Synthetic Data Evaluation

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22 papers in the last four weeks, up 100% on the four weeks before. 0.2% of all new papers.

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Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting
Oct 7, 2026Guoxiong Long, Huizhen Huang, Qikun Cai +2Synthetic Data Evaluation Synthetic Data Generation
School of Computer and Big Data, Minjiang University, No. 200 Xiyuangong Road, Fuzhou, 350108, Fujian, China
FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases
Oct 7, 2026Hongzhuo Chen, Zhanliang Wang, Florent Pollet +9Rare Disease Diagnosis Synthetic Data Evaluation
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA · Applied Mathematics and Computational Science Graduate Program, University of Pennsylvania, Philadelphia, PA, USA · Department of Biomedical Informatics, Columbia University, New York, NY, USA +6
Invent a Dataset: Measuring dataset generation abilities with zero seed
Oct 1, 2026Shivalika Singh, Andrija Djurisic, Gbemileke Onilude +2Synthetic Data Generation Synthetic Data Generation for Language Models
Adaption
Generation Provenance Before Behavior Attribution: Auditing Synthetic Speech Research Objects
Oct 1, 2026Sidi Chang, Peiying ZhuData Provenance Training Data Attribution
Blossom AI Labs Blossom AI Tokyo, Japan
Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution
Sep 30, 2026Joss ArmstrongModel Collapse Training Data Attribution
Ericsson, Athlone, Ireland
Synthetic Data Characterization via Training Dynamics
Sep 30, 2026Irene Lago, Ana Ezquerro, David VilaresSynthetic Data Generation for Language Models Synthetic Data Evaluation
Universidade da Coruña, CITIC, Spain · Graz University of Technology, IML, Austria
SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling
Sep 29, 2026Yunan Lu, Shuang Xie, Meghna Allamudi +4Synthetic Data Generation User Modeling
Shopify · Columbia University
AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
Sep 28, 2026Haotian Luo, Haoyu Wang, Zeyu Qin +5AI Agent Benchmarks Synthetic Benchmark Generation
HKUST · SLAI · NTU
StatD2GAN: When Calibration Masks Generator Quality in Held-Out Evaluation of Synthetic Weather Sequences
Sep 27, 2026Mustafa Ozaytac, Ozge Karadag AtasTime Series Generation Model Calibration
Department of Statistics, Graduate School of Science and Engineering, Hacettepe University, Ankara, 06800, Turkey · Department of Statistics, Faculty of Science, Hacettepe University, Ankara, 06800, Turkey
Artificial Societies Benchmark: A Validation Framework for Synthetic Research
Sep 24, 2026Edoardo Chidichimo, Min Jun Jung, Felix P. S. Wallis +1Benchmark Design Computational Social Science
Artificial Societies University of Oxford, UK
Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark
Sep 24, 2026Christine Park, Valerie Chen, Tim DettmersSynthetic Benchmark Generation Clinical Language Model Evaluation
Carnegie Mellon University
Augur: A Synthetic Decision Lab for Rehearsing Reactions to Product and Policy Changes
Sep 24, 2026Rahul Khedar, Mayank Malhotra, Avinash KarnLLM Evaluation LLM Decision-Making
PayPal AI
Consequential Behaviour and Representational Fairness in the Validation of Synthetic Research
Sep 23, 2026Florian Kutzner, Celina Kacperski, Laura de Molière +4Algorithmic Fairness Large Language Model-Based Social Simulation
decision-context. · Seeburg Castle University, Seekirchen am Wallersee, Austria. · Artificial Societies Ltd., London, United Kingdom. +1
Concept Drift from a Causal Perspective
Sep 21, 2026Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1Concept Drift Drifting Models
LIVIA, École de Technologie Supérieure · Graduate Program in Informatics (PPGIa), Pontifícia Universidade Católica do Paraná (PUCPR)
A paired synthetic construction-site image dataset for robust computer vision under adverse conditions
Sep 21, 2026Viet Huy Duong, Ruoxin Xiong, Md Abdullah Al Forhad +1Synthetic Data Generation Synthetic Benchmark Generation
Department of Computer Science, College of Arts and Sciences, Kent State University, Kent, Ohio, United States. · Assistant Professor, Construction Management Program, College of Architecture and Environmental Design, Kent State University, Kent, Ohio, United States. · Department of Computer Science and Engineering, University of North Texas, Denton, Texas, United States. +1
Error-Supervised Synthetic Learner Writing for Automated Essay Scoring
Sep 20, 2026Duy Anh NguyenAutomated Essay Scoring Synthetic Data Generation for Language Models
University of Greenwich, London, United Kingdom · FPT University, Can Tho, Vietnam
Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception
Sep 16, 2026Hannan Ejaz Keen, Muhammad Moazam Fraz, Karsten BernsSynthetic Data Generation for Robotics Robotic Perception
XITASO GmbH IT & Software Solutions, Augsburg, Germany · School of Electrical Engineering and Computer Science at National University of Sciences and Technology (NUST), Islamabad, Pakistan · Department of Computer Science at Technical University of Kaiserslautern, Kaiserslautern, Germany
Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder
Sep 15, 2026Graeme Kelly, Emilio J. Palacios-Garcia, Barry P. HayesSynthetic Data Generation Electric Vehicle Charging
School of Engineering and Architecture, University College Cork, Cork, Ireland · MaREI Centre, Sustainability Institute, University College Cork, Cork, Ireland
Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML
Sep 14, 2026Sidi Chang, Peiying ZhuLLM-as-a-Judge LLM Auditing
Blossom AI San Francisco, CA, USA
Meddies-PII: A Multilingual Framework for Personally Identifiable Information Extraction in Clinical De-identification
Sep 11, 2026Linh Uyen Le, Ho Dinh Tri, Christian Hoang +1Synthetic Data Generation Clinical Information Extraction
Meddies AI, Vietnam
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Sep 10, 2026Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1Causal Foundation Models Structural Causal Models
School of Artificial Intelligence, Nanjing University
Generating a Consistent Enterprise: Synthesis and Reference-Free Evaluation of Multi-System Business Data
Sep 10, 2026Benjamin Gruenbaum, Doron Porat, Assaf Natanzon +4Synthetic Data Generation Synthetic Benchmark Generation
Eon
Subgroup Membership Inference Audits of Differentially Private Synthetic Text
Sep 9, 2026Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +2Privacy Auditing Membership Inference Attacks
Imperial College London, Imperial Global Singapore · Nanyang Technological University, Singapore · Imperial College London, United Kingdom
Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure
Sep 7, 2026Alexander DoudkinUser Simulation Tabular Data Synthesis
Minds AI Labs, Inc.
MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design
Sep 4, 2026Noah J. Bagazinski, Md. Ferdous Alam, Jaya Manideep Rebbagondla +1Synthetic Data Generation Synthetic Data Evaluation
Department of Mechanical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139 · George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology Atlanta, GA 30318
Portable Causal Fairness Across Synthetic Data Generator Families
Sep 2, 2026Steven Golob, Sikha Pentyala, Martine De CockAlgorithmic Fairness Synthetic Tabular Data Generation
University of Washington Tacoma
IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]
Sep 2, 2026Lulu Xie, Yancheng Wang, Kanchan Chowdhury +3Synthetic Data Generation Image Generation
Arizona State University · Marquette University
Position: Privacy Is a Claim, Not a Property of Synthetic Data
Sep 1, 2026Jiachen Zhao, Antonia Januszewicz, Taeho JungPrivacy Auditing Privacy-Preserving ML
Department of Computer Science Engineering, University of Notre Dame, Notre Dame, United States.
A Network Science Perspective on Evaluating Deep Graph Generative Models
Sep 1, 2026Tianrui Mao, Abele Malan, Megha Khosla +2Synthetic Data Generation Generative Modeling
Delft University of Technology, the Netherlands · University of Neuchâtel, Switzerland
RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection
Aug 31, 2026Quan Hao, Chenxi Zhang, Ziyang Tao +6Synthetic Data Augmentation Synthetic Data Generation
1The School of Information Science and Technology, Beijing University of Technology · 2Pratt School of Engineering, Duke University · 3Academy of Mathematics and Systems Science, Chinese Academy of Sciences +3

