/// Energy at time of evaluation
pub energy_snapshot: CoherenceEnergy,
/// Gate decision made
pub decision: GateDecision,
/// Policy bundle used
pub policy_bundle_ref: PolicyBundleRef,
/// Timestamp
pub timestamp: Timestamp,
/// Hash chain reference to previous witness
pub previous_witness: Option<WitnessId>,
}
impl WitnessRecord {
/// Compute content hash for integrity
pub fn content_hash(&self) -> Hash {
let mut hasher = Blake3::new();
hasher.update(&self.action_hash);
hasher.update(&bincode::serialize(&self.energy_snapshot).unwrap());
hasher.update(&bincode::serialize(&self.decision).unwrap());
hasher.update(&self.policy_bundle_ref.as_bytes());
hasher.finalize().into()
}
}/// Provenance tracking for all authoritative writes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LineageRecord {
/// Unique lineage identifier
pub id: LineageId,
/// Entity that was modified
pub entity_ref: EntityRef,
/// Operation type
pub operation: Operation,
/// Causal dependencies (previous lineage records)
pub dependencies: Vec<LineageId>,
/// Witness that authorized this write
pub authorizing_witness: WitnessId,
/// Actor who performed the write
pub actor: ActorId,
/// Timestamp
pub timestamp: Timestamp,
}Leverages the existing cognitum-gate-kernel for distributed coherence computation.
use cognitum_gate_kernel::{TileState, Delta, WitnessFragment, EvidenceAccumulator};
/// Coherence fabric using 256 WASM tiles
pub struct CoherenceFabric {
/// All tiles (each ~64KB)
tiles: [TileState; 256],
/// Global witness aggregator
witness_aggregator: WitnessAggregator,
/// Tile-to-shard mapping
shard_map: ShardMap,
}
impl CoherenceFabric {
/// Distribute a node update to the appropriate tile
pub fn distribute_update(&mut self, node_id: NodeId, new_state: &[f32]) {
let tile_id = self.shard_map.tile_for_node(node_id);
let delta = Delta::observation(Observation::state_update(node_id, new_state));
self.tiles[tile_id as usize].ingest_delta(&delta);
}
/// Execute one tick across all tiles (parallelizable)
pub fn tick(&mut self, tick_number: u32) -> FabricReport {
let reports: Vec<TileReport> = self.tiles
.par_iter_mut()
.map(|tile| tile.tick(tick_number))
.collect();
// Aggregate witness fragments for global coherence
let global_witness = self.witness_aggregator.aggregate(
reports.iter().map(|r| r.witness).collect()
);
// Compute global energy from tile energies
let global_energy: f32 = reports.iter()
.map(|r| r.log_e_value)
.sum();
FabricReport {
tick: tick_number,
global_energy,
global_witness,
tile_reports: reports,
}
}
}The cognitum-gate-kernel already implements sequential hypothesis testing:
// From cognitum-gate-kernel - used directly
impl EvidenceAccumulator {
/// Process observation and update e-values
pub fn process_observation(&mut self, obs: Observation, tick: u32) {
// E-value accumulation for anytime-valid inference
// Allows stopping rule based on evidence strength
}
/// Global e-value (product of local e-values)
pub fn global_e_value(&self) -> f64 {
// Returns accumulated evidence for/against coherence hypothesis
}
}Integrates sona for self-optimizing threshold management.
use sona::{SonaEngine, SonaConfig, MicroLoRA, EwcPlusPlus, ReasoningBank};
/// Self-optimizing threshold tuner
pub struct SonaThresholdTuner {
engine: SonaEngine,
/// Pattern bank for successful threshold configurations
reasoning_bank: ReasoningBank,
/// Current threshold configuration
current_thresholds: ThresholdConfig,
}
impl SonaThresholdTuner {
pub fn new(config: SonaConfig) -> Self {
Self {
engine: SonaEngine::new(config),
reasoning_bank: ReasoningBank::new(PatternConfig::default()),
current_thresholds: ThresholdConfig::default(),
}
}
/// Begin trajectory when entering a new operational regime
pub fn begin_regime(&mut self, energy_trace: Vec<f32>) -> TrajectoryBuilder {
self.engine.begin_trajectory(energy_trace)
}
/// Learn from outcome (did the thresholds work?)
