Synthetic Data Generation

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

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The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets
May 19, 2026Gustav Olaf Yunus Laitinen-Fredriksson Lundström-ImanovData Provenance Dataset Watermarking
Department of Economics Stockholm University Stockholm, Sweden
Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
May 19, 2026Bing Wang, Rui Miao, Ximing Li +6Fake News Detection LLM Fine-Tuning
College of Computer Science and Technology, Jilin University Changchun, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University · School of Artificial Intelligence, Jilin University Changchun, China +3
SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
May 18, 2026Kailai Sun, Mingyi He, Heye Huang +5Conditional Image Generation Synthetic Data Generation
SMART, Singapore · Massachusetts Institute of Technology Cambridge, MA, USA · University of Florida Gainesville, Florida, USA
Generating Pretraining Tokens from Organic Data for Data-Bound Scaling
May 18, 2026Zichun Yu, Chenyan XiongLanguage Model Pretraining Synthetic Data Pretraining
Language Technologies Institute, Carnegie Mellon University · Xlue
Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale
May 18, 2026Jinghui Liu, Sarvesh Soni, Anthony NguyenSynthetic Data Augmentation Automated Medical Coding
Australian e-Health Research Centre, CSIRO, Australia · National Library of Medicine, National Institutes of Health, USA
MagBridge-Battery: A Synthetic Bridge Dataset for Li-ion Magnetometry and State-of-Health Diagnostics
May 17, 2026Sakthi Prabhu Gunasekar, Prasanna Kumar RangarajanBenchmark Design Synthetic Data Generation
Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham, India
Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
May 17, 2026Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12Tool-Augmented Language Model Agents Synthetic Data Generation
Northeastern University · Independent Researcher
TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies
May 17, 2026Daoming Wan, Yizheng Huang, Jimmy X. HuangClustering Synthetic Data Generation
Information Retrieval and Knowledge Management Research Lab, York University Toronto, Ontario, Canada
CasualSynth: Generating Structurally Sound Synthetic Data
May 17, 2026Zehua Cheng, Wei Dai, Jiahao Sun +1Structural Causal Models Synthetic Data Generation
Department of Computer Science, University of Oxford · FLock.io United Kingdom · Institute of Logic and Computation, TU Wien
On Applicability of Synthetic Datasets for Facial Expression Recognition
May 17, 2026Ali Azmoudeh, Erdi Sarıtaş, Ömer Yıldırım +1Class-Imbalanced Learning Synthetic Data Generation
Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye · Department of Informatics, University of Zurich, Zurich, Switzerland · Division of Engineering, NYU Abu Dhabi, Abu Dhabi, UAE
GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
May 15, 2026Chuancheng Zhang, Zhenhao Wang, Kaizheng Li +3Autonomous Driving Safety Evaluation Synthetic Data Generation
Shenzhen Research Institute of Shandong University, Shenzhen, 518057, Guangdong, China · School of Airspace Science and Engineering, Shandong University, 264209 Weihai, China. · School of Computer Science and Technology, Shandong University of Finance and Economics, 250014, Jinan, China.
Does Synthetic Layered Design Data Benefit Layered Design Decomposition?
May 14, 2026Kam Man Wu, Haolin Yang, Qingyu Chen +3Synthetic Data Generation
1HKUST · 2Webank
Privacy Evaluation of Generative Models for Trajectory Generation
May 14, 2026Stavros Bouras, Ioannis Kontopoulos, Chiara Pugliese +5Privacy Auditing Trajectory Generation
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece · Institute of Informatics and Teletematics, National Research Council (CNR), Pisa, Italy · Institute of Information Science and Technologies, National Research Council (CNR), Pisa, Italy
LiWi: Layering in the Wild
May 14, 2026Yu He, Fang Li, Haoyang Tong +7Intrinsic Image Decomposition Synthetic Data Generation
MAIS & NLPR, CASIA
SceneForge: Structured World Supervision from 3D Interventions
May 14, 2026Jizhizi Li, Jiayang Ao, Danny Wicks +1Multi-View Consistency Synthetic Data Generation
1Canva Research
NodeSynth: Socially Aligned Synthetic Data for AI Evaluation
May 14, 2026Qazi Mamunur Rashid, Xuan Yang, Zhengzhe Yang +5Language Model Generation Evaluation Language Model Safety Evaluation
Google Research · Google · Google Deepmind
Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection
May 13, 2026Anjir Ahmed Chowdhury, Syed Zawad, Feng YanSynthetic Data Generation LLM Inference Acceleration
Department of Computer Science University of Houston · IBM Research
Synthetic American Option Pricing via Jump-HMM-Driven Heston Implied Volatility
May 13, 2026Julia Sun, Zheyu Jin, Jiawei Zhang +1Quantitative Finance Synthetic Data Generation
Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell University, Ithaca, NY 14853
Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception
May 13, 2026Arka Bhowmick, Enes Ozeren, Ahmed Abdullah +13D Asset Generation Synthetic Data Generation
1BIT Technology Solutions GmbH, Germany · 2Mannheim University of Applied Sciences, Germany
SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing
May 13, 2026Marten J. Finck, Niklas C. Koser, Sarker M. Mahfuz +73D Mesh Generation Synthetic Data Generation
Visual Computing and Artificial Intelligence, Kiel University, Germany · Institute for Medical Informatics and Statistics, Kiel University, Germany · Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany +2
Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes
May 13, 2026Kaicong Huang, Talha Azfar, Weisong Shi +13D Gaussian Splatting Synthetic Data Generation
Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY USA 12180. · Department of Computer and Information Sciences, University of Delaware, Newark, DE USA 19716.
AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions
May 13, 2026Ishika Agarwal, Sofia Stoica, Emre Can Acikgoz +4Synthetic Data Generation Synthetic Data Generation for Language Models
University of Illinois Urbana-Champaign1 · Amazon2
Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model
May 13, 2026Tianhao Qiu, Xiaojun ChenKnowledge Augmentation for Language Models Synthetic Data Generation
Shenzhen University Shenzhen, China
When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method
May 13, 2026Sai Hemanth Kilaru, Sriram Theerdh Manikyala, Raghav Upadhyay +3LLM Prompting Social Bias in Language Models
University of Arizona. Tucson, AZ 85721
A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
May 12, 2026Mohamed Ahmed Mohamed, Xiaowei HuangSynthetic Data Pretraining Synthetic-to-Real Domain Adaptation
School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, UK
Covering Human Action Space for Computer Use: Data Synthesis and Benchmark
May 12, 2026Miaosen Zhang, Xiaohan Zhao, Zhihong Tan +14Computer-Use Agent Benchmarks Computer-Use Agents
1Southeast University · 2Mohamed bin Zayed University of Artificial Intelligence · 3Wuhan University +2
LychSim: A Controllable and Interactive Simulation Framework for Vision Research
May 12, 2026Wufei Ma, Chloe Wang, Siyi Chen +3Synthetic Data Generation 3D Scene Generation
Johns Hopkins University
STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
May 12, 2026Shuhan Ye, Yi Yu, Qixin Zhang +5Synthetic Data Generation Spiking Neural Networks
Nanyang Technological University · Jilin University · Wuhan University +1
SpatialForge: Bootstrapping 3D-Aware Spatial Reasoning from Open-World 2D Images
May 12, 2026Zishan Liu, Ruoxi Zang, Yanglin Zhang +5Visual Spatial Reasoning 3D Spatial Reasoning
1Lingnan University · 2XPENG Robotics

