mlsanityData to 14 Oct 2026

Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents

arXiv 2608.15109 ↗First posted 15 Aug 2026cs.AIcited by 0

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can still violate semantically meaningful domain constraints. We study the discovery and enforcement of three complementary inter-column constraint families---equations, linear inequalities, and logical dependencies. Our unified tool-grounded workflow represents all three as machine-executable hypotheses and applies a common interface for full-table validation, deterministic diagnosis, and counterexample-guided revision. A generator-agnostic postprocessor coordinates family-specific repairs on outputs from unchanged tabular generators. Across curated behavioral audits and end-to-end evaluations, the complete workflow improves held-out violation detection over one-shot direct prompting, while postprocessing yields zero measured violations for every retained, applicable constraint, improves downstream utility on most datasets, and largely preserves univariate marginals.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 28 references that are in the map. First posted on arXiv 15 Aug 2026.

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15 of 28 references.

References · 15

  1. 2019Modeling Tabular data using Conditional GANcited by 699
  2. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
  3. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  4. 2018Data Synthesis based on Generative Adversarial Networkscited by 185
  5. 2022STaSy: Score-based Tabular data Synthesiscited by 86
  6. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  7. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  8. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 28
  9. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  10. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 15
  11. 2025LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusioncited by 9
  12. 2024AIGT: AI Generative Table Based on Promptcited by 7
3 more
  1. 2024Preserving logical and functional dependencies in synthetic tabular datacited by 4
  2. 2025Dependency-aware synthetic tabular data generationcited by 2
  3. 2025Quality Assessment of Tabular Data using Large Language Models and Code Generationcited by 1

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  1. 2026MathConstraint: Automated Generation of Verified Combinatorial Reasoning Instances for LLMs
  2. 2026Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
  3. 2026TabTracer: Monte Carlo Tree Search for Complex Table Reasoning with Large Language Models
  4. 2026Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
  5. 2026Deep Tabular Research via Continual Experience-Driven Execution
  6. 2026Enhancing Table Reasoning with Deterministic Table-State Rewards
  7. 2025TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning
  8. 2026Differentially Private Synthetic Data via APIs 4: Tabular Data