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README.md

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 # Clarus Clinical Stability Benchmark
 
+The Clarus Clinical Stability Benchmark evaluates whether machine learning models can infer latent system instability from interacting proxy signals across multiple physiological and operational regimes.
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+Unlike traditional tabular benchmarks that reward threshold detection or single-feature correlations, Clarus datasets are designed so that instability emerges from interactions between variables.
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+The benchmark therefore measures stability reasoning rather than pattern memorization.
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 The Clarus Clinical Stability Benchmark evaluates whether machine learning models can detect **latent instability in complex clinical systems**.
 
 Most tabular benchmarks test pattern recognition from static variables.