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name: flow_recovery_diagnostics description: Integrate reduced InterFlow velocity fields and summarize executable mechanism-faithful recovery diagnostics.

Flow Recovery Diagnostics

Use this skill when a recovery experiment must implement the stochastic-interpolant paper mechanism without reading the original repository. Do not use it to claim full CIFAR, ImageNet, or tabular reproduction unless the corresponding real datasets and training stack actually ran.

Inputs

• Numeric base and target samples, time samples, and velocity parameters.
• A declared full or reduced recovery target from module_plan.json.

Outputs

• Deterministic numeric arrays, losses, gradients, or diagnostics depending on the module.
• JSON-compatible evidence for recovery logs.

Workflow

1. Build or consume stochastic-interpolant samples from endpoint pairs.
2. Keep objective computation separate from ODE integration.
3. Emit explicit checks that can be validated by recover-paper.

Validation

Run python -m pytest tests or validate the tree with validate_skill_tree.py --run-tests.

Limitations

The scripts are intentionally small and deterministic; they support bounded proxy recovery and are not a replacement for full neural image or tabular training.