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name: msfm_mechanism_evaluation description: Evaluate whether an MSFM recovery result exercised BatchOT coupling, Joint CFM loss, optimizer evidence, and soft-mode proxy constraints.

MSFM Mechanism Evaluation

Use this skill after a Multisample Flow Matching recovery experiment has produced recovery_result.json, training_trace.json, and generated skill invocation logs.

Do not accept a proxy run only because its metric is positive; this skill checks whether the recovery exercised the paper mechanisms and respected source/runtime boundaries.

Inputs

• Recovery result JSON.
• Training trace JSON.
• Generated skill invocation log.
• Optional minimum transport-cost reduction threshold.

Outputs

• Mechanism pass/fail report.
• Metric gap summary.
• Refinement hints assigned to module-level causes.

Workflow

1. Confirm proxy status is declared when full recovery is not claimed.
2. Verify BatchOT and uniform couplings were evaluated and double-stochastic evidence passed.
3. Verify Joint CFM loss and an optimizer step were executed.
4. Verify reduced/full booleans are honest and no original repository was read.
5. Confirm generated skill invocations cover coupling, loss, recovery, and evaluation modules.
6. Return actionable hints for missing mechanism evidence.

Validation

Run:

python scripts/evaluate_mechanism.py --self-test
python tests/test_evaluate_mechanism.py

Limitations

• This skill evaluates reduced/proxy mechanism evidence; it does not compare full ImageNet FID.