brainstorming
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
reduced_recovery_harnessRun a bounded DDPM proxy experiment that exercises generated schedule, epsilon-loss, and reverse-step skills.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill when full DDPM image training is blocked by runtime limits but soft-mode recovery permits a declared mechanism-faithful proxy. The harness must import or call the generated DDPM module skills rather than duplicating their core equations silently. Do not use it to claim full CIFAR-10 FID or Inception Score reproduction.
module_plan.json and environment/runtime_handoff.json.diffusion_schedule, epsilon_objective, and reverse_denoising_step.recovery/logs/generated_data_item.json.recovery/logs/training_trace.json with params_before and params_after.recovery/logs/generated_skill_invocations.json.recovery/source_manifest.json.recovery/recovery_result.json.x_t using closed-form DDPM noising.Run the harness script, then run:
python <distiller>/recover-paper/scripts/validate_recovery_experiment.py <attempt_dir> --output <attempt_dir>/recovery/experiment_validation.json
A valid soft-mode proxy must report is_proxy: true, reduced_training_executed: true, optimizer_step_executed: true, and training_step_executed: false.
The harness validates DDPM mechanism execution on a reduced synthetic dataset. It is not full image generation, does not estimate FID, and must be interpreted as proxy evidence only.