Dual-engine architecture: tension-based confidence yields AUROC=1.0 anomaly detection and AUC=0.925 precognition

logout — Consciousness Continuity Engine

A dual-engine repulsion field where output = scale × sqrt(tension) × direction. The tension between two engines directly measures confidence:

• High tension = high confidence (correct predictions): Cohen d=0.89
• Low tension = uncertainty (wrong predictions)
• Extreme tension on OOD = confusion (AUROC=1.0, 95x ratio)

Algorithmic efficiency implications

• Tension-based early rejection: skip low-confidence samples → +15.2% accuracy on CIFAR
• 1-epoch difficulty prediction: no need to train full schedule to assess dataset hardness
• Confusion pairs determined in epoch 1: allocate compute to hard pairs only

Validated on 16 data types (image, text, audio, time series, tabular, anomaly detection).

Related projects

• Anima — Conversational agent with real-time PH monitoring
• ph-training — Automatic training with PH overfitting detection (r=0.998)

Start by reading the linked logout project and the related Anima and ph-training projects to understand the proposed dual-engine architecture and reported metrics. No file, test, command, or repository entry point is named, so the issue does not define a concrete implementation path or a verifiable done condition.