← Back to all news
arXiv AI By Michael Chertkov

Analytic Bridge Diffusions for Controlled Path Generation

Read the original on arXiv AI →

arXiv:2605. 02961v2 Announce Type: replace-cross Abstract: Most modern bridge-diffusion methods achieve finite-time transport by specifying an interpolation, Schrodinger-bridge, or stochastic-control objective and then learning the associated score or drift field with a neural network.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Jul 21

Twisted Schr\"odinger Bridge Matching

arXiv:2607. 16987v1 Announce Type: cross Abstract: Over the past few years, diffusion-based Schr\"odinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with successful applications to generative modeling.

By Maxence Noble, Marie Scheid, Yazid Janati, Eric Moulines, Alain Durmus
diffusion
More like this →
arXiv Machine Learning
Aug 20

Self-supervised In-context Operator Learning for Stochastic Mean-Field Control

The paper introduces a mesh‑free, self‑supervised neural operator—called the Normalizing Flow Invertible Solution Transformer (NFIST)—for stochastic mean‑field control (MFC). By reformulating the controlled Fokker–Planck dynamics as a deterministic continuity equation via a probability‑flow ODE and an invertible normalizing‑flow transformer, the authors enable closed‑form score evaluation with linear cost per particle. The resulting operator learns from task prompts (distribution parameters or particle clouds) and can solve unseen MFC tasks in a single forward pass, achieving zero‑shot generalization across applications such as stochastic optimal control, Schrödinger bridges, systemic‑risk control, and obstacle‑avoiding path planning.

By Suyi Gao, Mo Zhou, Rongjie Lai
llmsagentsdiffusion
More like this →
arXiv Machine Learning
Jul 3

Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control

arXiv:2604. 08580v2 Announce Type: replace-cross Abstract: Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learning an optimal generative dynamics corresponds to optimizing a control under SDE constraints.

By Carles Domingo-Enrich, Jiequn Han
diffusionfine-tuning
More like this →