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name: predictor_corrector_sampling description: Execute bounded reverse predictor-corrector sampling checks for score-SDE mechanism-faithful recovery.

Predictor-Corrector Sampling

Use this skill when a score-SDE recovery must exercise reverse-dynamics sampling rather than only a training loss. It implements a deterministic scalar predictor-corrector loop that logs every reverse predictor and score-based corrector update.

Inputs

• Initial scalar latent state.
• Strictly decreasing time grid.
• Score function parameters for score(x)=a*x+c or an injected callable.
• Predictor step scale and corrector step size.

Outputs

• Ordered trajectory entries for predictor and corrector phases.
• Final sample value.
• Mechanism checks showing both phases executed.

Workflow

1. Validate the time grid is decreasing.
2. At each interval, evaluate the score and make a reverse predictor step.
3. Apply a corrector step in the score direction.
4. Append diagnostics for phase, time, state, score, and update.
5. Return the trajectory for recovery logs or tests.

Validation

Run python scripts/predictor_corrector_sampling.py --self-test or the generated skill validator with tests enabled.

Limitations

This scalar implementation is a mechanism check. It does not produce image samples or estimate FID.