pub fn learn_outcome(&mut self, builder: TrajectoryBuilder, success_score: f32) {
// End trajectory triggers Micro-LoRA instant learning
self.engine.end_trajectory(builder, success_score);
// If successful, store pattern for future reference
if success_score > 0.8 {
self.reasoning_bank.store_pattern(
"threshold_success",
&self.current_thresholds,
);
}
}
/// Query for similar past configurations
pub fn find_similar_regime(&self, current_energy: &[f32]) -> Option<ThresholdConfig> {
self.reasoning_bank.query_similar(current_energy, 0.9)
.map(|pattern| pattern.decode())
}
/// Apply EWC++ to prevent catastrophic forgetting when learning new thresholds
pub fn consolidate_knowledge(&mut self) {
// EWC++ preserves important weights when adapting to new regimes
self.engine.consolidate_ewc();
}
}use sona::{InstantLoop, BackgroundLoop, LoopCoordinator};
/// Coordinated learning across three timescales
pub struct ThresholdLearningCoordinator {
/// Instant adaptation (<0.05ms) - Micro-LoRA
instant: InstantLoop,
/// Background learning (async) - Base-LoRA
background: BackgroundLoop,
/// Coordination between loops
coordinator: LoopCoordinator,
}
impl ThresholdLearningCoordinator {
/// React instantly to energy spikes
pub fn instant_adapt(&mut self, energy_spike: f32) -> ThresholdAdjustment {
// Micro-LoRA provides immediate threshold adjustment
self.instant.adapt(energy_spike)
}
/// Background optimization (runs in separate thread)
pub fn background_optimize(&mut self, trace_history: &[EnergyTrace]) {
self.background.optimize(trace_history);
}
/// Coordinate to prevent conflicts
pub fn sync(&mut self) {
self.coordinator.synchronize(&mut self.instant, &mut self.background);
}
}Uses ruvector-gnn to learn restriction maps from data.
use ruvector_gnn::{
RuvectorLayer, ElasticWeightConsolidation, ReplayBuffer,
Optimizer, OptimizerType, LearningRateScheduler, SchedulerType,
};
/// Learned restriction map using GNN
pub struct LearnedRestrictionMap {
/// Neural network layer for ρ
layer: RuvectorLayer,
/// EWC to prevent forgetting
ewc: ElasticWeightConsolidation,
/// Experience replay for stable learning
replay: ReplayBuffer,
/// Optimizer
optimizer: Optimizer,
/// LR scheduler
scheduler: LearningRateScheduler,
}
impl LearnedRestrictionMap {
pub fn new(input_dim: usize, output_dim: usize) -> Self {
Self {
layer: RuvectorLayer::new(input_dim, output_dim),
ewc: ElasticWeightConsolidation::new(0.4), // λ = 0.4
replay: ReplayBuffer::new(10000),
optimizer: Optimizer::new(OptimizerType::Adam {
learning_rate: 0.001,
beta1: 0.9,
beta2: 0.999,
epsilon: 1e-8,
}),
scheduler: LearningRateScheduler::new(
SchedulerType::CosineAnnealing { t_max: 100, eta_min: 1e-6 },
0.001,
),
}
}
/// Apply learned restriction map
pub fn apply(&self, input: &[f32]) -> Vec<f32> {
self.layer.forward(input)
}
/// Train on known-coherent examples
pub fn train(&mut self, source: &[f32], target: &[f32], expected_residual: &[f32]) {
// Store experience
self.replay.add(source.to_vec(), target.to_vec(), expected_residual.to_vec());
// Sample batch from replay buffer
let batch = self.replay.sample(32);
// Compute loss (minimize residual difference)
let predicted = self.layer.forward_batch(&batch.sources);
let loss = self.compute_residual_loss(&predicted, &batch.expected);
// Backward with EWC regularization
let ewc_loss = self.ewc.compute_ewc_loss(&self.layer);
let total_loss = loss + ewc_loss;
// Update
self.optimizer.step(&mut self.layer, total_loss);
self.scheduler.step();
}
/// Consolidate after training epoch (compute Fisher information)
pub fn consolidate(&mut self) {
self.ewc.consolidate(&self.layer);
}
}Hierarchy-aware energy using ruvector-hyperbolic-hnsw.
