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name: reduced_recovery_evaluation description: Packages reduced recovery evidence, source-boundary checks, mechanism checks, metrics, and validation-ready logs.

Reduced Recovery Evaluation

Use this skill when a recovery or analysis task needs the PyHessian paper component described by this module. Do not use it to read or depend on the original PyHessian repository during recovery; the scripts are self-contained reduced implementations or contract checkers.

Inputs

• Experiment command log
• Generated skill invocation records
• Curvature metrics and training trace
• Runtime handoff and source manifest

Outputs

• Validator-compatible recovery_result.json fields
• Analysis-ready gap and acceptance summary

Workflow

1. Confirm that the requested experiment matches this module contract.
2. Use the script in scripts/recovery_contract.py for deterministic checks or as reference logic.
3. Preserve the paper mechanism: Soft-mode recovery can be valid only when it declares the proxy, records executable evidence, and demonstrates the paper mechanism numerically.
4. Write numeric evidence and avoid qualitative-only conclusions.
5. In recovery, record whether this skill was called, imported, or cross-checked.

Validation

Run python through the Distiller skill-tree validator with --run-tests, or run the test file in tests/ with the repository-independent Python path pointing at this skill's scripts/ directory.

Limitations

This generated skill captures reusable mechanism semantics. It is not a drop-in replacement for the original PyTorch package and does not authorize recovery to read the original repository.