AWhat changed
Specified paths between endpoint samples and learned probability-current velocities through quadratic regression objectives; extended the construction with latent noise to relate deterministic flows, scores and tunable diffusions.
Scientific scope
Specified paths between endpoint samples and learned probability-current velocities through quadratic regression objectives; extended the construction with latent noise to relate deterministic flows, scores and tunable diffusions. Population identities assume the stated density, smoothness and integrability conditions; they do not give exact finite-network transport. Closely related Flow Matching and rectified-flow work retain separate credited precedence, not new invention here. Arbitrary interpolants are not automatically optimal transport or Schrödinger bridges; further optimization is needed.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Population identities assume the stated density, smoothness and integrability conditions; they do not give exact finite-network transport. Closely related Flow Matching and rectified-flow work retain separate credited precedence, not new invention here. Arbitrary interpolants are not automatically optimal transport or Schrödinger bridges; further optimization is needed.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Building Normalizing Flows with Stochastic Interpolants
- Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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