Flow-Based Generative Modeling

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 331

All topics

Support-Conditioned Flow Matching Is Kernel Smoothing
May 13, 2026Daniel Matsui SmolaFlow Matching Cross-Attention
Department of Computer Science University of Washington Seattle, WA
Asymmetric Flow Models
May 13, 2026Hansheng Chen, Jan Ackermann, Minseo Kim +2Flow Matching T2I Generation
Stanford University
Coreset-Induced Conditional Velocity Flow Matching
May 13, 2026Xiao Wang, Zihua She, Jianxi SuConditional Flow Matching Rectified Flow
Department of Statistics, Purdue University
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Markov Models MeanFlow
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
May 12, 2026Jacob K. Christopher, James E. Warner, Ferdinando FiorettoConstrained Generative Modeling Flow Matching
University of Virginia · NASA Langley Research Center
Aligning Flow Map Policies with Optimal Q-Guidance
May 12, 2026Christos Ziakas, Alessandra Russo, Avishek Joey BoseFlow Matching Policy Optimization
Imperial College London · Mila
$h$ -control: Training-Free Camera Control via Block-Conditional Gibbs Refinement
May 12, 2026Yuzhu Wang, Xi Ye, Duo Su +2Markov Chain Monte Carlo Camera-Controlled Video Generation
Department of Computer Science and Technology, Tsinghua University · South China University of Technology
One-Step Generative Modeling via Wasserstein Gradient Flows
May 12, 2026Jiaqi Han, Puheng Li, Qiushan Guo +3Wasserstein Gradient Flows Image Generation
Stanford University · ByteDance
Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching
May 12, 2026Aditi Gupta, Soon Hoe Lim, Annan Yu +1Flow Matching Flow-Based Generative Modeling
Lawrence Berkeley National Laboratory · International Computer Science Institute · 1Lawrence Berkeley National Laboratory, 2International Computer Science Institute +4
Language Modeling with Hyperspherical Flows
May 11, 2026Justin Deschenaux, Caglar GulcehreNormalizing Flows Continuous Normalizing Flows
EPFL · Lausanne, Switzerland · EPFL, Lausanne, Switzerland +1
ELF: Embedded Language Flows
May 11, 2026Keya Hu, Linlu Qiu, Yiyang Lu +5Flow-Based Generative Modeling Diffusion Language Models
MIT
Follow the Mean: Reference-Guided Flow Matching
May 11, 2026Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom +1Flow Matching Flow-Based Generative Modeling
University of Amsterdam · AMLab
UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
May 11, 2026Danhui Zhang, Zhe Wang, Qing Qing +6Replay-Based Continual Learning Flow-Based Generative Modeling
Jilin University · Adelaide University · RMIT University +1
Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs
May 10, 2026Yunhong Lu, Qichao Wang, Hengyuan Cao +2Pairwise Preference Learning Rectified Flow
Zhejiang University · Shanghai Institute for Advanced Study-Zhejiang University · Shanghai Institute for Mathematics and Interdisciplinary Sciences
Kinetic-Optimal Scheduling with Moment Correction for Metric-Induced Discrete Flow Matching in Zero-Shot Text-to-Speech
May 10, 2026Dong Yang, Yiyi Cai, Haoyu Zhang +2Flow Matching TTS Synthesis
1The University of Tokyo · 2Independent Researcher
dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models
May 10, 2026Zhengyan Wan, Yidong Ouyang, Panwen Hu +1Group Relative Policy Optimization Image Generation
Mohamed bin Zayed University of Artificial Intelligence · East China Normal University · University of California, Los Angeles +1
On Variance Reduction in Learning Mean Flows
May 10, 2026Juanwu Lu, Ziran WangFlow Matching MeanFlow
Purdue University
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
May 9, 2026Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1Flow Matching Generative Modeling
École Polytechnique, CMAP, IP Paris Route de Saclay, 91120 Palaiseau, France · Università degli Studi di Padova, Via Trieste, 63, 35131 Padova, Italia · LaMME, Université Évry Paris-Saclay Boulevard François Mitterrand 91000, France
