| # Stability Manifold View |
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| The Clarus Clinical Stability Benchmark can be read as a set of local views into a shared stability manifold. |
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| Each dataset exposes a different clinical regime. |
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| The observable variables change across regimes, but the benchmark asks whether models can detect the same deeper question: |
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| Is the system moving toward stability or instability? |
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| # Core Idea |
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| A model should not only learn: |
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| - lactate patterns |
| - glucose patterns |
| - respiratory patterns |
| - staffing patterns |
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| It should learn the broader structure: |
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| - pressure rising |
| - buffer weakening |
| - coupling increasing |
| - response delay widening |
| - recovery margin closing |
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| This is the hidden stability geometry the benchmark probes. |
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| # Regime Map |
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| | Stability Axis | Example Dataset | |
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| | 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 |
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| Most models can fit one dataset. |
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| The harder question is whether they can recognize instability across regimes. |
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| A strong model should detect similar stability geometry even when the surface variables change. |
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| That is the purpose of the cross-regime transfer tests. |
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| # Benchmark Claim |
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| The Clarus benchmark evaluates whether models can move from local pattern recognition toward general stability reasoning. |
