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name: reduced_cic_recovery_harness description: Run a bounded soft-mode CIC proxy experiment that invokes generated batching, contrastive loss, and entropy reward skills.

Reduced CIC Recovery Harness

Use this skill when full URLB CIC pretraining is blocked or too expensive and soft-mode recovery allows a declared mechanism-faithful proxy. The harness must invoke the generated CIC module scripts and produce validator-compatible recovery artifacts.

Inputs

• attempt_dir: initialized Paper2Skills attempt directory.
• skill_root: directory containing the generated CIC module skills.
• runtime_handoff: environment handoff JSON from the prepare-recovery-environment stage.
• Optional seed and batch dimensions for deterministic reduced recovery.

Outputs

• recovery/recovery_result.json.
• recovery/logs/generated_data_item.json.
• recovery/logs/training_trace.json.
• recovery/logs/generated_skill_invocations.json.
• Supporting metrics and mechanism checks.

Workflow

1. Load the module-plan recovery target and runtime handoff.
2. Import the generated transition batching, CIC loss, and particle entropy scripts from skill_root.
3. Build a deterministic transition-skill proxy batch.
4. Compute CIC contrastive loss and kNN entropy rewards.
5. Run one dependency-free optimizer step on tiny linear encoders.
6. Save recovery artifacts with explicit proxy and reduced-training declarations.
7. Record invocation evidence for each core generated skill.

Validation

Run:

python scripts/run_reduced_cic_recovery.py --attempt-dir <attempt_dir> --skill-root <skill_root>
python tests/test_reduced_harness.py

Limitations

This harness does not claim full URLB results. It is a soft-mode proxy that validates the CIC mechanism under bounded runtime constraints.