Synthetic Data Generation

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Synthetic data generation studies how to create artificial examples that reproduce useful features of real data or provide exact, controllable labels. Common approaches include procedural simulators and domain randomization, generative models such as GANs, variational autoencoders and diffusion models, and large language models or agents that synthesize text, dialogues, and tool-use trajectories. These methods are used to address scarce or sensitive data, cover rare cases, and build controlled training and evaluation sets, with quality often measured by fidelity, privacy, downstream performance, and transfer to real benchmarks. A central open tension is that realism or strong benchmark scores do not guarantee representative, unbiased, causally valid data or reliable performance in real settings.

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629 papers69 papers in the last four weeks, up 1% on the four weeks before. 0.4% of all new papers.

EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
Mar 13, 2026En-Ya Kuo, Sebastien MotschSynthetic Data Generation Synthetic Tabular Data Generation
Arizona State University – School of Mathematical and Statistical Sciences, Tempe, AZ, USA
Feynman: Knowledge-Infused Diagramming Agent for Scalable Visual Designs
Mar 13, 2026Zixin Wen, Yifu Cai, Kyle Lee +5Synthetic Data Generation Tool-Using Vision-Language Agents
Carnegie Mellon University Pittsburgh, PA 15213, USA
SciMDR: Advancing Scientific Multimodal Document Reasoning
Mar 12, 2026Ziyu Chen, Yilun Zhao, Chengye Wang +3Multimodal Reasoning Multimodal QA
University of Chicago · Yale University · TCS Research
PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
Mar 12, 2026Minjia Wang, Yunfeng Wang, Xiao Ma +9Synthetic Data Generation LLM Agent Trajectory Synthesis
Apple · Harvard University
Semi-Synthetic Parallel Data for Translation Quality Estimation: A Case Study of Dataset Building for an Under-Resourced Language Pair
Mar 12, 2026Assaf Siani, Anna Kernerman, Ilan KernermanMT Evaluation Synthetic Data Generation
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
Mar 11, 2026Ziwei Wang, Zhentao He, Xingyi He +6Synthetic Data Generation Brain-Computer Interfaces
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China · Zhongguancun Academy, Beijing, China
SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
Mar 11, 2026Andrea Lampis, Michela Carlotta Massi, Nicola Pirastu +3Synthetic Data Generation Genomics
DEIB, Politecnico di Milano, Milan 20133, Italy · Health Data Science Centre, Human Technopole, Milan 20157, Italy · Genomics Research Centre, Human Technopole, Milan 20157, Italy +2
Interventional Time Series Priors for Causal Foundation Models
Mar 11, 2026Dennis Thumm, Ying ChenCausal Foundation Models Synthetic Data Generation
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore
PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling
Mar 11, 2026Nicholas I-Hsien Kuo, Marzia Hoque Tania, Blanca Gallego +1Medical Education Synthetic Data Generation
Centre for Big Data Research in Health (CBDRH), The University of New South Wales, Sydney, Australia
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
Mar 10, 2026Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin +1Tabular Foundation Models Synthetic Data Generation
Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy · InSilicoTrials Technologies, Riva Grumula 2, 34123 Trieste, Italy · Department of Engineering and Architecture, University of Trieste, Trieste, Italy +3
Variance-Corrected Multi-Asset Equity Simulation with Hybrid Hidden Markov Marginals
Mar 10, 2026Abdulrahman Alswaidan, Jeffrey D. VarnerQuantitative Finance Synthetic Data Generation
Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850
SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans
Mar 9, 2026Hansi Zeng, Zoey Li, Yifan Gao +7Synthetic Data Generation for LLM Agents LLM Agent Training
University of Massachusetts Amherst · Amazon · University of Montreal +2
Evaluating Synthetic Data for Baggage Trolley Detection in Airport Logistics
Mar 8, 2026Abdeldjalil Taibi, Mohmoud Badlis, Amina Bensalem +2Synthetic Data Evaluation Object Detection
The National School of Artificial Intelligence (ENSIA), Algiers 16000, Algeria · Information Systems and Development Department, Algiers Airport, Algiers 16000, Algeria
Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations
Mar 8, 2026Robin Doerfler, Lonce WyseSynthetic Data Generation Audio Generation
Audio Research & Development, Impulse Audio Lab GmbH, Munich, Germany · Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
Resource-Adaptive Federated Text Generation with Differential Privacy
Mar 7, 2026Jiayi Wang, John Gounley, Heidi HansonDifferentially Private Federated Learning Synthetic Data Generation for Language Models
Oak Ridge National Laboratory Oak Ridge, TN, USA
Physics-Informed Diffusion Model for Generating Synthetic Extreme Rare Weather Events Data
Mar 6, 2026Marawan Yakout, Tannistha Maiti, Monira Majhabeen +1Synthetic Data Generation Synthetic Data Augmentation
Department of Computer Science, University of London, London · Deepkapha AI Lab, Assen, The Netherlands
xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
Mar 6, 2026Gregor BaerSynthetic Benchmark Generation Interpretable Time-Series Classification
Information Systems, Industrial Engineering & Innovation Sciences Eindhoven University of Technology Eindhoven, The Netherlands
From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories
Mar 6, 2026Guanglin Zhou, Armin Catic, Motahare Shabestari +4Synthetic Data Generation Synthetic Clinical Data Generation
The University of Queensland, Brisbane, QLD, Australia · The University of New South Wales, Sydney, NSW, Australia · Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Iran +1
Harnessing Synthetic Data from Generative AI for Statistical Inference
Mar 5, 2026Ahmad Abdel-Azim, Ruoyu Wang, Xihong LinSynthetic Data Generation Synthetic Data Evaluation
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA · Harvard University, Boston, Massachusetts 02115, USA
FairFinGAN: Fairness-aware Synthetic Financial Data Generation
Mar 5, 2026Tai Le Quy, Dung Nguyen Tuan, Trung Nguyen Thanh +3Synthetic Data Generation Synthetic Tabular Data Generation
University of Koblenz, Germany · Hanoi University of Science and Technology, Vietnam · Banking Academy of Vietnam, Vietnam +1
KARL: Knowledge Agents via Reinforcement Learning
Mar 5, 2026Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal +23Synthetic Data Generation for LLM Agents LLM Agent Training
Databricks AI Research
ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI
Mar 5, 2026Jens Lehmann, Syeda Khushbakht, Nikoo Salehfard +4ARC-AGI Synthetic Data Generation
Dresden University of Technology Dresden, Germany · Amazon · TIB - Leibniz Information Centre Hanover, Germany +1
WebFactory: Automated Compression of Foundational Language Intelligence into Grounded Web Agents
Mar 5, 2026Sicheng Fan, Qingyun Shi, Shengze Xu +5Web Agents LLM Agent Training
Fudan University · IMean AI · The Chinese University of Hong Kong +1
Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning
Mar 5, 2026Jaekyun Ko, Dongjin Kim, Soomin Lee +2Synthetic Data Generation Diffusion-Based Image Generation
Department of Computer Science, Hanyang University · Mobile Experience (MX) Division, Samsung Electronics · Department of Computer Science, Toronto Metropolitan University
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
Mar 4, 2026Valentin Biller, Niklas Bubeck, Lucas Zimmer +6Medical Image Synthesis Synthetic Data Generation
Technical University of Munich (TUM), Germany · Munich Center for Machine Learning (MCML) · Chair for AI in Healthcare and Medicine, TUM and TUM University Hospital, Munich, Germany +3
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
Mar 4, 2026Yanbo Wang, Jiaxuan You, Chuan Shi +1Relational ICL Relational Foundation Models
Institute for Artificial Intelligence, Peking University · University of Illinois at Urbana-Champaign · Institute of Computing Technology, Beijing University of Post and Telecommunication +1
Controllable Generative Sandbox for Causal Inference
Mar 3, 2026Qi Zhang, Harsh Parikh, Ashley Naimi +3Synthetic Data Generation Causal Inference
Emory University · Yale University · Amgen
Synthetic-Child: An AIGC-Based Synthetic Data Pipeline for Privacy-Preserving Child Posture Estimation
Mar 3, 2026Taowen ZengHuman Pose Estimation Synthetic Data Generation
Independent Researcher
Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance
Mar 2, 2026Yiqi Lin, Guoqiang Liang, Ziyun Zeng +3Video Editing Instruction-Based Video Editing
Show Lab, National University of Singapore
CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
Mar 2, 2026Amir Asiaee, Zhuohui J. Liang, Chao YanSynthetic Data Generation Synthetic Tabular Data Generation
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA · Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Mar 13, 2026J/K move · Enter open · S save

EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

En-Ya Kuo, Sebastien Motsch

Arizona State University – School of Mathematical and Statistical Sciences, Tempe, AZ, USA

Synthetic Data Generation Synthetic Tabular Data Generation Tabular Diffusion Models Financial Fraud Detection Diffusion Transformer

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Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used to mitigate this problem. In this work, we propose the Clustered Embedding Diffusion-Transformer (EmDT), a diffusion model designed to generate fraudulent samples. Our key innovation is to leverage UMAP clustering to identify distinct fraudulent patterns, and train a Transformer denoising network with sinusoidal positional embeddings to capture feature relationships throughout the diffusion process. Once the synthetic data has been generated, we employ a standard decision-tree-based classifier (e.g., XGBoost) for classification, as this type of model remains better suited to tabular datasets. Experiments on a credit card fraud detection dataset demonstrate that EmDT significantly improves downstream classification performance compared to existing oversampling and generative methods, while maintaining comparable privacy protection and preserving feature correlations present in the original data.