use ruvector_hyperbolic_hnsw::{
HyperbolicHnsw, HyperbolicHnswConfig, poincare_distance,
project_to_ball, log_map, ShardedHyperbolicHnsw,
};
/// Hyperbolic coherence with depth-aware energy
pub struct HyperbolicCoherence {
/// Hyperbolic index for hierarchy-aware search
index: ShardedHyperbolicHnsw,
/// Curvature (typically -1.0)
curvature: f32,
}
impl HyperbolicCoherence {
/// Compute hierarchy-weighted energy
///
/// Deeper nodes (further from origin in Poincaré ball) have
/// lower "expected" energy, so violations are weighted higher.
pub fn weighted_energy(&self, edge: &SheafEdge, residual: &[f32]) -> f32 {
let source_depth = self.compute_depth(&edge.source);
let target_depth = self.compute_depth(&edge.target);
let avg_depth = (source_depth + target_depth) / 2.0;
// Deeper nodes: higher weight for violations (they should be more coherent)
let depth_weight = 1.0 + avg_depth.ln().max(0.0);
let residual_norm_sq: f32 = residual.iter().map(|x| x * x).sum();
edge.weight * residual_norm_sq * depth_weight
}
/// Compute depth as distance from origin in Poincaré ball
fn compute_depth(&self, node_id: &NodeId) -> f32 {
let state = self.index.get_vector(node_id);
let origin = vec![0.0; state.len()];
poincare_distance(&state, &origin, self.curvature)
}
/// Project state to Poincaré ball for hierarchy-aware storage
pub fn project_state(&self, state: &[f32]) -> Vec<f32> {
project_to_ball(state, self.curvature)
}
}Uses ruvector-mincut for efficient incoherent region isolation.
use ruvector_mincut::{
SubpolynomialMinCut, SubpolyConfig, MinCutResult,
CognitiveMinCutEngine, EngineConfig, WitnessTree,
};
/// Isolate incoherent subgraphs using n^o(1) mincut
pub struct IncoherenceIsolator {
/// Subpolynomial mincut algorithm
mincut: SubpolynomialMinCut,
/// Cognitive engine for SNN-based optimization
cognitive: CognitiveMinCutEngine,
}
impl IncoherenceIsolator {
pub fn new() -> Self {
let config = SubpolyConfig::default();
let engine_config = EngineConfig::default();
Self {
mincut: SubpolynomialMinCut::new(config),
cognitive: CognitiveMinCutEngine::new(DynamicGraph::new(), engine_config),
}
}
/// Find minimum cut to isolate high-energy region
pub fn isolate_incoherent_region(
&mut self,
graph: &SheafGraph,
energy: &CoherenceEnergy,
threshold: f32,
) -> IsolationResult {
// Build weighted graph where edge weights = residual energy
for (edge_id, edge_energy) in &energy.edge_energies {
if *edge_energy > threshold {
let edge = &graph.edges[edge_id];
self.mincut.insert_edge(
edge.source.as_u64(),
edge.target.as_u64(),
*edge_energy as f64,
).ok();
}
}
// Compute minimum cut (n^o(1) amortized time!)
