Reasoning-Driven Synthetic Data Generation and Evaluation

2026-03-31

AI Summary

This work addresses the challenges of real-world data scarcity, high acquisition costs, and privacy sensitivity in multimodal AI training by introducing Simula, a novel framework that pioneers inference-driven synthetic data generation without requiring any seed data. By integrating an agent-based architecture with a controllable generation pipeline, Simula enables fine-grained control over data characteristics and computational resource allocation, substantially enhancing the interpretability and scalability of synthetic data. Through a comprehensive multidimensional evaluation protocol, the framework simultaneously validates both the intrinsic quality of the generated data and its effectiveness in downstream tasks across multiple benchmarks, offering a practical pathway and design paradigm for AI development under data-constrained conditions.

Abstract

Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.

Problem

• synthetic data
• data scarcity
• multi-modal models
• data generation
• scalability

Innovation

• reasoning-driven
• synthetic data generation
• seedless agentic approach
• controllable data synthesis
• explainable AI