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Toward Semantically-Consistent Tuning-Free Customization for Rectified Flow Transformers

Jian Jin, Kai Zhang, Zhenyong Fu, Jian Yang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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Geometry-Aware Flow Matching for Sparse-View 3D Gaussian Splatting

Abdullah Azeem, Ruisheng Wang, Qingquan Li, Abubakar Siddique

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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45%Niche pick
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State Augmented Flows

Dwij Mehta, Arvind Renganathan, Vipin Kumar

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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SQUEEZE: Preserving Homeomorphism and Smooth in Higher-Dimensional Flows

Wangzi Yao, Yue Sun, Rongmin Chen, Honglie Wang and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Flow-Guided Target-Space Alignment via Path Consistency

Ruizhi Yuan, Zeqiu Yu, Wei Gao, Wei Chen and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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ControlFlow3D: Distilling Multi-View Knowledge into Latent Flow Matching for Point Cloud Upsampling

Yuang Liu, Zhi Zuo, Zhengkai Zhao, Lirui Zhang and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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CuBic: Curvature-Driven Dynamic Inference Caching for Fast, High-Fidelity Flow Matching

Yuyang Chen, Linqian Zeng, Yijin Zhou, Hengjie Li and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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Inference-Time Self-Aligned Drifting for Few-Step Flow Matching

Shigui Li, JIAN XU, Wei Chen, Junmei Yang and 3 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Exact Recovery of Lipschitz Orthogonal Coordinate Transformations via Constrained Normalizing Flows

Isaac Manring, Kejun Huang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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FLINT: Coupling Proximal Initialization and Bounded Stochasticity for Flow-Matching Inverse Problems

Junseo Bang, daewon choi, Dong Ju Mun, Se Young Chun

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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Co-Evolving Interpolants and Flows via Path-Flow Alignment

Zeyu M Li, William X Chen, Xiang Cheng

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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High-Fidelity Boltzmann Samplingvia Physical Prior Lifted Continuous GFlowNets

Xizhi Tian, Wenhao Deng, Haojia Hui, Hang Chen and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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GenRM-Flow: Generators are Process-aware Reward Models in Flow Matching

Siming Fu, Zheming Fu, Ruizhe He, Zeyue Xue and 6 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AegisFlow: Training-free Non-myopic Path-safe Guided Flow Matching

Kunpeng Liu, Boshi Zhang, Keyou You

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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ESS-Flow: training-free guidance as Bayesian inference in source space

Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund, Fredrik Lindsten

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026TuftsFlow matching

Primal-Dual Flow Matching for Sample-Wise Constrained Generation

Zhengyan Huan, Peter Y. Lu, Shuchin Aeron

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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57%Worth a look
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DN-Flow: Driver–Navigator Structured Flow Matching for Mixed-Type Tabular Data Generation

Guangzhao Chai, Jing Liu, Youxi Wu, Yan Li

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Compatible Likelihoods for Flow Matching on Manifolds

Lucas Ng, Georgios Batzolis, Mark Girolami

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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Contact Geometry for Generative Models: An Unbalanced Optimal Transport Formulation

Andrea Testa, Søren Hauberg, Andras Kupcsik, Tamim Asfour and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Coupled Guidance for Flow Matching

Mateo Clémente, Leo Brunswic, Yang, Amir Rasouli

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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Geometric Velocity Regularity for Flow Matching on Manifold-Concentrated Data

Shuntuo Xu, Zhou Yu, Kenji Fukumizu

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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DeFlowCritic: Dense Latent Reward Alignment for Text-to-Image Flow Matching Models

Zeeshan Khan, Xin Yu, Shizhe Chen, Cordelia Schmid

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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67%Highly rated
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SiliciclasticReservoirs: A Million-Reservoir Dataset and Flow-Matching Foundation Model for 3D Siliciclastic Reservoir Generation

Ilgar Baghishov, Elnara Rustamzade, Graeme Henkelman, John Foster and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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Temporal Pair Consistency for Flow Matching

Chika Maduabuchi, Jindong Wang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data

Hao Mo, Liying Yang, Shumin Yao, Xinxing Yu and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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Mamba Flow Matching Neural Processes: Linear-Time Inference for Irregularly Observed Spatial Fields

Cosmo Santoni, Giovanni Charles, Timothy James Hitge, Oliver Watson and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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Robust Flow Matching under Target Corruption and Label Noise

Mert Can Kurucu, Erik Englesson, Hossein Azizpour

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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Stable GFlowNets with Probabilistic Guarantees

Zengxiang Lei, Ananth Shreekumar, Jonathan Rosenthal, Ruoyu Song and 5 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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Space Group Conditional Flow Matching

Omri Puny, Yaron Lipman, Benjamin K Miller

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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Plug-and-Play ADMM for Inverse Problems with Flow Matching Denoiser

Dibyanshu Kumar, Magda Gregorova

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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83%Must read
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Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness

SelFix selects fixed-point solutions by trajectory straightness for rectified flow inversion, improving reconstruction and editing accuracy.

