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name: ttur_update_schedule description: Run deterministic two-time-scale update traces for GAN-style min-max proxy dynamics.

Ttur Update Schedule

Use this skill when a recovery or implementation needs the ttur_schedule module from the TTUR/FID paper without rereading the original repository. Do not use it to claim full GAN reproduction unless real image features, dataset statistics, and training evidence are available.

Inputs

• Numeric arrays or JSON files matching the module contract.
• For recovery orchestration, a module-plan target and runtime handoff.

Outputs

• Deterministic JSON-compatible statistics, distances, traces, or recovery checks.
• Diagnostics sufficient to audit reduced/proxy recovery.

Workflow

1. Validate input shapes and finite numeric values.
2. Execute the module-specific deterministic script in scripts/.
3. Preserve diagnostics and command evidence when used in recovery.
4. Treat synthetic activations or toy dynamics as proxy evidence only.

Validation

Run python tests/test_ttur_update_schedule.py from this skill directory.

Limitations

This skill is self-contained and does not require the original TTUR repository. It does not download Inception models or train full GANs.

Refinement Note

Include an equal-rate ablation when possible; it should set separate_rates false so TTUR evidence cannot be satisfied by a single shared learning rate.