# Stability Manifold View

The Clarus Clinical Stability Benchmark can be read as a set of local views into a shared stability manifold.

Each dataset exposes a different clinical regime.

The observable variables change across regimes, but the benchmark asks whether models can detect the same deeper question:

Is the system moving toward stability or instability?

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# Core Idea

A model should not only learn:

- lactate patterns
- glucose patterns
- respiratory patterns
- staffing patterns

It should learn the broader structure:

- pressure rising
- buffer weakening
- coupling increasing
- response delay widening
- recovery margin closing

This is the hidden stability geometry the benchmark probes.

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# Regime Map

| Stability Axis | Example Dataset |
|---|---|
| Pressure load | clinical-hemodynamic-collapse-v0.1 |
| Buffer exhaustion | clinical-fluid-balance-instability-v0.1 |
| Coupling cascade | clinical-organ-coupling-cascade-v0.1 |
| Delayed response | clinical-intervention-delay-failure-v0.1 |
| Recovery window | clinical-recovery-window-detection-v0.1 |
| Compensation failure | clinical-hemorrhage-compensation-collapse-v0.1 |
| Control-loop failure | clinical-autonomic-instability-v0.1 |
| Cellular energy failure | clinical-cellular-energy-instability-v0.1 |
| Operational collapse | clinical-hospital-operational-collapse-v0.1 |

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# Why This Matters

Most models can fit one dataset.

The harder question is whether they can recognize instability across regimes.

A strong model should detect similar stability geometry even when the surface variables change.

That is the purpose of the cross-regime transfer tests.

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# Benchmark Claim

The Clarus benchmark evaluates whether models can move from local pattern recognition toward general stability reasoning.