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benchmark_scope.md

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+# Benchmark Scope
+
+The Clarus Stability Benchmark evaluates whether machine learning systems can detect **latent instability dynamics** across complex systems.
+
+Most tabular benchmarks measure a model’s ability to detect statistical correlations in datasets.
+
+The Clarus benchmark instead focuses on **stability reasoning** — the ability to detect when interacting system variables are approaching instability.
+
+The benchmark is built around the idea that many complex systems share common instability mechanisms even when their surface variables differ.
+
+Examples include:
+
+- clinical physiological systems
+- molecular and protein systems
+- quantum computing systems
+
+In each domain, instability arises when interacting pressures exceed the system’s capacity to maintain stability.
+
+The datasets in this benchmark expose only **observable proxy variables**.
+
+The latent stability rules and generators used to produce the datasets are not included.
+
+This design ensures that models must infer instability from interactions between variables rather than from explicit rules.
+
+---
+
+# System Domains
+
+The benchmark currently spans three system scales.
+
+## Clinical Systems
+
+Datasets describing physiological instability.
+
+Examples include:
+
+- circulation and perfusion collapse
+- respiratory control instability
+- renal filtration failure
+- endocrine feedback instability
+- metabolic supply-demand imbalance
+
+These datasets simulate clinical monitoring conditions where multiple physiological signals interact over time.
+
+---
+
+## Molecular Systems
+
+Datasets describing molecular stability and protein behavior.
+
+Examples include:
+
+- protein folding pathway instability
+- mutation-driven structural destabilization
+- aggregation risk
+- chaperone rescue window failure
+- protein interface collapse
+- conformational switching instability
+
+These datasets test whether models can detect instability in molecular interaction networks.
+
+---
+
+## Quantum Systems
+
+Datasets describing instability in quantum computing devices.
+
+Examples include:
+
+- coherence collapse
+- gate sequence instability
+- entanglement decay
+- error correction failure
+- control pulse instability
+
+These datasets represent simplified stability conditions in noisy intermediate-scale quantum (NISQ) devices.
+
+---
+
+# Benchmark Design Principles
+
+The benchmark follows several design constraints.
+
+### No Single-Feature Dominance
+
+Labels cannot be predicted using a single variable.
+
+Instability emerges from **interactions between variables**.
+
+### Hidden Stability Geometry
+
+Datasets expose only observable proxies.
+
+The latent stability rules used to generate labels are not published.
+
+### Adversarial Symmetry
+
+Datasets include examples with very similar values but different outcomes.
+
+This prevents models from relying on simple thresholds.
+
+### Mixed Instability Mechanisms
+
+Datasets include multiple instability regimes within the same domain.
+
+---
+
+# Evaluation Philosophy
+
+The benchmark evaluates models across several reasoning levels.
+
+1. single-dataset prediction
+2. within-domain transfer
+3. cross-domain transfer
+4. missing observation robustness
+5. class imbalance robustness
+
+These evaluation tasks test whether models learn **general instability reasoning** rather than dataset-specific patterns.
+
+---
+
+# Intended Use
+
+The Clarus Stability Benchmark is designed for research into:
+
+- machine learning reasoning over complex systems
+- stability detection in noisy environments
+- cross-domain generalization
+- robustness to incomplete observations
+
+The benchmark is not a simulator for clinical, molecular, or quantum systems.
+
+Instead it provides compact tabular datasets that express stability dynamics through observable proxies.