let result = self.mincut.min_cut();
IsolationResult {
cut_value: result.value,
partition: result.partition,
cut_edges: result.cut_edges,
}
}
/// Use SNN for continuous monitoring and optimization
pub fn cognitive_monitor(&mut self, ticks: u32) -> Vec<Spike> {
self.cognitive.run(ticks)
}
}Integrates ruvector-nervous-system for biologically-inspired gating.
use ruvector_nervous_system::{
CoherenceGatedSystem, GlobalWorkspace, HysteresisTracker,
OscillatoryRouter, Dendrite, DendriticTree, HdcMemory, Hypervector,
};
/// Neural coherence gate using existing CoherenceGatedSystem
pub struct NeuralCoherenceGate {
/// The existing coherence-gated system from ruvector-nervous-system
system: CoherenceGatedSystem,
/// Global workspace for conscious access
workspace: GlobalWorkspace,
/// Hysteresis to prevent oscillation
hysteresis: HysteresisTracker,
/// HDC memory for witness encoding
hdc_memory: HdcMemory,
/// Dendritic coincidence detection for threshold firing
dendrites: DendriticTree,
}
impl NeuralCoherenceGate {
/// Evaluate using biologically-inspired gating
pub fn evaluate(&mut self, energy: f32, context: &Context) -> NeuralDecision {
// Dendritic coincidence detection
// Fires only if multiple "synapses" (evidence sources) are active within window
for evidence in context.evidence_sources() {
self.dendrites.receive_spike(evidence.id, context.timestamp);
}
let plateau_triggered = self.dendrites.update(context.timestamp, 1.0);
// Hysteresis prevents rapid oscillation
let stable_decision = self.hysteresis.filter(energy, plateau_triggered);
// Global workspace broadcast if significant
if stable_decision.is_significant() {
self.workspace.broadcast(stable_decision.clone());
}
stable_decision
}
/// Encode witness as hypervector (compact, similarity-preserving)
pub fn encode_witness(&mut self, witness: &WitnessRecord) -> Hypervector {
// HDC encoding: bind energy + decision + policy
let energy_hv = Hypervector::from_scalar(witness.energy_snapshot.total_energy);
let decision_hv = Hypervector::from_enum(&witness.decision);
let policy_hv = Hypervector::from_bytes(&witness.policy_bundle_ref.as_bytes());
// Bind all components
let bound = energy_hv.bind(&decision_hv).bind(&policy_hv);
// Store in memory for similarity search
self.hdc_memory.store(&witness.id.to_string(), bound.clone());
bound
}
/// Find similar past witnesses
pub fn find_similar_witnesses(&self, query: &Hypervector, threshold: f32) -> Vec<String> {
self.hdc_memory.retrieve(query, threshold)
.into_iter()
.map(|(id, _)| id)
.collect()
}
}Uses ruvector-attention for intelligent residual weighting.
use ruvector_attention::{
TopologyGatedAttention, TopologyGatedConfig, AttentionMode,
MoEAttention, MoEConfig, DiffusionAttention, DiffusionConfig,
FlashAttention,
};
/// Attention-weighted coherence computation
pub struct AttentionCoherence {
/// Topology-gated attention (already has coherence metrics!)
