| # 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 |