Semin Kim, Jihwan Yoon, Seunghoon Hong

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution

LucidNFT improves flow-based real-world super-resolution via LR-anchored multi-reward preference optimization that reduces hallucinations while preserving perceptual quality.

Song Fei, Tian Ye, Sixiang Chen, Zhaohu Xing and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 4/5
medium 7/10
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76%Highly rated
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Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures

Truncated signature inversion is reframed as learning conditional path distributions via signature-conditioned flow matching, with derived Bayes error baselines and validated reconstruction on real data.

Junoh Kang, Kiseop Lee, Bohyung Han

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 4/5
medium 5/10
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86%Must read
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Accelerating Rectified Flow Models via Trajectory-Aware Caching

TACache decomposes rectified flow velocity errors into magnitude and direction components to skip steps and reconstruct velocities without extra evaluations, achieving up to 4.14x faster image and 2.11x faster video generation.

Xiao Liu, Kai Liu, Naiyang Guan, Hongliang Lu and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Preconditioned Flow Matching

Ill-conditioned intermediate covariances make flow matching regress low-variance directions slowly; preconditioning into isotropic space improves optimization and generation quality.

Shadab Ahamed, Eshed Gal, Md Shahriar Rahim Siddiqui, Simon Ghyselincks and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 3/5
medium 9/10
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72%Highly rated
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VTV-FM: Flow Matching through Variational Terminal-Velocity Closure

VTV-FM enables second-order flow matching via a minimum-acceleration variational terminal-velocity closure for static data, improving transport geometry and generation quality.

Haoyang Jiang, Yuheng Li, Di Yang, Yanhai Xiong and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 3/5
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88%Must read
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Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching

Condition-dependent source distributions for flow matching improve text-to-image generation via variance regularization and directional alignment, accelerating convergence up to 3x in FID.

Junwan Kim, Jiho Park, Seonghu Jeon, Seungryong Kim

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
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86%Must read
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Improving Function Space Flow Matching with Kernel Optimal Transport

kFFM replaces arbitrary pairing in Functional Flow Matching with kernel optimal transport to improve infinite-dimensional generative modeling and outperforms baselines on time-series and PDE benchmarks.

Fred Xu, Thomas Markovich, Barbora Barancikova, Yizhou Sun

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 3/5
medium 9/10
strict 2/5
72%Highly rated
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When Riemann flows with Wasserstein: Generative Modeling of Probability Distributions on Manifolds

RWEFM generatively models meta-distributions on manifolds via Riemannian Wasserstein flow matching, yielding valid flows and efficient GPU-optimal transport approximations for non-Euclidean data.

Doron Haviv, Edward De Brouwer, Rishabh Anand, Rex Ying and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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83%Must read
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GenRec: Knowing Where to Reconstruct and Where to Generate

GenRec separates reconstruction and generation via observation masks to preserve fidelity in visible regions while synthesizing plausible unobserved content.

Ata Çelen, Jaewoo Jung, Federico Tombari, Marc Pollefeys and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 4/5
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91%Must read
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Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems

Flow map denoisers implicitly define a one-parameter family spanning the distortion-perception tradeoff via lookahead parameter t, matching or exceeding specialized baselines across inverse problems.

Nicolas Zilberstein, Morteza Mardani, Santiago Segarra

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 17 of 20 reviewers recommend it
lenient 4/5
medium 10/10
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70%Highly rated
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Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions

Discrete Flow Matching achieves non-asymptotic KL and total variation convergence bounds under minimal approximation error assumptions with improved scaling in vocabulary size and dimension.

Le-Tuyet-Nhi PHAM, Giovanni Conforti, Zhenjie Ren, Alain Durmus

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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AI panel: 4 of 20 reviewers recommend it
lenient 2/5
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74%Highly rated
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World Motion Models: Flexible Sequence Modeling of SE(3) Trajectories

World Motion Models unify dynamic 3D entities via sparse SE(3) trajectories and flow-matching, enabling any-to-any conditioning across prediction, control, and retargeting tasks.

Jiahui Lei, Qianqian Wang, Trevor Darrell, Angjoo Kanazawa

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5
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70%Highly rated
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A Theory on Flow Matching with Neural Networks

Flow matching with over-parameterized 2-layer ReLU networks achieves convergence, generalization, and sample-generation Wasserstein guarantees via multi-task representation bounds.

Yihan He, Qishuo Yin, Yuan Cao, Jianqing Fan and 1 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 5 of 20 reviewers recommend it
lenient 2/5
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strict 1/5
72%Highly rated
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Unlocking the Duality between Flow and Field Matching

CFM and forward-only IFM are equivalent via a bijection, but general IFM is strictly more expressive, yielding cross-framework techniques.