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Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting

Guoxiong Long, Huizhen Huang, Qikun Cai, Tao Huang+1

School of Computer and Big Data, Minjiang University, No. 200 Xiyuangong Road, Fuzhou, 350108, Fujian, China

Synthetic Data Evaluation Synthetic Data Generation Time Series Generation Differential Privacy Time Series Forecasting

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Differentially private time-series generators commonly produce fixed-length synthetic windows, whereas downstream forecasting models often require long continuous training sequences. How these windows are assembled after generation can therefore alter the effective synthetic data presented to a forecaster, even when the trained generator remains unchanged. We study this post-generation sequence assembly process by systematically varying overlap rates and window-weighting schemes and evaluating the resulting sequences in terms of boundary continuity, statistical and temporal fidelity, and Train-on-Synthetic-Test-on-Real (TSTR) forecasting utility. Across four types of public datasets (ETTh1, ETTm1, Weather, and Appliances) and five forecasting models, the results reveal a clear forecaster-dependent assembly principle: downstream TSTR utility is jointly shaped by the forecaster, overlap rate, and window-weighting scheme, leading to distinct assembly preferences across forecasting models. Increased overlap generally improves boundary continuity, but improvements in continuity or individual fidelity diagnostics do not consistently reduce forecasting error, indicating that these diagnostics alone are insufficient for selecting assembly configurations. Complete five-forecaster assembly grids, together with matched Train-on-Real-Test-on-Real (TRTR) references, further characterize these regularities and quantify assembly-dependent utility relative to real-data training. We then validate the identified principles through additional analyses of robustness and generator variability.

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