Latest Flow Matching Research Papers

The newest Flow Matching papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Flow Matching so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• Coupled Guidance for Flow Matching
  Mateo Clémente, Léo Maxime Brunswic, Rui Heng Yang, Amir Rasouli · arXiv · Oct 29, 2026

  Guidance methods are essential tools for steering the generation process in diffusion models, improving the generalizability of stochastic robotic controllers and enabling high-quality controlled image synthesis. Despite strong parallels be…

• Physics-Guided Flow Matching: A Generative AI Framework for Global Total Electron Content Forecasting
  Si-Tong Yang, Feng Ye, Xian Lu, Dong Lin et al. · arXiv · Oct 27, 2026

  Accurate forecasting of global total electron content (TEC) is important for space weather monitoring and the reliable operation of satellite communication and navigation systems. We propose a physics-guided generative artificial intelligen…

• Cross-Scenario Controller Parameter Transformation from Real-World Driving Data through Flow Matching
  Christian Zink, Maik Pfefferkorn, Eric Lenz, Felix Kallmeyer et al. · arXiv · Oct 23, 2026

  Advanced Driver Assistance Systems have become increasingly widespread, aiming to support drivers, reduce workload, and improve traffic safety. However, many systems are rarely used because their behavior feels unnatural and does not match …

• Trajectory Matching: Discrete Flow Matching with Structured Actions
  Niklas Kemper, Marten Lienen, David Lüdke, Stephan Günnemann · arXiv · Oct 23, 2026

  The natural units of structured discrete generation are often not independent single-component edits but richer actions: attaching a fragment to a molecule, inserting a node together with an incident edge, or growing a hierarchy by adding a…

• Piano2Pop: Expanding Piano Demos into Pop Productions Without Aligned Supervision
  Yun-Chen Cheng, Chih-Pin Tan, Tzu-Hung Huang, Yi-Hsuan Yang · arXiv · Oct 22, 2026

  We introduce Piano2Pop, a music-expansion task that transforms piano MIDI into instrumental pop productions. Enabled by recent advances in music generation and polyphonic music transcription, our pseudo-supervised approach conditions a pret…

• Velocity Ambiguity Profiles: Time-Resolved Bayes-Risk Diagnostics for Flow Matching
  Yunpeng Mei, Xiaowen Zhu, Chenyu Wang, Chenbo Xin et al. · arXiv · Oct 21, 2026

  Flow matching trains a continuum of velocity regression problems but usually reports a loss averaged over interpolation time, obscuring which regions are limited by intrinsic ambiguity and which remain data-limited. We introduce the Velocit…

• Unlocking Spatial Grounding in Flow-Matching Models via SNR Tuning
  Martin Thißen, Elke Hergenröther · arXiv · Oct 21, 2026

  Despite the success of foundation models in image synthesis, achieving fine-grained spatial control remains a significant challenge, typically requiring auxiliary structural adapters or complex inference-time steering. While modern vision-l…

• Intelligent design of shear wall layout based on conditional flow matching
  Hongmei Zhang, Jiacheng Guo, Jixing Cao, Ahmed Sharhan · Advanced Engineering Inform... · Oct 21, 2026
• Kronecker Delta Rule for Long Context Linear Attention
  Anamaria-Roberta Hartl, Levente Zólyomi, Michael List, Günter Klambauer et al. · arXiv · Oct 19, 2026

  We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories. KronLSTM extends the xLSTM family with a memory-revision mechanism that corrects targeted associati…

• ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference
  Kai Lehman, Jameson Dong, Carolina Cuesta-Lazaro, Niall Jeffrey et al. · arXiv · Oct 16, 2026

  The evidence, $\mathcal{Z} = p(x | \mathcal{M})$, of a data-generating model $\mathcal{M}$ is a powerful tool in Bayesian frameworks: it enables model inference, model misspecification tests, and goodness-of-fit assessment as an alternative…

• REKI: Registering Knowledge in VLAs
  Juliette Marrie, Paul Pacaud, Adrien Ramanana Rahary, Shizhe Chen et al. · arXiv · Oct 15, 2026

  We introduce REKI, a 2.4B vision-language-action model (VLA) pretrained exclusively on public data that sets a new Pareto frontier between performance and model size: on LIBERO-Plus, RoboTwin Clean2Rand and RoboCasa365, it is outperformed o…

• ReAl: Refine neural operators via aligning dynamical flow
  Jing Xu, Zenglin Xu, Li Yi, Ruoxi Jiang et al. · arXiv · Oct 14, 2026

