Flow-Based Generative Modeling

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66 papers in the last four weeks, up 288% on the four weeks before. 0.6% of all new papers.

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Structured Coupling for Flow Matching
May 8, 2026Xavier Sumba, Carles Balsells-Rodas, Yingzhen LiFlow Matching Disentangled Representation Learning
Imperial College London
Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
May 8, 2026Kim Yong Tan, Yueming Lyu, Ivor Tsang +1Diffusion Model Guidance Black-Box Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
Conservative Flows: A New Paradigm of Generative Models
May 7, 2026Eshed Gal, Md Shahriar Rahim Siddiqui, Moshe Eliasof +1Langevin Dynamics Generative Flow Networks
The University of British Columbia Vancouver, Canada · University of Cambridge Cambridge, United Kingdom
A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models
May 7, 2026Aditya Ranganath, Mukesh SinghalFlow Matching Generative Modeling
Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore, CA 94551, USA · Department of Electrical Engineering University of California Merced, CA 95343, USA
Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline
May 7, 2026Zhengyi Guo, Jiayuan Sheng, David D. Yao +1Fine-Tuning Flow-Based Generative Modeling
FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation
May 7, 2026Mingfeng Lin, Jiakun Chen, Liang Han +1Flow Matching Conditional Image Generation
Harbin Institute of Technology (Shenzhen)
Flow Matching with Arbitrary Auxiliary Paths
May 7, 2026Xin Peng, Ang GaoConditional Flow Matching Flow-Based Generative Modeling
School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
May 7, 2026Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta +3Flow Matching Flow-Based Generative Modeling
University of Texas at Austin
P-Guide: Parameter-Efficient Prior Steering for Single-Pass CFG Inference
May 7, 2026Xin Peng, Ang GaoFlow Matching Classifier-Free Guidance
School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
May 7, 2026Sicheng Ma, Tianyue Yang, Xiuzhe Wu +1Flow Matching One-Step Generative Modeling
Department of Physics University of Cambridge · The Center for Computational Science University College London · Department of Computer Science University College London
SDFlow: Similarity-Driven Flow Matching for Time Series Generation
May 7, 2026Wei Li, Shibo Feng, Pengcheng Wu +3Flow Matching Time Series Generation
1Shanghai Jiao Tong University · 2Shanghai University · 3Nanyang Technological University +2
Spherical Flows for Sampling Categorical Data
May 7, 2026Jannis Chemseddine, Gregor Kornhardt, Gabriele SteidlFlow Matching Synthetic Data Generation
Technische Universität Berlin
HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
May 6, 2026Nandiraju Gireesh, Yuanliang Ju, Chaoyi Xu +3Hierarchical Planning Long-Horizon Robotic Manipulation
1Peking University · University of Toronto. · 2Galbot
A Few-Step Generative Model on Cumulative Flow Maps
May 5, 2026Zhiqi Li, Duowen Chen, Yuchen Sun +1One-Step Generative Modeling Flow-Based Generative Modeling
Georgia Institute of Technology, Atlanta, United States of America
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
May 5, 2026Hao Zhou, Rui Zhang, Han Wan +1Physics-Informed Generative Modeling Uncertainty Quantification
Gaoling School of Artificial Intelligence, Renmin University of China
Generative Modeling with Orbit-Space Particle Flow Matching
May 4, 2026Sinan Wang, Jinjin He, Shenyifan Lu +3Flow Matching One-Step Generative Modeling
Georgia Institute of Technology, USA
Stable GFlowNets with Probabilistic Guarantees
May 3, 2026Zengxiang Lei, Ananth Shreekumar, Jonathan Rosenthal +6Generative Flow Networks Flow-Based Generative Modeling
Lyles School of Civil and Construction Engineering, Purdue University, IN, USA · Department of Computer Science, Purdue University, IN, USA · Computer Science and Engineering, University of California, Santa Cruz, USA
CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making
May 2, 2026Guowei Zou, Haitao Wang, Beiwen Zhang +2Flow Matching Multi-Agent Coordination
Sun Yat-sen University
