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name: flow_matching_recovery_harness description: Run a bounded soft-mode Flow Matching proxy recovery that invokes generated OT-path, CFM-loss, and CNF-ODE skills.

Flow Matching Recovery Harness

Use this skill when validating whether generated Flow Matching skills can execute the paper's core mechanism under bounded soft-mode recovery. The harness must not read the original source repository and must clearly label proxy evidence.

Inputs

• Attempt directory with module_plan.json and environment/runtime_handoff.json.
• Generated skill root containing conditional_ot_paths, conditional_flow_matching_loss, and cnf_ode_sampling.
• Deterministic synthetic noise/data pairs for a small OT-CFM proxy.

Outputs

• recovery/recovery_result.json.
• recovery/logs/training_trace.json.
• recovery/logs/generated_data_item.json.
• recovery/logs/generated_skill_invocations.json.
• recovery/source_manifest.json.

Workflow

1. Read the module-plan target and runtime handoff.
2. Build deterministic paired vectors that define an analytic OT displacement problem.
3. Import and call the generated OT-path, CFM-loss, and ODE-sampling scripts.
4. Run a scalar proxy optimizer update against the CFM loss and record before/after values.
5. Integrate the learned constant vector field and compare to the analytic OT endpoint.
6. Write validator-compatible recovery artifacts with mechanism checks and command provenance.

Validation

Run:

python tests/test_proxy_recovery.py
python scripts/run_proxy_recovery.py --attempt-dir /path/to/attempt --skill-root /path/to/generated_skills

Limitations

This harness is a declared reduced/proxy recovery. It validates the paper mechanism on deterministic vectors and must not be reported as full ImageNet/CIFAR-scale reproduction.