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name: stochastic_interpolant_protocol description: Construct endpoint-valid stochastic interpolant samples with latent schedules and derivative targets for reduced or full generative recovery experiments.

Stochastic Interpolant Protocol

Use this skill when a recovery or implementation needs to construct paper-faithful stochastic-interpolant tuples from endpoint samples. Do not use it as a sampler by itself; it only defines the bridge data used to learn fields.

Inputs

• Endpoint values x0 and x1 with the same length.
• Time values t in [0, 1], either one scalar or one value per sample.
• Latent noise z with the same length as the endpoints.
• Schedule name; the included script supports linear_quadratic_gamma.

Outputs

• x_t = (1 - t) x0 + t x1 + gamma(t) z.
• dot_x_t = x1 - x0 + gamma_dot(t) z.
• gamma, gamma_dot, and endpoint-validity diagnostics.

Workflow

1. Validate matching endpoint and latent vector lengths.
2. Broadcast scalar time or validate per-sample time values.
3. Use the linear interpolant and gamma(t)=2t(1-t) unless the caller supplies another validated implementation.
4. Keep score-related division outside this skill because gamma=0 at endpoints.
5. Save diagnostics when the output is used as recovery evidence.

Validation

Run:

python scripts/interpolant_protocol.py --demo
python tests/test_interpolant_protocol.py

Limitations

This skill intentionally avoids neural models, endpoint coupling optimization, and source-repository assumptions. It preserves the paper's construction contract and leaves field learning to objective skills.