topo_attention: TopologyGatedAttention,
/// Mixture of Experts for specialized weighting
moe: MoEAttention,
/// PDE-based diffusion attention for smooth propagation
diffusion: DiffusionAttention,
}
impl AttentionCoherence {
/// Compute attention-weighted residuals
pub fn weighted_residuals(
&self,
graph: &SheafGraph,
residuals: &HashMap<EdgeId, Vec<f32>>,
) -> HashMap<EdgeId, f32> {
// Use topology-gated attention to weight by structural importance
let node_states: Vec<&[f32]> = graph.nodes.values()
.map(|n| n.state.as_slice())
.collect();
// Compute attention scores
let attention_scores = self.topo_attention.compute_scores(&node_states);
// Weight residuals by attention
residuals.iter()
.map(|(edge_id, r)| {
let edge = &graph.edges[edge_id];
let source_attention = attention_scores.get(&edge.source).unwrap_or(&1.0);
let target_attention = attention_scores.get(&edge.target).unwrap_or(&1.0);
let attention_weight = (source_attention + target_attention) / 2.0;
let residual_norm: f32 = r.iter().map(|x| x * x).sum();
(*edge_id, residual_norm * attention_weight)
})
.collect()
}
/// Use MoE for specialized residual processing
pub fn moe_route_residual(&self, residual: &[f32], context: &[f32]) -> Vec<f32> {
// Route to specialized expert based on residual characteristics
self.moe.forward(residual, context)
}
/// Diffusion-based energy propagation
pub fn diffuse_energy(&self, energy: &CoherenceEnergy, steps: usize) -> CoherenceEnergy {
// PDE-based smoothing of energy across graph
self.diffusion.propagate(energy, steps)
}
}Uses ruvector-raft for multi-node sheaf synchronization.
use ruvector_raft::{RaftNode, RaftConfig, LogEntry, ConsensusState};
/// Distributed coherence across multiple nodes
pub struct DistributedCoherence {
/// Raft consensus node
raft: RaftNode,
/// Local sheaf graph
local_graph: SheafGraph,
/// Pending updates to replicate
pending: Vec<GraphUpdate>,
}
impl DistributedCoherence {
/// Propose a graph update to the cluster
pub async fn propose_update(&mut self, update: GraphUpdate) -> Result<(), ConsensusError> {
let entry = LogEntry::new(bincode::serialize(&update)?);
self.raft.propose(entry).await?;
Ok(())
}
/// Apply committed updates from Raft log
pub fn apply_committed(&mut self) {
while let Some(entry) = self.raft.next_committed() {
let update: GraphUpdate = bincode::deserialize(&entry.data).unwrap();
self.local_graph.apply_update(update);
}
}
/// Get global coherence (leader aggregates from all nodes)
pub async fn global_coherence(&self) -> Result<CoherenceEnergy, ConsensusError> {
if self.raft.is_leader() {
// Aggregate from all followers
let energies = self.raft.collect_from_followers(|node| {
node.local_coherence()
}).await?;
Ok(self.aggregate_energies(energies))
} else {
// Forward to leader
self.raft.forward_to_leader(Request::GlobalCoherence).await
}
}
}Hybrid storage with PostgreSQL for authority and ruvector for graph operations.
-- Policy bundles (immutable)
CREATE TABLE policy_bundles (
id UUID PRIMARY KEY,
version VARCHAR(32) NOT NULL,
thresholds JSONB NOT NULL,
escalation_rules JSONB NOT NULL,
signature BYTEA NOT NULL,
approvers UUID[] NOT NULL,
required_approvals INT NOT NULL,
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);
-- Witness records (append-only)
CREATE TABLE witness_records (
id UUID PRIMARY KEY,
action_hash BYTEA NOT NULL,
energy_snapshot JSONB NOT NULL,
decision JSONB NOT NULL,
policy_bundle_id UUID REFERENCES policy_bundles(id),
timestamp TIMESTAMPTZ NOT NULL,
previous_witness UUID REFERENCES witness_records(id),
content_hash BYTEA NOT NULL
);
-- Lineage records (append-only)
CREATE TABLE lineage_records (
id UUID PRIMARY KEY,
entity_ref JSONB NOT NULL,
operation VARCHAR(32) NOT NULL,
dependencies UUID[] NOT NULL,
authorizing_witness UUID REFERENCES witness_records(id),
