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

Data Evolution by Wittgenstein's Rule Following
Jun 21, 2026Aydin Ghojogh, Benyamin GhojoghSynthetic Data Generation
Generating Public Health Responses using Survey-Augmented Large Language Models
Jun 20, 2026Leonardo Marciaga, Thuyen Pham, Julia Rezvani +4Synthetic Data Augmentation Synthetic Data Generation
Illinois Institute of Technology · University of Massachusetts Amherst · Portland State University +4
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Jun 19, 2026Franco Martino O'Rourke, Ana Trisovic, Dimitris BertsimasStructural Causal Models Multivariate Time Series Classification
Massachusetts Institute of Technology, Cambridge, MA, USA.
OSOG: A Differentiable, Physics-Informed Synthetic Data Engine for Micro-Optical Environments
Jun 19, 2026Caio SilvaDifferentiable Rendering Synthetic Data Generation
Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA∗
Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition
Jun 19, 2026Raphaël Bagat, Zhe Zhang, Junichi Yamagishi +2Synthetic Data Generation Speech Processing
Universit´e de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France · National Institute of Informatics, Tokyo, Japan
Scaling Diverse Language Generation for 3D Visual Grounding
Jun 18, 2026Austin T. Wang, Dongchen Yang, Angel X. ChangSynthetic Data Generation Text Generation
Simon Fraser University
Synthetic Network Packet Generation through Statistical Learning and Genetic Algorithms
Jun 18, 2026Mayank Raj, Nathaniel D. Bastian, Lance Fiondella +1Synthetic Data Generation Synthetic Tabular Data Generation
University of Massachusetts Dartmouth Dartmouth, MA USA · United States Military Academy West Point, NY USA
The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Jun 18, 2026Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam +1Synthetic Data Generation Controllable Text Generation
University of Amsterdam, Amsterdam, The Netherlands · Booking.com, Amsterdam, The Netherlands
Critical Percolation as a Synthetic Data Model for Interpretability
Jun 18, 2026Aryeh Brill, Tom Ingebretsen CarlsonSynthetic Data Generation Neural Network Interpretability
Principles of Intelligence
One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception
Jun 18, 2026Keqin Zeng, Shuting Su, Shihao Lin +2Synthetic Data Augmentation World Models
Tsinghua University · SenseTime Research · Sun Yat-Sen University +1
Source-Grounded Data Generation for Text-to-JSON Learning
Jun 18, 2026Sunghee Ahn, Guijin Son, Youngjae YuGrounded Text Generation Synthetic Data Generation
1Seoul National University
RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents
Jun 17, 2026Ruishan Fang, Siyuan Lu, Chenyi Zhuang +1Synthetic Data Generation Sample-Efficient RL
Inclusion AI, Ant Group · Zhejiang University · Westlake University +1
Semantic Flip: Synthetic OOD Generation for Robust Refusal in Embodied Question Answering and Spatial Localization
Jun 15, 2026Dongbin Na, Chanwoo Kim, Giyun Choi +1Synthetic Data Generation Embodied QA
RGA Inc.
SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
Jun 15, 2026Wenjie Wang, Yue Huang, Zhengqing Yuan +6LLM Alignment Synthetic Data Generation
University of Notre Dame · Carnegie Mellon University · LMU Munich +1
Topological Flow Matching
Jun 14, 2026Kacper Wyrwal, İsmail İlkan Ceylan, Alexander TongFlow Matching Synthetic Data Generation
University of Oxford · TU Wien · AITHYRA
Informative Missingness to Generate Irregular Clinical Time Series
Jun 14, 2026Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora +4Synthetic Data Generation Time Series Generation
Aalborg University, Aalborg, Denmark · University of Pavia, Pavia, Italy · Bowling Green State University, Bowling Green, Ohio, USA
OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model
Jun 14, 2026Jiali Wen, Hongxia Gao, Litao Li +4Visual Question Answering Vision-Language Models
2South China University of Technology · 1Xi’an Jiaotong University · 3Shenzhen Loop Area Institute +1
Open Annotations and Synthetic Data for Field Localisation in Indian Bank Cheques
Jun 14, 2026Jaganadh GopinadhanSynthetic Data Augmentation Synthetic Data Generation
A Qualitative Review of GenAI-Based Methods for Data Generation and Augmentation in Industrial Computer Vision Applications
Jun 12, 2026Paul Koch, Paul Hofmann, Ferdinand Waßelewsky +3Synthetic Data Augmentation Synthetic Data Generation
Fraunhofer IPK pascalstraße 8-9, Berlin,Germany · Hamburg University of Applied Sciences (HAW Hamburg) Stiftstraße 69, Hamburg, Germany · Technical University Berlin (Tu-Berlin)Jun pascalstraße 8-9, Berlin, Germany
VeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
Jun 12, 2026Xiaoxian Duan, Zequn Liu, Yingce XiaSynthetic Data Generation Multimodal Reasoning
Institute of Automation, Chinese Academy of Sciences, Beijing, China · Zhongguancun Academy, Beijing, China
Disparate Impact in Synthetic Data Generation
Jun 11, 2026Paul Andrey, Michaël Perrot, Batiste Le Bars +1Synthetic Data Generation Algorithmic Fairness
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStal, F-59000 Lille, France
A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction
Jun 11, 2026John Fields, K M Sajjadul Islam, Ruchitha Thota +2Synthetic Data Generation AI in Education
Business Analytics Concordia University Wisconsin · Computer Science Marquette University · Computer Science Concordia University Wisconsin +1
How Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation
Jun 11, 2026Chase M. Fensore, Kaustubh Dhole, Jason Fan +2Benchmark Design Synthetic Data Generation
Department of Computer Science Emory University Atlanta, USA
TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
Jun 10, 2026Siyu Li, Toan Tran, Lingyi Zhao +2Trajectory Generation Human Behavior Simulation
Dept. of Computer Science, Emory University, Atlanta, GA, USA · Novateur Research Solutions, Ashburn, VA, USA
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
Jun 10, 2026Enhan Zhao, Wei Wu, Yuanrui Zhang +23D Spatial Reasoning Synthetic Data Generation
1Peking University · 2Ant International · 3The University of Hong Kong
ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories
Jun 9, 2026Siyuan Luo, Nairong Zheng, Lin Zhou +6Synthetic Data Generation Tool-Using Agents
University of Electronic Science and Technology of China · SenseTime Research
Fixed-Parameter Tractability of Private Synthetic Data Generation
Jun 9, 2026Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3Synthetic Data Generation Privacy-Preserving ML
Google Deepmind · Institute for Mathematical and Computational Engineering, Faculty of Mathematics and School of Engineering, Pontificia Universidad Cat´olica de Chile · Google Research
Boosting ECG Classification Performance by Pre-training with Synthesized Data
Jun 9, 2026Naoki Nonaka, Jun SeitaSynthetic Data Pretraining Synthetic-to-Real Domain Adaptation
Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters
ClusBench: The Clustering Benchmark Data Resource You've All Been Waiting For (?)
Jun 9, 2026David P. HofmeyrBenchmark Construction Clustering
School of Mathematical Sciences, Lancaster University
Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
Jun 9, 2026Yueyang Liu, Joon-Seok Kim, Andreas ZüfleSynthetic Data Generation Synthetic Anomaly Generation
Emory University, Atlanta, USA

