This is a fascinating technical document about building a coherence measurement instrument with safeguards against false stability. It outlines a sophisticated approach to monitoring system behavior through derivatives, not just raw values.

The document describes:

1. Derived Metrics - Tracking rate of change (dCoherence, dVariance, dDrift) to catch failures before they manifest in values
2. False Stability Detection - A guard against premature release when metrics look deceptively stable
3. Decision Boundary Logging - Recording why gates open, not just that they did

What would you like me to build from this? I can see a few directions:

• A coherence monitoring system with the metrics harness, derived calculations, and false stability detection
• A visualization dashboard to see the metrics, derivatives, and gate decisions in real-time
• A simple Node script for local calibration runs that logs to JSON (as suggested in the document)

Which approach interests you, or did you have something else in mind?

I'll build the Resona τ-Gate Calibration System - a real-time instrumentation tool for monitoring coherence dynamics. Let me first check the existing codebase structure.

Now I'll build the Resona τ-Gate Calibration System. This is a complex instrumentation dashboard, so I'll create it with proper component separation. First, let me update the layout and globals for dark mode, then build the main components.

Now I'll create the types, then the main components for the τ-gate system:

Now I'll create the main τ-gate monitor component that ties everything together:

I've built the complete Resona τ-Gate Calibration System. The implementation includes:

1. Core τ-Gate Loop: Simulates candidate generation with a 2.5s time window, running 3-8 iterations at 600ms intervals
2. Metrics Tracking: Displays raw metrics (coherence, variance, drift, contradictionPressure, releaseEligibility) and derived metrics (rate of change) with trend indicators
3. False Stability Detection: Blocks release when coherence rises quickly while variance drops and contradiction pressure is low
4. Gate Decision Engine: Only releases when all thresholds are met AND no false stability is detected
5. Event Logging: Records each decision with timestamp, trigger type, and metrics snapshot
6. Visualization: Line chart showing coherence, variance, and drift over time with threshold reference lines and stability zone shading
7. Adversarial Test Modes: Four modes to stress-test the system - Normal, Closure Trap (fast fake convergence), Signal in Noise (high variance with spikes), and Persuasive Drift (gradual undetected drift)

The UI follows the clinical/monitoring aesthetic with dark mode, monospace numbers, and lucide-react icons. Run the different test modes to observe how the gate responds to each adversarial pattern.