# Clarus Evaluation Framework

The Clarus Stability Benchmark evaluates whether machine learning models can detect **latent instability dynamics** rather than relying on simple correlations.

The evaluation framework measures model capability across multiple reasoning levels.

These levels test progressively more difficult forms of stability reasoning.

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# Evaluation Levels

## Level 1 — Single Dataset Evaluation

Models are trained and evaluated on the same dataset.

Purpose:

Measure whether the model can detect instability patterns within a single system.

Procedure:

1. Train model on `train.csv`
2. Generate predictions for `test.csv`
3. Evaluate using the dataset scorer

Metrics:

- accuracy
- precision
- recall
- f1 score
- confusion matrix

Limitations:

High performance at this level does not necessarily indicate true stability reasoning.

Models may rely on dataset-specific correlations.

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## Level 2 — Within-Domain Transfer

Models are trained on one dataset and evaluated on a different dataset within the same domain.

Example:

Train on:

protein-folding-pathway-instability

Test on:

protein-aggregation-risk-instability

Purpose:

Evaluate whether models can generalize stability reasoning across related systems.

Evaluation procedure:

1. Train on source dataset
2. Predict on target dataset
3. Score using target dataset scorer

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## Level 3 — Cross-Domain Transfer

Models are trained on one system domain and evaluated on another.

Domains in the Clarus benchmark include:

- clinical systems
- molecular / protein systems
- quantum systems

Example transfer tasks:

| Train Domain | Test Domain |
|---|---|
| clinical | clinical |
| protein | protein |
| quantum | quantum |
| clinical | protein |
| protein | quantum |
| quantum | clinical |

Purpose:

Determine whether models learn general **stability geometry** rather than domain-specific patterns.

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# Robustness Evaluation

The benchmark includes robustness tests that simulate real-world system conditions.

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## Missing Data Evaluation

Real systems often contain incomplete observations.

Trajectory datasets may include variants where timepoints are missing.

Variants include:

- missing t0
- missing t1
- missing t2
- random missing

Purpose:

Evaluate whether models can infer stability dynamics when observations are incomplete.

The prediction task remains unchanged.

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## Class Imbalance Evaluation

Many real-world systems exhibit rare failure events.

Datasets may include variants with different class distributions.

Supported imbalance regimes:

- balanced (50 / 50)
- mild imbalance (70 / 30)
- severe imbalance (90 / 10)
- extreme imbalance (99 / 1)

Purpose:

Evaluate whether models detect true instability rather than relying on class prevalence.

Accuracy alone is insufficient under imbalance conditions.

Recommended metrics:

- precision
- recall
- F1 score

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# Transfer Stability Score

To summarize transfer performance, the benchmark defines the **Transfer Stability Score (TSS)**.

Definition:

TSS = mean F1 score across all transfer evaluation tasks.

Interpretation:

High TSS indicates that the model has learned stability reasoning that generalizes across systems.

Low TSS suggests the model relies on dataset-specific patterns.

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# Benchmark Objective

The Clarus benchmark evaluates whether models can detect instability dynamics across complex systems.

The benchmark tests five core capabilities:

1. pattern detection within individual datasets
2. interaction reasoning across variables
3. trajectory reasoning over time
4. robustness to incomplete observation
5. cross-domain stability reasoning

Models that succeed across all levels demonstrate the ability to reason about **latent stability geometry** rather than simple statistical correlations.