Jun 21, 2026, stat.MLJ/K move · Enter open · S save

Data Evolution by Wittgenstein's Rule Following

Aydin Ghojogh, Benyamin Ghojogh

Synthetic Data Generation

Open paper AskPDF arXiv

This paper introduces Wittgenstein's Rule Following (WRF) data evolution, a framework in philomatics for evolving or generating a new dataset from a sequence of previously observed datasets. The method is inspired by Ludwig Wittgenstein's rule-following considerations and his notion of family resemblance in Philosophical Investigations. Unlike standard synthetic data generation, where the goal is usually to sample from or augment a fixed distribution, WRF aims to continue the implicit rule expressed by a historical sequence of datasets while preserving resemblance to the previous datasets. WRF represents each dataset by structural descriptors rather than pointwise correspondences. These descriptors summarize geometric, distributional, clustering, and, in the supervised case, label-based properties of the data. The method predicts a rule-following target by extrapolating descriptor trajectories and a family-resemblance target by averaging historical descriptors. Candidate datasets are then generated from the observed history through balanced or bounded mixture recombination, scored according to these targets, and optionally refined through differentiable optimization in descriptor space. The proposed framework allows both sample size and feature dimension to vary over time and does not assume that the next dataset is a direct transformation of the last one. Simulations on synthetic and image datasets show that WRF can generate meaningful continuations of evolving datasets in both unsupervised and supervised settings.

Momentum

Related topics

Latest papers 417