LatentFlow framework conditions stochastic processes in seconds without training

LatentFlow provides a unified mathematical framework for conditioning stochastic processes—a notoriously difficult problem when observations are nonlinear, likelihoods are non-Gaussian, or global constraints apply. The key insight is to represent any stochastic process as a deterministic transformation of a simple latent innovation. This reduces the complex problem of process-level conditioning to a latent-space inference problem: pull the likelihood back through the transformation, sample the resulting law using a guided probability flow, and push samples forward. The construction is provably exact at the level of the target law; approximation enters only through finite terminal noising, Monte Carlo guidance, and time discretization, all of which are explicit and systematically reducible.

Because LatentFlow is training-free, conditioning reduces to solving a single reverse-time stochastic differential equation (SDE). This enables conditional sampling in seconds on a single desktop CPU across a remarkably broad set of model classes that previously had no shared scalable method. These include classical spatial priors, nonlinear stochastic dynamics, mechanistic models from physics and biology, stochastic partial differential equations, heavy-tailed and extreme-value processes, point and discrete-state processes, and even neural-network or simulator-defined processes. By eliminating the need for bespoke algorithmic constructions, LatentFlow promises to make advanced stochastic conditioning practical for real-world applications in science, engineering, and data analysis.