Synthetic Data Generation

Follow this topic Ask about this topic

Momentum

43 papers in the last four weeks, up 59% on the four weeks before. 0.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 417

All topics

Template-as-Ontology: Configurable Synthetic Data Infrastructure for Cross-Domain Manufacturing AI Validation
May 11, 2026Grama ChethanManufacturing Synthetic Data Generation
Siemens Digital Industries Software, Plano, TX 75024, United States
Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training
May 11, 2026Daniel Dratschuk, Paul SwobodaOptical Music Recognition Synthetic Data Generation
Heinrich Heine University Düsseldorf, Germany
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Synthetic Data Generation One-Step Generative Modeling
AMLab · University of Amsterdam · CuspAI +1
diffGHOST: Diffusion based Generative Hedged Oblivious Synthetic Trajectories
May 11, 2026Florent Guépin, Cheick Tidiani Cisse, Denis Renaud +2Trajectory Generation Synthetic Data Generation
Orange Research, Belfort, France · Orange Business, Belfort, France
VISTA: A Generative Egocentric Video Framework for Daily Assistance
May 11, 2026Yu-Hsiang Liu, Yu-Chien Tang, An-Zi YenSynthetic Data Generation AI Agent Benchmarks
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan
Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
May 11, 2026Shiqiang Wang, Herbert Woisetschläger, Hans Arno Jacobsen +1Synthetic Data Generation LLM Training
Department of Computer Science, University of Exeter, UK · Technical University of Munich, Germany · Department of Electrical and Computer Engineering, University of Toronto, Canada +1
Building Korean linguistic resource for NLU data generation of banking app CS dialog system
May 11, 2026Jeongwoo Yoon, On-yu Park, Changhoe Hwang +3Synthetic Data Generation Intent Classification
DICORA, Hankuk University of Foreign Studies · Université Gustave Eiffel
TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
May 11, 2026Wilson Wongso, Lihuan Li, Arian Prabowo +4Trajectory Generation Synthetic Data Generation
University of New South Wales · Hong Kong University of Science and Technology (Guangzhou)
PREPING: Building Agent Memory without Tasks
May 11, 2026Yumin Choi, Sangwoo Park, Minki Kang +2Agent Memory Synthetic Data Generation
KAIST
Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents
May 11, 2026Roben Delos Reyes, Daniel Capurro, Nicholas GeardDistribution Shift Robustness Synthetic Data Generation
School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia · Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
An Information-Theoretic Criterion for Efficient Data Synthesis
May 11, 2026Hanyu Li, Zhengqi Sun, Xiaotie DengSynthetic Data Pretraining Synthetic Data Generation
CFCS, School of Computer Science, Peking University, Beijing, China · Department of Information Management, Peking University, Beijing, China
Concordia: Self-Improving Synthetic Tables for Federated LLMs
May 11, 2026Jimin Huang, Duanyu Feng, Nuo Chen +8LLM Fine-Tuning Synthetic Data Generation
University of Manchester · The Fin AI · National University of Singapore +5
A Real-Calibrated Synthetic-First Data Engine
May 10, 2026Yukang Shen, Zhiguo Liu, Yingshu Li +1Synthetic Data Augmentation Synthetic Data Generation
Kennesaw State University, Marietta, GA, USA. · Department of Computer Science, Georgia State University, Atlanta, GA, USA.
GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
May 10, 2026Jinhao Jing, Zheng Ma, Jinwei Liang +9Mathematical Reasoning Benchmarks Synthetic Data Generation
SenseTime Research · The Chinese University of Hong Kong, Shenzhen · University of California, Los Angeles +3
LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection
May 10, 2026Darren Zhu, Daren LerSynthetic Data Augmentation Algorithm Selection
Department of Statistics and Data Science National University of Singapore · Department of Computer Science National University of Singapore
CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
May 9, 2026Nicolás Astorga, Anita Kriz, Mihaela van der SchaarStructural Causal Models Knowledge Augmentation for Language Models
DAMTP, University of Cambridge, Cambridge, UK
Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models
May 8, 2026Aswin RRV, Jacob Dineen, Divij Handa +4Synthetic Data Generation RL for Language Model Reasoning
Arizona State University · Google Cloud AI Research · Google DeepMind
From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data
May 8, 2026Yue Yu, Jiayu Wang, Jiajia Shi +2Synthetic-to-Real Domain Adaptation Image-to-Image Translation
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200433, China · Institute of Trustworthy Embodied AI, Fudan University, Shanghai 200433, China
Flow Matching for Count Data
May 8, 2026Ganchao Wei, John PearsonFlow Matching Synthetic Data Generation
Department of Neurobiology Department of Statistical Science Duke University Durham, NC, USA · Department of Neurobiology Department of Electrical and Computer Engineering Duke University Durham, NC, USA
Generating training datasets for legal chatbots in Korean
May 8, 2026Changhoe Hwang, Jee-Sun Nam, Eric LaporteLegal NLP Synthetic Data Generation
Hankuk University of Foreign Studies, DICORA · Univ Gustave Eiffel, CNRS, LIGM
Target-Aware Data Augmentation for SAT Prediction
May 7, 2026Eshed Gal, Uri Ascher, Eldad HaberSynthetic Data Augmentation Graph Neural Networks
Department of Computer Science The University of British Columbia Vancouver, Canada · Department of Earth, Ocean and Atmospheric Sciences The University of British Columbia Vancouver, Canada
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
May 7, 2026Hugo Cazaux, Eyjólfur Ingi Ásgeirsson, Hlynur StefánssonSynthetic Data Augmentation Synthetic Data Generation
Department of Engineering Reykjavík University Iceland, Menntavegur 1, 102
Spherical Flows for Sampling Categorical Data
May 7, 2026Jannis Chemseddine, Gregor Kornhardt, Gabriele SteidlFlow Matching Synthetic Data Generation
Technische Universität Berlin
When2Speak: A Dataset for Temporal Participation and Turn-Taking in Multi-Party Conversations for Large Language Models
May 7, 2026Vihaan Nama, Shreya Mendi, Zian Ye +1Synthetic Data Generation Synthetic Data Generation for Language Models
Pratt School of Engineering Duke University
Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
May 6, 2026Yllias Chali, Deen AbdullahSynthetic Data Generation Text Summarization
University of Lethbridge Alberta, Canada
Syn4D: A Multiview Synthetic 4D Dataset
May 6, 2026Zeren Jiang, Yushi Lan, Yihang Luo +83D Point Tracking 4D Reconstruction
VGG, University of Oxford · Nanyang Technological University · Naver Labs Europe
Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems
May 5, 2026Yilun Zhao, Jinbiao Wei, Tingyu Song +3Agentic Search Synthetic Data Generation
Yale NLP Lab · National University of Singapore · NYU Shanghai
From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving
May 3, 2026Ali Nouri, Yifei Zhang, Yifan Zhang +53D Gaussian Splatting 3D Scene Editing
Volvo Cars, Gothenburg, Sweden · Zenseact AB, Gothenburg, Sweden · Lund University, Lund, Sweden +1
Skipping the Zeros in Diffusion Models for Sparse Data Generation
May 3, 2026Phil Sidney Ostheimer, Mayank Nagda, Andriy Balinskyy +6Synthetic Data Generation Diffusion Model Acceleration
1RPTU University Kaiserslautern-Landau · 2Heidelberg Univer · University of California, Irvine.
DataEvolver: Let Your Data Build and Improve Itself via Goal-Driven Loop Agents
May 3, 2026Qisong Zhang, Wenzhuo Wu, Zhuangzhuang Jia +7Synthetic Data Augmentation Synthetic Data Generation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications · Institute of Artificial Intelligence · Institute of Artificial Intelligence (TeleAI), China Telecom

