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论文提出 CD-FFM，一种基于似然函数流匹配（likelihood functional flow matching）的生成式气候降尺度模型，可将再分析数据降尺度至1公里分辨率，在芬诺斯坎迪亚日气温数据上优于回归、神经算子和扩散模型，且采样速度极快并能给出精确似然。
该论文提出 Normalizing Flow Maps（NFM），一种可精确求逆的推前映射类模型，统一了 normalizing flows 与 flow maps，支持高效的精确似然评估与少步生成。在 CIFAR-10、ImageNet-64 条件图像生成和 Boltzmann Generators 平衡采样任务上达到了参数量匹配条件下 normalizing flows 的最先进水平。
这篇论文将事件序列预测重新表述为条件生成建模问题，提出了一个同时衡量预测准确性和分布真实性的基准，并引入Wasserstein BaryBooster算法，将扩散模型的真实感转化为预测精度。
论文提出一种面向路网轨迹生成的图约束生成范式 Anticipatory Generative Modeling，并提出模型 Foresight，通过"anticipate–select–update–re-anticipate"循环解决路口转移选择问题，超越扩散模型等基线方法。
该论文引入了一种以神经网络复杂度（Barron能量）为核心的最优传输框架，替代传统L²动能，从而在概率测度空间上定义新的几何结构，并证明了扩散生成模型在Barron几何下的次优性（超多项式score逼近下界）。
提出GenST框架，利用微调后的大语言模型从节点描述中提取语义特征，结合时空VAE与生成式Transformer（GenT），在零样本条件下预测传感器网络中未观测节点的未来状态，显著优于现有基线。
Latent-Foresight proposes an end-to-end framework that jointly learns a latent tokenizer and a flow-based generative dynamics model, explicitly structuring the latent space for temporal predictability and outperforming two-stage pipelines on future scene understanding tasks.
The paper proposes ReLOBGen, a limit order book message generator that ensures replayability by construction by selecting referenced orders from the current resting book and masking invalid tokens, achieving 100% replayability in 500-message rollouts and a 2.7-3.6x speedup per replayed message over the LOBS5 baseline.
The paper introduces T-RoPE, a time-aware modification to Rotary Position Embedding for sequential recommendation systems, demonstrating significant performance improvements on benchmarks and real-world impact in e-commerce.
This paper introduces Riemannian Wasserstein Entropic Flow Matching (RWEFM), a generative framework for modeling probability distributions on Riemannian manifolds, with applications in scientific domains like single-cell biology and protein conformations.
The paper provides a unified probabilistic framework for large language models, describing them through probability measures, training via maximum-likelihood estimation, and text generation as stochastic simulation, with insights into phenomena like hallucination and the role of diffusion models.
This paper introduces a continuous-time generative dynamics framework that leverages score-based models to generate data at arbitrary timestamps on learned data manifolds, with applications to video and scientific data.
The paper presents VIOT, a variational incompressible optimal transport operator that uses a Fourier Neural Operator to predict divergence-free velocity fields for efficient incompressible density transport, achieving orders of magnitude speedup over traditional optimization methods.
The paper proposes Representation-based Masked Diffusion Model (RMDM), which leverages text representations to improve parallel token updates in masked diffusion models, enhancing generation quality especially in few-step sampling.
The paper proposes CoMA-DiT, a bidirectional cross-modal Diffusion Transformer for latent augmentation in multimodal brain state decoding, which enhances performance in tasks like auditory attention decoding and emotion recognition by using paired modalities as mutual supervisory signals.
This paper identifies a duality between continuous and discrete flow matching, showing that projecting continuous convex-interpolant paths via argmax yields discrete flows, and explores how different source geometries affect transition timing and generation quality.
This paper introduces Newton Matching, a unified framework for fine-tuning and sampling in generative models, which addresses limitations of existing methods by treating learning as an iterative optimization process and leveraging conditional-matching structure.
This paper proposes CAT-OV and CAT-OT, two lightweight, training-free algorithms that adapt step-sizes in Flow Matching sampling based on curvature, improving image quality and reducing generation steps by up to 40%.
PathGuide reformulates classifier-free guidance selection as an on-policy transport problem in flow-based generative models, using the weak form of the continuity equation to dynamically optimize guidance scales for improved sample fidelity.
The article presents BFN-RL, a unified generative modeling framework for offline reinforcement learning based on Bayesian Flow Networks, capable of generating effective trajectories across discrete and continuous state spaces.