Posterior Augmented Flow Matching
May 1, 2026George Stoica, Sayak Paul, Matthew Wallingford +6Flow Matching Conditional Image Generation
Georgia Tech · University of Washington · Hugging Face +2
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
May 1, 2026Jiarui Xing, Song Wang, Jian WangFlow Matching Uncertainty Quantification
Yale University · Harvard Medical School
Binomial flows: Denoising and flow matching for discrete ordinal data
May 1, 2026Yair Shenfeld, Ricardo Baptista, Stefano PeluchettiFlow Matching Flow-Based Generative Modeling
A unified perspective on fine-tuning and sampling with diffusion and flow models
Apr 30, 2026Carles Domingo-Enrich, Yuanqi Du, Michael S. AlbergoStochastic Optimal Control Diffusion Sampling
Microsoft Research New England · Harvard University, Kempner Institute
How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
Apr 29, 2026Jerry Y. Huang, Justin Lin, Sheel Shah +2Generative Modeling Flow-Based Generative Modeling
Carnegie Mellon University
FlowS: One-Step Motion Prediction via Local Transport Conditioning
Apr 28, 2026Leandro Di Bella, Adrian Munteanu, Bruno CornelisFlow Matching Trajectory Generation
Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium · IMEC, Kapeldreef 75, B-3001 Leuven, Belgium
CoreFlow: Low-Rank Matrix Generative Models
Apr 27, 2026Dongze Wu, Linglingzhi Zhu, Yao XieGenerative Modeling Continuous Normalizing Flows
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
FlowPlace: Flow Matching for Chip Placement
Apr 26, 2026Peng Xie, Ke Xue, Yunqi Shi +6Constrained Generative Modeling Flow Matching
1State Key Laboratory of Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · 3Huawei Noah’s Ark Lab, China
MotionHiFlow: Text-to-motion via hierarchical flow matching
Apr 25, 2026Heng Li, Xiaotong Lin, Ling-An Zeng +3Flow Matching Text-to-Motion Generation
Sun Yat-sen University · Shandong University · Guangdong Province Key Laboratory of Information Security Technology, China +1
Normalizing Flows with Iterative Denoising
Apr 21, 2026Tianrong Chen, Jiatao Gu, David Berthelot +2Normalizing Flows Autoregressive Image Generation
Apple Machine Learning Research.
Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance
Apr 21, 2026Weitao DuFlow Matching Image Generation
DAMO Academy
Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokinetics
Apr 19, 2026César Ojeda, Niklas Hartung, Wilhelm Huisinga +6Zero-Shot Time Series Forecasting Healthcare
University of Potsdam, Germany · Technical University Berlin, Germany · Freie Universität Berlin, Germany +2

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Structured Coupling for Flow Matching

Xavier Sumba, Carles Balsells-Rodas, Yingzhen Li

Imperial College London

Flow Matching Disentangled Representation Learning Latent Variable Models Unsupervised Representation Learning Generative Modeling Flow-Based Generative Modeling

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Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent structure. Latent-variable models, by contrast, capture structure but often sacrifice generative quality. We bridge this gap by proposing Structured Coupling for Flow Matching (SCFM), a cooperative framework that augments flow matching with structured latent representation learning. By introducing structured latent variables and exogenous noise into the source, SCFM jointly learns a structured prior (via latent variable modeling) and a continuous transport map (via flow matching). It uses a shared time-dependent recognition network for both latent variable model variational inference and intermediate-time flow velocity estimation. This yields a structurally informed yet unconditional, simulation-free flow model, where the latent variable model can also assist flow sampling. Empirically, SCFM facilitates unsupervised latent representation learning for clustering, disentanglement and downstream tasks, while remaining competitive with flow matching in sample quality, showing that meaningful structure can be learned without sacrificing generative fidelity.

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Latest papers 331