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name: recovery_evaluation_harness description: Run bounded executable OT-CFM proxy recoveries with command logs, mechanism checks, and validator-compatible artifacts.

Recovery Evaluation Harness

Use this skill when recovering this paper under bounded runtime constraints. It is responsible for producing executable evidence, not for hand-writing final metrics. Do not use it to claim full CIFAR or single-cell reproduction unless the corresponding real runtime and data actually ran.

Inputs

• Attempt directory containing module_plan.json and generated skills.
• Runtime handoff describing available Python packages, GPU state, and allowed recovery sources.
• Generated CFM objective and minibatch OT coupling skills.

Outputs

• recovery/recovery_result.json with numeric metrics and target metadata copied from the module plan.
• recovery/logs/training_trace.json with loss_before, loss_after, params_before, and params_after.
• recovery/logs/generated_data_item.json, generated-skill invocation log, source manifest, and command log.

Workflow

1. Select the strongest feasible target from the runtime handoff. Under soft mode, declare a reduced proxy only after full training is blocked.
2. Build deterministic source and target minibatches.
3. Invoke the generated minibatch OT skill to compute a coupling and the CFM objective skill to construct velocity targets.
4. Run a real optimizer update on a tiny trainable linear vector-field parameter.
5. Save validator-compatible artifacts and mechanism checks.
6. Run the recovery experiment validator and refine any missing evidence.

Validation

Run the recovery script on a temporary attempt directory, then run the Distiller recovery validator. The skill itself can be validated with python tests/test_recovery_metrics.py.

Limitations

The bundled harness is a reduced/proxy experiment. It validates the CFM/OT mechanism but does not reproduce full-scale neural ODE image generation or single-cell benchmarks.