Restoration-Aligned Generative Flow Models for Blind Motion Deblurring
May 9, 2026Insoo Kim, Jinwoo ShinFlow Matching Image Deblurring
1NAVER Cloud · 2KAIST AI
Deterministic Decomposition of Stochastic Generative Dynamics
May 9, 2026Xingyu Song, Yuan Mei, Naoya TakeishiFlow Matching Stochastic Differential Equations
1The University of Tokyo · 2Zhejiang University · *Visiting student at The University of Tokyo from Zhejiang University.
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
May 8, 2026Moritz Piening, Richard Duong, Gabriele SteidlFlow Matching Generative Modeling
Institut für Mathematik, Technische Universität Berlin, Germany
Exploring and Exploiting Stability in Latent Flow Matching
May 8, 2026Rania Briq, Michael Kamp, Ohad Fried +2Flow Matching Efficient Inference
Forschungszentrum Jülich · Technical University Dortmund · Lamarr Institute +4
What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
May 8, 2026Alec Helbling, Sebastian Gutierrez Hernandez, Benjamin Hoover +2Flow Matching Flow-Based Generative Modeling
Georgia Tech · IBM Research
Normalizing Trajectory Models
May 8, 2026Jiatao Gu, Tianrong Chen, Ying Shen +3Normalizing Flows Diffusion Model Distillation
Apple · UIUC
Flow-OPD: On-Policy Distillation for Flow Matching Models
May 8, 2026Zhen Fang, Wenxuan Huang, Yu Zeng +8Diffusion Model Alignment Flow Matching
University of Science and Technology of China · University of California, Los Angeles · 3The Chinese University of Hong Kong +1
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
May 8, 2026Ying Shen, Tianrong Chen, Yuan Gao +6Unified Multimodal Models Multimodal Generation
UIUC · Apple
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
May 8, 2026Amin Karimi Monsefi, Dominic Culver, Nikhil Bhendawade +3Flow Matching Diffusion Language Model Inference
1The Ohio State University · 2Apple
Consistency Regularised Gradient Flows for Inverse Problems
May 8, 2026Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra +1Latent Diffusion Models Wasserstein Gradient Flows
Laboratoire MAP5, UMR 8145, Université Paris Cité, CNRS · Department of Mathematics, Imperial College London · Heriot-Watt University, MACS & Maxwell Institute
Scaling Categorical Flow Maps
May 8, 2026Oscar Davis, Anastasiia Filippova, Pierre Ablin +4Language Model Scaling Flow Map Learning
Apple · University of Oxford
Flow Matching for Count Data
May 8, 2026Ganchao Wei, John PearsonFlow Matching Synthetic Data Generation
Department of Neurobiology Department of Statistical Science Duke University Durham, NC, USA · Department of Neurobiology Department of Electrical and Computer Engineering Duke University Durham, NC, USA

May 13, 2026, cs.LGJ/K move · Enter open · S save

Support-Conditioned Flow Matching Is Kernel Smoothing

Daniel Matsui Smola

Department of Computer Science University of Washington Seattle, WA

Flow Matching Cross-Attention Kernel Methods Flow-Based Generative Modeling Conditional Generative Modeling

Open paper AskPDF arXiv

Generative models are often conditioned on a small set of examples via cross-attention. Under the Gaussian optimal-transport path, we show that the exact velocity field induced by a finite support set is a Nadaraya--Watson kernel smoother whose bandwidth decreases with flow time, from broad averaging at early steps to nearest-neighbor at late steps. A single Gaussian-kernel attention head exactly computes this field, connecting cross-attention conditioning to classical kernel theory. The theory predicts three failure regimes: nearest-neighbor collapse of the kernel at high dimension, mismatch between the isotropic kernel and the data geometry, and insufficient support for nonparametric estimation. Experiments on Gaussian mixtures, spherical shells, and DINOv2 ImageNet features confirm that learned conditioning improves in precisely these regimes, and that IP-Adapter's cross-attention implements approximate NW smoothing in practice.

Momentum

Related topics

Latest papers 331