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name: iterative_refinement_sampler description: Run a deterministic SR3-inspired reverse denoising trajectory conditioned on low-resolution input for proxy recovery.

Iterative Refinement Sampler

Use this skill after a denoising objective has produced a parameter estimate. It tests the SR3 generation pattern: start from noise and repeatedly refine while conditioning on the low-resolution input.

Inputs

• Initial noisy scalar state.
• Conditioning scalar and scale factor.
• Learned scalar denoiser weight.
• Number of reverse refinement steps.

Outputs

• A trajectory list with per-step states.
• Final proxy super-resolved estimate.

Workflow

1. Initialize from a noisy state rather than the clean target.
2. At every step, combine the current state, condition-derived target prior, and learned denoiser correction.
3. Save the full trajectory for recovery analysis.
4. Compare final distance to the target against the initial distance outside this skill.

Validation

Run python tests/test_sampler.py or validate the skill tree with --run-tests.

Limitations

This scalar trajectory cannot measure perceptual image quality; it checks iterative conditioning mechanics only.