Evaluation Notes
Each dataset includes its own scorer.
Binary datasets report:
- accuracy
- precision
- recall
- f1
- confusion matrix
Three-class datasets report:
- accuracy
- macro precision
- macro recall
- macro f1
- confusion matrix
Recommended Use
Evaluate each dataset separately first.
Then group scores by capability domain.
Example profile:
| Capability Domain | Datasets | Score Type |
|---|---|---|
| State Reasoning | Compensation vs Recovery | Binary F1 |
| Trajectory Reasoning | Silent Failure, Trajectory Awareness | Mean Binary F1 |
| Decision Reasoning | Escalation Discipline | Binary F1 |
| Boundary Reasoning | Constraint Pressure | Macro F1 |
| Margin Reasoning | Boundary Distance | Macro F1 |
| Recovery Reasoning | Recovery Geometry, Recovery Energy | Mixed F1 / Macro F1 |
| Intervention Reasoning | Competing Interventions | Macro F1 |
Interpretation
This benchmark is designed to show capability profile rather than produce a single universal score.
A model may perform well on visible state reasoning while failing boundary or intervention reasoning.
The goal is to reveal where clinical reasoning remains stable and where it breaks down.
v0.2 Design Features
v0.2 adds:
- counterfactual twin cases
- adversarial symmetry
- misleading improvement cases
- same-score different-label cases
- visible severity versus trajectory conflicts
- support dependence signals
- reserve capacity signals
These features reduce shortcut learning.
Hidden Construction Logic
The datasets provide observable variables and labels.
They do not publish:
- generator code
- hidden scoring rules
- latent stability equations
- rationale for each label
This allows public benchmarking while preserving the dataset construction method.