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ab680a9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # 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 |