arXiv:2605.28198v1 Announce Type: new Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes. In this paper, we propose a hierarchical hybrid top-down and bottom-up (H-TDBU) framework that decouples semantic structures from stochastic texture. In the top-down path, structure-driven logical constraints and cross-modal alignment rules are constructed, while in the bottom-up path, lightweight tabular generat
This research addresses current limitations in synthetic data generation, which is becoming critical as AI models demand vast, high-quality datasets that actual data often cannot provide safely or sufficiently.
Improving synthetic data generation directly enhances the capabilities, safety, and privacy of AI models by providing more robust, diverse, and consistent training data, crucial for generalizable AI applications.
The proposed hybrid framework, H-TDBU, offers a new paradigm for creating synthetic tabular data that is more logically consistent and handles data heterogeneity better than previous methods.
- · AI model developers
- · Data scientists
- · Industries with sensitive data (healthcare, finance)
- · Cloud providers offering data services
- · Traditional data collection companies (if not adapting)
- · AI models reliant solely on insufficient real-world data
Wider adoption of advanced synthetic data generation techniques across AI research and development.
Accelerated development of AI agents and autonomous systems due to more reliable training data.
Potential for new ethical and regulatory frameworks around the use and lineage of highly realistic synthetic data.
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