  Neural partial differential equation (PDE) surrogates trained with mean squared error (MSE) can capture dominant dynamics but often lose high-frequency information and fine-scale structures. Generative models (e.g., Flow Matching) can captu…

• Are We Overthinking One-Step Generation?
  Woobin Park, Jaeyeon Kim, Yilun Du, Sitan Chen et al. · arXiv · Oct 14, 2026

  Diffusion and flow matching are now dominant frameworks for generative modeling in continuous domains, but they typically require multiple network evaluations at inference. This has motivated extensive research on one-step generative modeli…

• FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation
  Mohamed Kamel, Tamer Nadeem · arXiv · Oct 14, 2026

  Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency. Classical matched filtering and LMMSE receivers are limite…

• Scaling Online Optimal Transport Minibatches for Flow Matching
  Peter Chen, Sergey Levine · arXiv · Oct 13, 2026

  A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically on how source samples are paired with data. Independent random pairing introduces substanti…

• Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast
  Jiajing Hu, Xu Han, Li-Ping Liu · arXiv · Oct 13, 2026

  Accurate uncertainty quantification is critical for many time series forecasting applications, and recent studies have increasingly adopted generative models to capture predictive uncertainty. Existing approaches often combine deterministic…

• Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning
  Teo Deveney, Lea Bogensperger, Hok Shing Wong · arXiv · Oct 9, 2026

  Flow-matching and diffusion models are increasingly used as priors for linear inverse problems, however, most methods are bespoke to one class of trajectory prior. Plug-and-play methods have been proposed, however these implicitly decorrela…

• CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope
  Gautier Marti · arXiv · Oct 9, 2026

  Correlation matrices combine nonlinear positive-definiteness constraints with a simultaneous row-column permutation symmetry. Their generation therefore involves two distinct problems. First, every output must remain on the correlation elli…

• Few-Step Generation via Data-Space Iteration
  Shanchuan Lin, Yansong Peng, Fu-Yun Wang, Haoqi Fan · arXiv · Oct 8, 2026

  Flow matching has emerged as a scalable paradigm for training high-quality generative models, but sampling from the learned probability flow requires many network evaluations. Distillation can reduce this cost to one or a few evaluations; h…

• Decoupling Exploration from Optimization in RLVR
  Saif Punjwani, Micah Goldblum · arXiv · Oct 7, 2026

  Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas abs…

• Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
  Peng Liu, Shaoxiang Qin, Theodore Potsis, Lili Ji et al. · arXiv · Oct 7, 2026

  Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limite…

• QF3: Fast Flow RL with Filtered Q-Gradients
  Chung Min Kim, Brent Yi, David McAllister, Hongsuk Choi et al. · arXiv · Oct 6, 2026

  Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for improving pre-trained flow policies or learning them from scratch through interaction. We i…

• Denoising Hierarchical Representations: Joint Continuous Diffusion for Language Modeling
  Mathias Ollu, Nikos Komodakis · arXiv · Oct 6, 2026

  Diffusion Language Models (DLMs) hold the promise of order-agnostic, parallel text generation. Recently, continuous diffusion and flow matching models have seen substantial gains, driven by carefully crafted token representations and diffus…

• Co-Evolving Paths and Flows via Path-Flow Alignment
  Zeyu Michael Li, William Xingxu Chen, Xiang Cheng · arXiv · Oct 6, 2026

  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 path network and a flow network using the…

• FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching
  Emmanouil Panagiotou, Eirini Ntoutsi · arXiv · Oct 6, 2026

  In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outcome. To be useful in practice, such an explanation should ch…

• Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals
  Jaedong Hwang · arXiv · Oct 6, 2026

  Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. For multi-channel brain recordings, however, part of this structure is known in advance. Ele…

• Two-Sample Testing via Generative Processes
  Eshant English, Kenji Fukumizu, Taiji Suzuki · arXiv · Oct 6, 2026

  Deciding whether two samples come from the same distribution is a classical problem in statistics, and generative transport offers a new way to approach it. We build a stochastic interpolant directly between the two samples and observe that…

• IdeaLens: Detecting AI Ideas in Long-form Writing
  Rishanth Rajendhran, Minjoon Choi, Jenna Russell, Ramya Namuduri et al. · arXiv · Oct 5, 2026

  While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came fr…

• What Matters for Latent Reasoning with Flow Matching
  Yassine Ouali, Adrian Bulat, Georgios Tzimiropoulos · arXiv · Oct 5, 2026

  Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought must meet five requirements: it should be useful, helping produce the correct answer rat…