actor UUID NOT NULL,
timestamp TIMESTAMPTZ NOT NULL
);
-- Event log (deterministic replay)
CREATE TABLE event_log (
sequence_id BIGSERIAL PRIMARY KEY,
event_type VARCHAR(64) NOT NULL,
payload JSONB NOT NULL,
timestamp TIMESTAMPTZ NOT NULL DEFAULT NOW(),
signature BYTEA NOT NULL
);/// Graph substrate using ruvector for vector/graph operations
pub struct RuvectorSubstrate {
/// Node state vectors (HNSW indexed)
node_store: VectorDB,
/// Edge data with restriction maps
edge_store: GraphStore,
/// Cached residuals for incremental computation
residual_cache: ResidualCache,
}
impl RuvectorSubstrate {
/// Find nodes similar to a query state
pub async fn find_similar_nodes(
&self,
query_state: &[f32],
k: usize,
) -> Vec<(NodeId, f32)> {
self.node_store.search(query_state, k).await
}
/// Get subgraph for localized coherence computation
pub async fn get_subgraph(&self, center: NodeId, hops: usize) -> SheafSubgraph {
let node_ids = self.edge_store.bfs(center, hops).await;
let nodes = self.node_store.get_batch(&node_ids).await;
let edges = self.edge_store.edges_within(&node_ids).await;
SheafSubgraph { nodes, edges }
}
}| Application | Description | Coherence Use |
|---|---|---|
| LLM Anti-Hallucination | Guard against confident incorrect outputs | Energy spike → retrieval escalation |
| Financial Regime Detection | Detect market regime changes | Spectral drift → throttle trading |
| Fraud/Compliance Proofs | Auditable decision trails | Witness records for every gate |
| Application | Description | Coherence Use |
|---|---|---|
| Autonomous Robotics | Refuse action on structural mismatch | Energy threshold → motion stop |
| Medical Monitoring | Detect persistent diagnostic disagreement | Spectral analysis → alert |
| Zero-Trust Security | Structural coherence for access | Graph consistency → authorization |
| Application | Description | Coherence Use |
|---|---|---|
| Scientific Theory Pruning | Distributed hypothesis consistency | Global energy minimization |
| Economic Stress Testing | Policy impact simulation | Counterfactual coherence |
| Machine Self-Awareness | System self-model consistency | Reflexive coherence |
| ADR | Decision |
|---|---|
| ADR-CE-001 | Sheaf Laplacian defines coherence witness, not probabilistic confidence |
| ADR-CE-002 | Incremental computation with stored residuals, subgraph summaries, global fingerprints |
| ADR-CE-003 | PostgreSQL + ruvector as unified substrate |
| ADR-CE-004 | Signed event log with deterministic replay |
| ADR-CE-005 | Governance objects are first-class, immutable, addressable |
| ADR-CE-006 | Coherence gate controls explicit compute ladder (Reflex → Retrieval → Heavy → Human) |
| ADR-CE-007 | Thresholds auto-tuned from production traces with governance approval |
| ADR-CE-008 | Multi-tenant isolation at data, policy, and execution boundaries |
- Provable Consistency - Mathematical witnesses replace probabilistic guesses
- Auditable Decisions - Every gate decision has immutable witness record
- Localized Debugging - Edge residuals pinpoint inconsistency sources
- Incremental Efficiency - Only recompute affected subgraphs
- Graceful Degradation - Compute ladder handles varying complexity
- Governance by Design - Policy bundles require multi-party approval
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Restriction map design complexity | High | Medium | Provide learned initialization from data |
| Cold start (no history) | Medium | Low | Bootstrap from domain priors |
| Computational overhead | Medium | Medium | SIMD-optimized residual calculation, incremental updates |
| Threshold tuning difficulty | Medium | Medium | Auto-tune from production traces with governance |
| Graph size scaling | Low | High | Subgraph partitioning, distributed computation |