Daniil Shlenskii, Alexander Varlamov, Nazar Buzun, Aleksandr Korotin

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation

STREAM applies Riemannian flow matching to histopathology VFM patch-token features on the hypersphere to avoid conditioning collapse, achieving state-of-the-art synthetic image generation via a stochastic bridge and anisotropic decoder.

Won June Cho, Daeky Jeong, Hyeongyeol Lim, Hongjun Yoon

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 0/5
71%Highly rated
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Structured Coupling for Flow Matching

SCFM augments flow matching with structured latent variables and a shared recognition network to jointly learn structured priors and continuous transport maps, enabling unsupervised clustering and disentanglement without sacrificing generative quality.

Francisco Sumba Toral, Carles Balsells Rodas, Yingzhen Li

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 1

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5
88%Must read
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Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

Active Flow Expansion uses verifier-guided active exploration to grow a flow model's generable set, yielding theoretical guarantees and superior out-of-distribution molecule and protein design.

Riccardo De Santi, Bruce D Lee, Cristian Jensen, Kimon Protopapas and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels

Classifier-based adaptive stopping treats MCMC trajectory termination as learnable via GFlowNets, reducing trajectory lengths while improving mode coverage and mixing.

Kirill Korolev, Nikita Morozov, Stepan Pavlenko, Esmeralda S Whitammer and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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Path-Guided Flow Matching for Dataset Distillation

PGFM introduces flow matching for generative dataset distillation with deterministic ODE synthesis, continuous path-to-prototype guidance, and 7.6x efficiency gains over diffusion methods.

xuhui li, Zhengquan luo, Zixu Wu, Xiwei Liu and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
71%Highly rated
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Total Variation Rates for Riemannian Flow Matching

Nonasymptotic total variation analysis of Riemannian flow matching bounds sampling error by discretization and learning terms via curvature-aware differential inequalities. Explicit polynomial iteration complexities follow on hyperspheres and SPD manifolds.

Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 1/5
72%Highly rated
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Flow Perturbation++: Multi-Step Unbiased Jacobian Estimation for High-Dimensional Boltzmann Sampling

Flow Perturbation++ performs multi-step unbiased Jacobian estimation for continuous normalizing flows, reducing variance to improve high-dimensional Boltzmann sampling.

xinpeng peng, Ang Gao

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 8 of 20 reviewers recommend it
lenient 3/5
medium 5/10
strict 0/5
74%Highly rated
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gfnx: Fast and Scalable Library for Generative Flow Networks in JAX

gfnx is a JAX library for training and evaluating GFlowNets that achieves up to 80x speedups over PyTorch benchmarks across diverse tasks.

Daniil Tiapkin, Artem Agarkov, Nikita Morozov, Ian Maksimov and 3 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 4/10
strict 2/5
88%Must read
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Learning Visual Feature-Based World Models via Residual Latent Action

Residual Latent Action predicts visual feature dynamics via flow matching, outperforming diffusion world models with orders-of-magnitude faster inference and enabling offline robot learning from videos.

Xinyu Zhang, Zhengtong Xu, Yutian Tao, Yeping Wang and 2 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 47

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69%Highly rated
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Generative Modeling by Value-Driven Transport

A discrete-time stochastic control formulation yields value-driven transport policies that generate data via straight, fast, robust paths and support conditional generation and guidance.

Pablo Moreno-Muñoz, Adrian Müller, Gergely Neu

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 3 of 20 reviewers recommend it
lenient 2/5
medium 1/10
strict 0/5
78%Highly rated
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TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics

TracingFlow is a simulation-free flow matching framework using second-order dynamics and acceleration fields to infer continuous system evolution from sparse snapshots, achieving superior trajectory and distribution accuracy.

Yuhao Sun, Zekun Wu, Zixun Huang, Peijie Zhou

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Flow Matching from Viewpoint of Proximal Operators

Optimal transport conditional flow matching equals exact proximal operators via extended Brenier potentials without density assumptions, yields explicit vector fields, converges with batch size, and contracts exponentially normal to manifold-supported targets.

Kenji Fukumizu, Wei Huang, Han Bao, Shuntuo Xu and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 3/10
strict 2/5
74%Highly rated
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Coreset-Induced Conditional Velocity Flow Matching

CCVFM replaces isotropic noise with a coreset-derived Gaussian mixture source for hierarchical rectified flow, using a lightweight correction flow for residuals to achieve competitive few-step generation.

Xiao Wang, Zihua She, Jianxi Su

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 2/5
medium 6/10
strict 1/5
86%Must read
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Few-Step Cofolding with All-Atom Flow Maps

DeCAF distills all-atom biomolecular cofolding diffusion models into few-step flow maps with SE(3)-aligned endpoint losses, improving accuracy and physical validity at strict inference budgets.

Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam and 6 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 3/5
medium 10/10
strict 1/5
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