The paper introduces FlowGDA, a flow‑matching framework for gradual domain adaptation that models adaptation as continuous transport on a statistical manifold. FlowGDA learns time‑dependent velocity fields to generate smooth domain trajectories, avoiding adversarial Min–Max optimization by using a simulation‑free regression objective. The method incorporates source‑anchored transport, semantic consistency loss, and a two‑stage optimization scheme to prevent semantic drift, achieving superior performance over state‑of‑the‑art techniques.
By Yubo Huang, Yushe Cao, Zixi Wang, Jingzehua Xu, Xin Lai, Xiongyu Hu, Shuai Zhang
The paper introduces Finsler Flow Matching (FFM), a method for learning continuous stochastic dynamics from discrete Markov transition graphs in single‑cell snapshot data. FFM constructs a Finsler geometry using first and second local moments, learns directed geodesics via neural networks, and employs these to build geometry‑aware stochastic paths that can be simulated without explicit score or flow matching. Benchmark tests show that FFM better recovers intermediate cellular states, especially when dynamics are directional or anisotropic.
By Niklas Canova, Jonas Simon Fleck
arXiv:2608. 03916v1 Announce Type: new Abstract: Trajectory inference is a fundamental problem in many scientific domains: given a collection of unpaired snapshots of observations at discrete time points, the goal is to generate smooth trajectories that best resemble and interpolate the data.
By Bartolo Dazzini, Giovanni Conforti, Alain Durmus, Aram-Alexandre Pooladian
The paper introduces Function Projection for Flow Matching (FP‑FM), an algorithm that conditions generative models directly on samples from a target distribution. FP‑FM learns basis functions spanning velocity fields for training distributions and adapts to new distributions via a simple least‑squares projection, enabling efficient sample generation without additional training at inference time. Variants of the method trade expressivity for computational cost by enriching coefficient calculations, and the approach shows markedly better precision and recall than baselines, especially on unseen distributions.
By Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta, David Fridovich-Keil, Sandeep P. Chinchali, Ufuk Topcu
arXiv:2602.08318v3 Announce Type: replace-cross
Abstract: Flow matching (FM) is increasingly used in scientific domains for time series generation and forecasting, where data often arise from underly...
By Soon Hoe Lim, Shizheng Lin, Michael W. Mahoney, N. Benjamin Erichson
We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity field, we jointly train an endpoint-preserving p...
ALICE is a foundation model that estimates mutual information (MI) without per‑distribution training. Trained only on synthetic distributions, it acts as an in‑context estimator of rectified‑flow velocity fields, producing MI via a fixed identity that integrates squared differences between joint and conditional fields. The authors validate ALICE on a challenging benchmark and demonstrate its applicability to unseen data in biology, genetics, and neuroscience, achieving performance comparable to neural estimators trained separately for each distribution.
By Giulio Franzese, Simone Rossi, Pietro Michiardi
arXiv:2611.06796v1 Announce Type: new
Abstract: Few-step distillation offers efficient generation with diffusion and flow models, but preserving quality with only a few sampling steps remains challen...
By Yihong Luo, Tianyu Pang, Tianyang Hu
arXiv:2608. 03967v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects, requiring only an unnormalised target density specified through a reward.
By Yordan Raykov, Rodrigo Veiga
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
By Zhenglun Kong, Mufan Qiu, John Boesen, Xiang Lin, Sukwon Yun, Tianlong Chen, Manolis Kellis, Marinka Zitnik
The paper introduces ReCouple, a framework that constructs coarse-grained optimal transport (OT) couplings to improve flow matching without requiring rollouts. Extensions ReCouple+ and ReCouple++ address continuous-source settings and add spatial regularization, respectively. Experiments show that these methods balance coupling optimality, neural network realizability, and generation quality, especially in low NFE regimes.
By Soon Hoe Lim, Aditi Gupta, Michael W. Mahoney, N. Benjamin Erichson
arXiv:2607. 26398v1 Announce Type: new Abstract: Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration.
By Mark Goldstein, Anshuk Uppal, Raghav Singhal, Aahlad Puli, Rajesh Ranganath