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name: score_sde_loss description: Construct continuous-time denoising score matching losses and tiny optimizer-step checks for Score SDE recovery.

Score SDE Loss

Use this skill when training or validating a time-conditioned score model for SDE-perturbed data. It is appropriate for reduced recovery experiments that must prove the denoising score matching mechanism ran.

Inputs

• Clean numeric batch.
• SDE marginal mean/std provider.
• Score model parameters for a simple callable or an external score function.
• Time and Gaussian noise samples.
• Learning rate for a reduced optimizer step.

Outputs

• Perturbed data and target score.
• Scalar loss before and after update.
• Parameters before and after update.
• Boolean evidence for optimizer execution.

Workflow

1. Use an SDE marginal to perturb data at continuous time t.
2. Build target score -noise / std.
3. Evaluate squared score residuals.
4. For reduced recovery, run a gradient step on a tiny linear score model.
5. Save loss and parameter traces with params_before and params_after.

Validation

Run:

python tests/test_score_loss.py
python scripts/score_loss.py --data -1 -0.5 0.5 1 --t 0.4 --noise 0.2 -0.1 0.1 -0.2

Limitations

The included optimizer is a deterministic reduced proxy for mechanism validation. It does not replace full neural image-model training.