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name: lpips_recovery_harness description: Run a bounded source-safe LPIPS-style recovery experiment and emit validation-compatible recovery artifacts.

LPIPS Recovery Harness

Use this skill after the LPIPS distance, BAPPS 2AFC protocol, and linear calibration skills exist and validate. It coordinates a soft-mode reduced recovery without reading the original source repository.

Inputs

• Attempt directory.
• Generated skills root.
• module_plan.json with the fast recovery target.
• environment/runtime_handoff.json.

Outputs

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

Workflow

1. Read the module plan and runtime handoff.
2. Declare the strongest feasible target: full BAPPS/pretrained LPIPS if runtime is ready, otherwise soft-mode synthetic BAPPS-style proxy.
3. Construct deterministic reference and distorted patches.
4. Invoke generated LPIPS distance logic to compute per-layer contributions.
5. Invoke the calibration skill for a bounded update.
6. Invoke the 2AFC protocol skill to score calibrated distances.
7. Write mechanism checks, command logs, source manifest, and recovery result.
8. Run the Distiller recovery experiment validator.

Validation

Run this skill from the attempt directory with:

python scripts/run_lpips_recovery.py --attempt-dir <attempt_dir> --skills-root <generated_skills_root>

Limitations

The bundled harness creates a reduced proxy dataset unless the caller extends it with real BAPPS/pretrained assets. It must not be reported as full Table 5 reproduction.