| Metric | Target | Enabled By |
|---|---|---|
| Single residual calculation | < 1us | SIMD intrinsics |
| Full graph energy (10K nodes) | < 10ms | Parallel computation |
| Incremental update (1 node) | < 100us | Tile-local updates |
| Gate evaluation | < 500us | Neural gate |
| Witness persistence | < 5ms | PostgreSQL |
| Tile tick (256 tiles parallel) | < 1ms | cognitum-gate-kernel |
| SONA instant adaptation | < 0.05ms | Micro-LoRA |
| MinCut update (amortized) | n^o(1) | Subpolynomial algorithm |
| HDC witness encoding | < 10us | Hypervector ops |
| Hyperbolic distance | < 500ns | Poincaré SIMD |
| Attention-weighted energy | < 5ms | Flash attention |
| Distributed consensus | < 50ms | Raft protocol |
- Core sheaf graph data structures
- Residual calculation with SIMD optimization
- Basic energy aggregation
- In-memory storage backend
- Policy bundle schema and validation
- Witness record creation and persistence
- Lineage tracking for writes
- PostgreSQL storage integration
- Compute ladder implementation
- Threshold-based gating logic
- Persistence detection
- Escalation pathways
- Incremental coherence computation
- Spectral analysis for drift detection
- Auto-tuning from traces
- Multi-tenant isolation
| Feature | Default | Description |
|---|---|---|
default |
Yes | Core coherence with tiles, SONA, nervous-system |
full |
No | All integrations enabled |
tiles |
Yes | cognitum-gate-kernel 256-tile fabric |
sona |
Yes | Self-optimizing threshold tuning |
learned-rho |
Yes | GNN-learned restriction maps |
hyperbolic |
Yes | Hierarchy-aware Poincaré energy |
mincut |
Yes | Subpolynomial graph partitioning |
neural-gate |
Yes | Nervous-system CoherenceGatedSystem |
attention |
No | Attention-weighted residuals (MoE, PDE) |
distributed |
No | Raft-based multi-node coherence |
postgres |
No | PostgreSQL governance storage |
simd |
Yes | SIMD-optimized residual calculation |
spectral |
No | Eigenvalue-based drift detection |
wasm |
No | WASM bindings for browser/edge |
| Crate | Version | Purpose |
|---|---|---|
cognitum-gate-kernel |
workspace | 256-tile WASM coherence fabric |
sona |
workspace | Self-optimizing thresholds with EWC++ |
ruvector-gnn |
workspace | Learned restriction maps, replay buffers |
ruvector-mincut |
workspace | Subpolynomial n^o(1) graph partitioning |
ruvector-hyperbolic-hnsw |
workspace | Hierarchy-aware Poincaré energy |
ruvector-nervous-system |
workspace | CoherenceGatedSystem, HDC witnesses |
ruvector-attention |
workspace | Topology-gated attention, MoE |
ruvector-raft |
workspace | Distributed consensus |
ruvector-core |
workspace | Vector storage and HNSW search |
ruvector-graph |
workspace | Graph data structures |
| Dependency | Purpose |
|---|---|
ndarray |
Matrix operations for restriction maps |
rayon |
Parallel residual computation |
blake3 |
Content hashing for witnesses |
bincode |
Binary serialization |
tokio |
Async runtime for distributed coherence |
| Dependency | Feature | Purpose |
|---|---|---|
sqlx |
postgres | PostgreSQL async client |
nalgebra |
spectral | Eigenvalue computation |
serde_json |
- | JSON serialization for governance |
wasm-bindgen |
wasm | WASM bindings for browser deployment |
-
Hansen, J., & Ghrist, R. (2019). "Toward a spectral theory of cellular sheaves." Journal of Applied and Computational Topology.
-
Curry, J. (2014). "Sheaves, Cosheaves and Applications." arXiv:1303.3255.
-
Robinson, M. (2014). "Topological Signal Processing." Springer.
-
RuVector Team. "ruvector-core Architecture." ADR-001.
-
Original Gist: "Coherence Engine Vision." https://gist.github.com/ruvnet/e511e4d7015996d11ab1a1ac6d5876c0
- ADR-001: Ruvector Core Architecture
- ADR-003: SIMD Optimization Strategy
- ADR-006: Memory Management
- ADR-007: Security Review & Technical Debt