May 11, 2026, cs.AIJ/K move · Enter open · S save

Template-as-Ontology: Configurable Synthetic Data Infrastructure for Cross-Domain Manufacturing AI Validation

Grama Chethan

Siemens Digital Industries Software, Plano, TX 75024, United States

Manufacturing Synthetic Data Generation Synthetic Data Evaluation

Open paper AskPDF arXiv

LLarge language model (LLM)-based AI agents deployed in manufacturing environments require populated, schema-correct data for validation, yet production MES data is proprietary, privacy-encumbered, and vendor-specific. This paper introduces the Template-as-Ontology principle: a single Python configuration module (700-770 lines, 45 validated exports) serves simultaneously as the specification for a time-stepped manufacturing simulator and as the runtime domain schema for AI analytics tools, producing alignment by construction rather than integration. We formally define the domain template as a typed relational configuration schema and prove that structural alignment between simulation and tool layers is guaranteed by single-source consumption. A five-layer pipeline--simulation, PostgreSQL, CDC/Iceberg lakehouse, star schema, and 12 parameterized AI tools--generates causally coherent, MES-shaped data spanning 66 entity types across four operational domains mapped to ISA-95/IEC 62264. We validate the architecture with six industry templates (aerospace, pharma, automotive, electronics, beverages, warehousing) running on identical framework code. Calibration experiments (60 runs, 10 seeds per template) confirm parametric controllability: observed KPIs fall within configured ranges across all templates. A controlled hallucination experiment (72 tool invocations, Qwen3-32B) demonstrates that ontology-constrained parameters eliminate tool-parameter fabrication (0% constrained vs. 43% unconstrained hallucination rate for the evaluated model, Fisher's exact test p < 10^-12); the 0% constrained rate is an architectural guarantee that holds for any model. The framework provides a reusable data layer for discrete manufacturing AI validation.

Momentum

Related topics

Latest papers 417