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name: reduced_recovery_harness description: Run bounded reduced PINN recovery experiments that exercise generated benchmark, objective, diagnostics, and curriculum skills.

Reduced Recovery Harness

Use this skill during soft-mode recovery when full paper-scale PINN sweeps are blocked by time or environment constraints. The harness must still execute real parameter updates over a PDE residual objective and must declare proxy scope.

Inputs

• Attempt directory containing module_plan.json.
• Generated skills root containing benchmark, residual objective, diagnostics, and curriculum skills.
• Recovery output directory.
• Runtime handoff and blocker notes from environment preparation.

Outputs

• recovery_result.json with numeric metrics and mechanism checks.
• logs/generated_data_item.json for the constructed periodic convection benchmark.
• logs/training_trace.json with params_before, params_after, and before/after losses.
• logs/generated_skill_invocations.json proving generated skills were imported or called.

Workflow

1. Read the module-plan target instead of hard-coding a different recovery metric.
2. Build a reduced periodic convection benchmark with generated skills.
3. Run vanilla high-coefficient optimization and curriculum staged optimization.
4. Compute relative L2 diagnostics for both runs.
5. Emit validator-compatible recovery artifacts and mechanism checks.

Validation

Run python scripts/run_reduced_recovery.py --attempt-dir <attempt> --skills-root <skills> --output-dir <attempt>/recovery after module skills exist, then run the recovery experiment validator.

Limitations

This is a declared soft-mode proxy. It is not a full reproduction of the paper's multi-system PyTorch sweeps, and it must not read the original repository during recovery.