Optimizing complex systems, ranging from LLM prompts to multi-turn agents, traditionally requires labor-intensive manual iteration. We formalize this challenge as a stochastic generative optimization problem where a generative language model acts as the optimizer, guided by numerical rewards and text feedback to discover the best system. We introduce Prioritized Optimization with Local Contextual Aggregation (POLCA), a scalable framework designed to handle stochasticity in optimization -- such as noisy feedback, sampling minibatches, and stochastic system behaviors -- while effectively managing the unconstrained expansion of solution space. POLCA maintains a priority queue to manage the exploration-exploitation tradeoff, systematically tracking candidate solutions and their evaluation histories. To enhance efficiency, we integrate an $\varepsilon$-Net mechanism to maintain parameter diversity and an LLM Summarizer to perform meta-learning across historical trials. We theoretically prove that POLCA converges to near-optimal candidate solutions under stochasticity. We evaluate our framework on diverse benchmarks, including $τ$-bench, HotpotQA (agent optimization), VeriBench (code translation) and KernelBench (CUDA kernel generation). Experimental results demonstrate that POLCA achieves robust, sample and time-efficient performance, consistently outperforming state-of-the-art algorithms in both deterministic and stochastic problems. The codebase for this work is publicly available at https://github.com/rlx-lab/POLCA.
Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
Laboratory for Big Data and Decision（大数据与决策实验室）；National University of Defense Technology（国防科技大学）；Samsung AI Center Cambridge（三星AI研究中心）；Queen Mary University of London（伦敦玛丽女王大学）；Fudan University（复旦大学）；ShanghaiTech University（上海科技大学）；Shanghai Innovation Institute（上海创新研究院）
Benchmarking Stochastic Interpolants for Modeling Physical Systems
Department of Mechanical Engineering（机械工程系）；Carnegie Mellon University（卡内基梅隆大学）；Department of Geophysical Sciences Committee on Computational and Applied Mathematics（地球科学系计算与应用数学委员会）；University of Chicago（芝加哥大学）；LMD/IPSL, CNRS, ENS, Université PSL École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université（LMD/IPSL、CNRS、ENS、Université PSL 工程学院、巴黎理工学院、索邦大学）；Réseau de Transport d’Électricité (RTE)（电力传输网络（RTE））
Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations
Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University（机器学习与神经计算系，多纳德斯脑认知行为研究所，拉德堡德大学）
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
Department of Biology, University of Copenhagen（哥本哈根大学生物系）；Department of Applied Mathematics and Computer Science, Technical University of Denmark（丹麦技术大学应用数学与计算机科学系）；Google DeepMind（谷歌DeepMind）；Technical University of Berlin (TU Berlin)（柏林工业大学）；Technical University of Munich（慕尼黑工业大学）
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Stochastic Parameterization of Column Physics using Generative Adversarial Networks
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
我们展示了使用概率机器学习技术来开发大气柱物理的随机参数化。在对NASA的Modern-Era Retrospective analysis for Research and Applications，version 2（MERRA 2）数据进行适当的预处理以最大限度地减少MERRA 2估计垂直速度的高频，高波数分量的影响之后，我们使用生成对抗网络来学习非绝热源垂直剖面的概率分布。这可以被看作是对以前类似但确定性的方法的改进，这些方法试图减轻人类设计的物理参数化的缺点和气候模式中“物理”步骤的计算需求。
Generative Modeling via Kernelized Stochastic Interpolants
CCM, Flatiron Institute, New York, USA（CCM，Flatiron研究所，纽约，美国）；Courant Institute of Mathematical Sciences, New York University, New York, USA（数学科学学院，纽约大学，纽约，美国）
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Amsterdam（阿姆斯特丹大学）；Delft University of Technology（代尔夫特理工大学）；Centrum Wiskunde & Informatica（数学与计算机科学中心）
Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
Department of Physics, University of Genova（基因瓦大学物理系）；Department of Materials Science, University of Milano-Bicocca（米兰-比科卡大学材料科学系）；Institut Lumière Matière, UMR5306 Université Lyon 1—CNRS（光与物质研究所，里昂1大学-CNRS）
Multimarginal generative modeling with stochastic interpolants
Center for Cosmology and Particle Physics（宇宙学与粒子物理中心）；New York University（纽约大学）；Courant Institute of Mathematical Sciences（库朗数学科学研究所）；University of California, Berkeley（加州大学伯克利分校）
SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion
School of Resources and Environmental Engineering, Hefei University of Technology（资源与环境工程学院，合肥工业大学）；School of Computing and Data Science, University of Hong Kong（计算与数据科学学院，香港大学）
