# 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They do not publish: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.