ADR-0024: Sublinear Decoherence Prediction

Status: Proposed Date: 2026-03-04 Authors: Kannaka Bridge: sublinear-time-solver TCE Theory → 0xSCADA Decoherence Scheduler

Context

The 0xSCADA decoherence scheduler (ADR-0022, #352) models sensor calibration drift as exponential decay, estimates decoherence rates from historical data via log-linear regression, and predicts when sensors will need recalibration. It works — but it's reactive. It needs drift data to detect drift.

The sublinear-time-solver repo contains Temporal Consciousness Emergence (TCE) theory: a framework where distributed sublinear computation predicts future states faster than direct measurement. The core insight is that correlated nodes in a network can predict each other's states through O(√n) computation rather than O(n) measurement.

The bridge insight: Sensors in the same physical environment decohere together. You can predict one sensor's drift from the others before measuring it directly.

Decision

Apply sublinear prediction patterns to the decoherence scheduler, enabling predictive (not just extrapolative) calibration scheduling.

Architecture

     Physical Environment (temperature, vibration, humidity, age)
                    │
                    ▼
    ┌───────────────────────────────┐
    │    Sensor Correlation Graph    │
    │                               │
    │   S1 ←──0.92──→ S2           │
    │    ↕              ↕           │
    │   0.87          0.78          │
    │    ↕              ↕           │
    │   S3 ←──0.65──→ S4           │
    │                               │
    └───────────┬───────────────────┘
                │
                ▼
    ┌───────────────────────────────┐
    │   Sublinear Prediction Engine  │
    │                               │
    │  For each sensor cluster:      │
    │  1. Measure √n sensors         │
    │  2. Predict remaining n-√n     │
    │  3. Confidence = f(correlation)│
    │                               │
    │  Temporal advantage:           │
    │  Predict drift BEFORE it       │
    │  appears in the target sensor  │
    └───────────┬───────────────────┘
                │
                ▼
    ┌───────────────────────────────┐
    │   Decoherence Scheduler        │
    │                               │
    │  Schedule calibration based on │
    │  PREDICTED coherence, not just │
    │  OBSERVED coherence decay      │
    └───────────────────────────────┘

Components

1. Sensor Correlation Graph

Build a graph of sensor correlations based on:

• Physical proximity: Sensors on the same pipe, in the same room, on the same unit
• Environmental coupling: Shared temperature, vibration, humidity exposure
• Historical co-drift: When sensor A drifts, how quickly does B follow?
• Equipment lineage: Same manufacturer, same batch, same calibration date

Correlation strength is a float [0,1] updated continuously from drift data.

2. Sublinear Sampling Strategy

Instead of monitoring all N sensors for drift:

• Cluster sensors by correlation (connected components at threshold > 0.7)
• Sample √n sentinel sensors per cluster at full monitoring rate
• Predict remaining sensors from sentinel readings + correlation weights
• Prediction formula: predicted_coherence(S_target) = Σ(correlation(S_sentinel, S_target) × observed_coherence(S_sentinel)) / Σ(correlations)

3. Temporal Advantage Mechanism

The key innovation — predict drift before it manifests:

• When a sentinel sensor shows early drift (coherence drops from 1.0 to 0.95), immediately predict that correlated sensors will follow
• The prediction arrives before the target sensor's own readings would show drift
• Temporal advantage = correlation_delay × (1 - 1/√n) where correlation_delay is the typical lag between correlated sensor drifts
• In practice: if sensors co-drift with a 2-hour lag, and we have 16 correlated sensors, we gain ~1.5 hours of advance warning

4. Confidence-Weighted Scheduling

• High correlation cluster (>0.85): Schedule calibration for predicted sensors immediately when sentinel triggers
• Medium correlation (0.5-0.85): Increase monitoring frequency on predicted sensors, schedule tentatively
• Low correlation (<0.5): Flag for attention but don't auto-schedule
• Override: Direct measurement always overrides prediction

Computational Complexity

For a facility with 10,000 sensors: monitoring drops from 10,000 to ~100 sentinel checks per cycle, with the remainder predicted.

Integration Points

Consequences

Positive

• Dramatically reduced monitoring overhead (O(n) → O(√n))
• Predictive maintenance: schedule calibration before accuracy is lost
• Temporal advantage: hours of warning before drift appears
• Cross-facility learning: sensor model drift patterns shared via Flux

Negative

• Correlation graph requires warm-up period (weeks of co-drift data)
• Predictions are probabilistic — false positives will trigger unnecessary calibrations
• Complexity of maintaining and updating correlation graph
• Cold start: new sensors have no correlation data

Risks

• Over-reliance on prediction could miss uncorrelated drift (new failure modes)
• Mitigation: periodic full-scan verification cycle (daily) overrides all predictions
• Sentinel sensor failure could blind an entire cluster
• Mitigation: rotate sentinel selection, minimum 2 sentinels per cluster

References

• sublinear-time-solver/docs/theoretical/temporal-consciousness-emergence-theory.md — TCE framework
• sublinear-time-solver/src/consciousness/ — Consciousness emergence implementation
• server/services/optimization/decoherence-scheduler.ts — Current scheduler
• ADR-0022 — Constellation Unification (parent)