# Robustness Evaluation Suite

The Clarus benchmark evaluates model robustness across three dimensions.

## 1 Stability Reasoning

Core datasets evaluate whether models detect latent instability mechanisms.

Examples:

- perfusion instability
- renal filtration instability
- respiratory drive instability
- endocrine feedback instability

## 2 Missing Observation Robustness

Missing data variants evaluate reasoning under incomplete trajectories.

Variants include:

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

## 3 Prevalence Robustness

Imbalance datasets evaluate robustness to instability prevalence shifts.

Variants include:

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

## Benchmark Objective

Models that truly learn stability geometry should remain robust across:

- missing observations
- prevalence shifts
- cross-domain transfer