May 19, 2026, econ.GNJ/K move · Enter open · S save

The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets

Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov

Department of Economics Stockholm University Stockholm, Sweden

Data Provenance Dataset Watermarking Synthetic Data Generation Model Collapse

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Generative artificial intelligence is rapidly transforming the supply side of training data: an increasing share of new tokens, images, and structured records is produced by previous-generation models rather than by human originators. Recursive training on such synthetic content induces a measurable and often irreversible loss of distributional fidelity, a phenomenon known as model collapse. We develop the first unified microeconomic theory of synthetic data markets under model collapse. We introduce the Synthetic Data Contamination Equilibrium (SDCE), prove existence and generic uniqueness, derive a welfare decomposition W = W_prod + W_cons - L_coll - L_info, establish a Wasserstein-gradient-flow mean-field collapse limit, prove an impossibility of information-constrained implementation, and obtain closed-form expressions for the welfare-maximizing provenance subsidy s* = KL(q||p)/(2 kappa) and the welfare-maximizing watermark strength w* = (1 - psi) KL(q||p)/(2 kappa psi). We prove an information-theoretic Cramer-Rao lower bound on any provenance estimator using only producer-side observations and show that the Provenance-Market Iterative Retraining (PMIR) algorithm attains this bound up to constants while converging to an epsilon-SDCE in O(epsilon^-2 log T) iterations. A reduced-form OLS estimation on a C4-synthetic benchmark over ten retraining generations yields a collapse-rate coefficient b-hat = 0.181 (HAC s.e. 0.024), within one standard error of the structural prediction 0.183. Calibrated experiments raise generation-ten model quality by 23.1 percent over the unregulated benchmark while lowering the 2-Wasserstein drift on a held-out diversity probe from 0.318 to 0.142. Scaling experiments over generations t in {1,...,10} recover a logarithmic-in-t collapse law log Q_t = log Q_0 - 0.183 t rho^2 with R^2 = 0.962.

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