File size: 1,802 Bytes
b7a7004 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # 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. |