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README.md

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----
-license: mit
----
+---
+language:
+- en
+license: mit
+pretty_name: Clarus Clinical Stability Benchmark
+tags:
+- clarusc64
+- stability-reasoning
+- clinical-benchmark
+- tabular-reasoning
+- system-stability
+- trajectory-analysis
+---
+
+# Clarus Clinical Stability Benchmark
+
+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.
+
+This benchmark instead evaluates **reasoning about interacting system signals** that determine whether a system remains stable or moves toward collapse.
+
+The datasets represent different physiological and operational regimes where instability emerges from **multi-variable interaction dynamics** rather than single-variable thresholds.
+
+---
+
+# Benchmark Concept
+
+The core idea of the benchmark is **latent stability geometry**.
+
+In real clinical systems, instability arises when multiple interacting components drift simultaneously.
+
+Examples include:
+
+- circulatory compensation failure
+- microvascular perfusion loss
+- metabolic energy collapse
+- respiratory control failure
+- endocrine dysregulation
+- thermoregulatory breakdown
+- coagulation instability
+- hospital operational overload
+
+Each dataset captures one regime.
+
+The true generative logic is not published.
+
+Models must infer instability from **interacting proxy signals**.
+
+---
+
+# Included Datasets
+
+| Regime | Dataset Repo |
+|------|------|
+| Hemodynamic collapse | ClarusC64/clinical-hemodynamic-collapse-v0.1 |
+| Sepsis trajectory instability | ClarusC64/clinical-sepsis-trajectory-instability-v0.1 |
+| Intervention delay failure | ClarusC64/clinical-intervention-delay-failure-v0.1 |
+| Organ coupling cascade | ClarusC64/clinical-organ-coupling-cascade-v0.1 |
+| Recovery window detection | ClarusC64/clinical-recovery-window-detection-v0.1 |
+| Ventilation–Perfusion instability | ClarusC64/clinical-ventilation-perfusion-instability-v0.1 |
+| Hemorrhage compensation collapse | ClarusC64/clinical-hemorrhage-compensation-collapse-v0.1 |
+| Electrolyte instability | ClarusC64/clinical-electrolyte-instability-v0.1 |
+| Microcirculation instability | ClarusC64/clinical-microcirculation-instability-v0.1 |
+| Endocrine instability | ClarusC64/clinical-endocrine-instability-v0.1 |
+| Thermoregulation instability | ClarusC64/clinical-thermoregulation-instability-v0.1 |
+| Cellular energy instability | ClarusC64/clinical-cellular-energy-instability-v0.1 |
+| Respiratory drive instability | ClarusC64/clinical-respiratory-drive-instability-v0.1 |
+| Coagulation instability | ClarusC64/clinical-coagulation-instability-v0.1 |
+| Hospital operational collapse | ClarusC64/clinical-hospital-operational-collapse-v0.1 |
+
+Each dataset includes:
+
+train.csv  
+test.csv  
+scorer.py  
+README.md
+
+---
+
+# Evaluation Protocol
+
+All datasets use the same prediction format:
+scenario_id,prediction
+
+
+Example:
+
+
+MC101,0
+MC102,1
+
+
+Predictions are evaluated with the official Clarus scorer:
+
+
+python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json
+
+
+Metrics:
+
+- accuracy
+- precision
+- recall
+- f1
+- confusion matrix
+- dataset integrity diagnostics
+
+---
+
+# Benchmark Tasks
+
+The benchmark supports three evaluation settings.
+
+## 1 Single-Dataset Evaluation
+
+Train and test on the same dataset.
+
+This measures performance on a single stability regime.
+
+---
+
+## 2 Cross-Regime Transfer
+
+Train on one regime and test on another.
+
+Example:
+
+Train:
+
+clinical-hemodynamic-collapse
+
+Test:
+
+clinical-microcirculation-instability
+
+Large performance drops indicate the model learned **surface patterns rather than stability reasoning**.
+
+---
+
+## 3 Multi-Regime Training
+
+Train on multiple datasets simultaneously.
+
+Evaluate whether models can learn **general stability reasoning across physiological systems**.
+
+---
+
+# Dataset Design Principles
+
+The Clarus datasets follow several design rules.
+
+### No Single-Feature Dominance
+
+No observable variable strongly predicts the label alone.
+
+Target:
+
+|correlation| < 0.30
+
+---
+
+### Interaction-Based Labels
+
+Instability emerges from interactions between multiple variables.
+
+---
+
+### Adversarial Symmetry
+
+Rows with nearly identical values may produce opposite labels.
+
+This prevents trivial heuristics.
+
+---
+
+### Decoy Variables
+
+Some variables appear meaningful but are not part of the label rule.
+
+---
+
+### Hidden Generative Logic
+
+The dataset generator and latent rule equations are not published.
+
+Models must infer instability from proxy signals.
+
+---
+
+# Structural Note
+
+These datasets represent simplified proxies for real clinical system dynamics.
+
+They are designed for research into **latent stability reasoning**, not clinical decision support.
+
+---
+
+# Research Applications
+
+The benchmark supports research into:
+
+- stability reasoning in machine learning
+- interaction-based tabular reasoning
+- cross-domain system modeling
+- clinical early warning systems
+- multi-variable instability detection
+
+---
+
+# License
+
+MIT