Generative Modeling

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39 papers in the last four weeks, up 144% on the four weeks before. 0.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 347

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E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation
Jun 1, 2026Lin Jiang, Dahai Yu, Ximiao Li +1Time Series Generation Generative Modeling
Florida State University
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
May 31, 2026Feifan Jiang, Yinan Bu, Shihao Wu +2Graph Generation Synthetic Data Generation
Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
A Unifying View of Variational Generative Wasserstein Flows
May 29, 2026Paul Caucheteux, Clément Bonet, Anna KorbaWasserstein Gradient Flows Generative Modeling
ENSAE, CREST, IP Paris · MAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
May 28, 2026Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger +2Constrained Generative Modeling Molecular Optimization
Department of Computer Science, ETH Zurich · ETH AI Center · NCCR Catalysis, Switzerland +1
Procedural Generation of First Person Shooter Maps using Map-Elites
May 28, 2026Simone de Donato, Pier Luca Lanzi, Daniele LoiaconoGenerative Modeling Procedural Content Generation
Politecnico di Milano — DEIB
Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future
May 28, 2026Pierre-André NoëlGenerative Modeling Diffusion Models
ServiceNow AI Research
Generative Models and Statistical Validation
May 28, 2026Sascha Diefenbacher, Sofia Palacios Schweitzer, Gregor KasieczkaGenerative Modeling Density Estimation
Institut für Theoretische Physik, Universität Heidelberg, Germany · Physics Division, Lawrence Berkeley National Laboratory, Berkeley, USA · NHETC, Department of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA +1
NeuROK: Generative 4D Neural Object Kinematics
May 28, 2026Chen Geng, Guangzhao He, Yue Gao +3Generative Modeling 3D Representation Learning
1Stanford University · 3Cornell University · University of Cambridge
DC-Motion: Decoupling Structure and Details via Discrete-Continuous Tokens for Human Motion Generation
May 28, 2026Hequan Wang, Xuean Chen, Jiaxu Zhang +2Text-to-Motion Generation Human Motion Generation
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China · Geophysical Exploration Brigade of Hubei Provincial Geological Bureau, Wuhan 430058, China · Information Systems Technology and Design Pillar, Singapore University of Technology and Design, 487372, Singapore
The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
May 28, 2026Tianhua ChenVariational Autoencoders Generative Modeling
School of Computing and Engineering[cs.LG] University of Huddersfield
AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling
May 28, 2026Yiheng Li, Zhuo Li, Ruibing Hou +4Cross-Modal Learning Unified Multimodal Models
Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China · Independent Author · University of Chinese Academy of Sciences, China
Conf-Gen: Conformal Uncertainty Quantification for Generative Models
May 27, 2026Gabriel Loaiza-Ganem, Kevin Zhang, Wei Cui +2Uncertainty Quantification Conformal Prediction
Layer 6 AI, Toronto, Canada
Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
May 27, 2026Julien Lalanne, David Picard, Lionel Boillot +3Flow Matching Uncertainty Quantification
Navier, CNRS, Univ Gustave Eiffel, ENPC, IP Paris, France · TotalEnergies, OneTech, France · LIGM, CNRS, Univ Gustave Eiffel, ENPC, IP Paris, France
Parameter-Efficient Generative Modeling with Controlled Vector Fields
May 27, 2026Peyman MortezaGenerative Modeling Continuous Normalizing Flows
Sampling Triangulations and Calabi-Yau Threefolds with Autoregressive GNNs
May 26, 2026Nate MacFaddenGraph Neural Networks Generative Modeling
Department of Physics, Cornell University, Ithaca, NY 14853 USA
Balancing Fidelity and Diversity in Diffusion Models via Symmetric Attention Decomposition: Hopfield Perspective
May 26, 2026Hyunmin Cho, Woo Kyoung Han, Kyong Hwan JinSelf-Attention Transformer Attention
Department of Electrical Engineering, Korea University, Seoul, South Korea.
Geometric Flow Matching for Molecular Conformation Generation via Manifold Decomposition
May 25, 2026Yunqing Liu, Yi Zhou, Wenqi FanFlow Matching Generative Modeling
The Hong Kong Polytechnic University Hong Kong, China
Geometry-Aware Image Flow Matching
May 24, 2026Junho Lee, Kwanseok Kim, Joonseok LeeFlow Matching Image Generation
1Seoul National University, Seoul, Korea.
A computational phase transition for learning-to-sample from Ising models
May 23, 2026Andrej Risteski, Thuy-Duong VuongPhase Transitions Ising Model
Machine Learning Department, Carnegie Mellon University. · Department of Computer Science and Engineering, UC San Diego.
TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
May 22, 2026Arseny Ivanov, Sergei Kholkin, Vladislav Gromadskii +3Masked Diffusion Models Autoregressive Language Modeling
AXXX, Russia · Applied AI Institute, Moscow, Russia · MIRAI, Russia
Leveraging Foundation Models for Causal Generative Modeling
May 22, 2026Aneesh Komanduri, Xintao WuCausal Counterfactual Generation Image Generation
University of Arkansas Fayetteville, Arkansas, USA
LLM-driven design of physics-constrained constitutive models: two agents are better than one
May 22, 2026Marius Tacke, Matthias Busch, Kian Abdolazizi +4Neural Surrogate Modeling Multi-Agent LLM Systems
1Helmholtz-Zentrum Hereon, Geesthacht, Germany · 2Hamburg University of Technology, Hamburg, Germany · 3RWTH Aachen University, Aachen, Germany +2
Valid and Expressive Copulas for Irregular Multivariate Time Series
May 22, 2026Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1Irregular Time-Series Modeling Copula Models
Institute of Computer Science University of Hildesheim Hildesheim, Germany
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
May 22, 2026Hoyun Choi, Junghyo Jo, Deok-Sun LeeGraph Rewiring Graph Generation
School of Computational Sciences, Korea Institute for Advanced Study, 85, Hoegi-ro, 02455, Seoul, Korea. · Department of Physics Education, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. · Center for Theoretical Physics and Artificial Intelligence Institute, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. +1
Generative Modeling by Value-Driven Transport
May 21, 2026Pablo Moreno-Muñoz, Adrian Müller, Gergely NeuValue Function Estimation Stochastic Optimal Control
Universitat Pompeu Fabra · Barcelona, Spain · ETH Zürich +2
Memorisation, convergence and generalisation in generative models
May 20, 2026Antoine Maillard, Sebastian GoldtNeural Network Memorization Memorization in Generative Models
1INRIA Paris & DI ENS, PSL University, Paris, France · 2International School of Advanced Studies (SISSA), Trieste, Italy
Latent Process Generator Matching
May 19, 2026Lukas Billera, Hedwig Nora Nordlinder, Ben MurrellGenerative Modeling
Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
When Does Model Collapse Occur in Structured Interactive Learning?
May 19, 2026Yuchen Wu, Kangjie Zhou, Weijie SuModel Collapse Generative Modeling
School of Operations Research and Information Engineering, Cornell University · Department of Statistics, Columbia University · Department of Statistics and Data Science, University of Pennsylvania
Probability-Conserving Flow Guidance
May 19, 2026Parsa Esmati, Junha Hyung, Amirhossein Dadashzadeh +2Diffusion Model Guidance Classifier-Free Guidance
University of Bristol · KAIST
Tail Annealing for Heavy-Tailed Flow Matching
May 19, 2026Jean PachebatFlow Matching Generative Modeling
1CMAP, Ecole Polytechnique, Institut Polytechnique de Paris, France.

Jun 1, 2026, cs.LGJ/K move · Enter open · S save

E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation

Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang

Florida State University

Time Series Generation Generative Modeling Diffusion Models

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Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall distributional fidelity while failing to faithfully capture extreme events. To advance existing research, we propose E4GEN, an explainable diffusion framework for extreme event-aware time-series generation. E4GEN provides systematic insights into when, what, and how to control extreme-event generation through three key components. First, E-Activator learns the dataset-adaptive extreme-control signal activation step during the denoising process without interfering with regular temporal components, including trend and seasonality. Second, E-Predictor determines what control signal to enforce through Self-Driven Semantic Prediction, where each sample derives its own control signal by inferring latent extreme-event information during generation. It also includes a novel Data-Conditioned Training, Noise-Initiated Sampling mechanism to address the issue of unavailable training labels. Third, E-Control specifies how to control extreme-event generation through a trainable Extreme Control Network, which transforms the semantic control signal into layer-wise signals and injects it into the denoising process. We evaluate E4GEN on six datasets with 17 metrics, and extensive experiments show that E4GEN outperforms state-of-the-art models across multiple dimensions, including overall fidelity, extreme-event fidelity, and downstream utility.

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Latest papers 347