arXiv:2505.13466v1 Announce Type: new
Abstract: The scarcity of data depicting dangerous situations presents a major obstacle to training AI systems for safety-critical applications, such as construction safety, where ethical and logistical barriers hinder real-world data collection. This creates an urgent need for an end-to-end framework to generate synthetic data that can bridge this gap. While existing methods can produce synthetic scenes, they often lack the semantic depth required for scene simulations, limiting their effectiveness. To address this, we propose a novel multi-agent framework that employs an iterative, in-the-loop collaboration between two agents: an Evaluator Agent, acting as an LLM-based judge to enforce semantic consistency and safety-specific constraints, and an Editor Agent, which generates and refines scenes based on this guidance. Powered by LLM’s capabilities to reasoning and common-sense knowledge, this collaborative design produces synthetic images tailored to safety-critical scenarios. Our experiments suggest this design can generate useful scenes based on realistic specifications that address the shortcomings of prior approaches, balancing safety requirements with visual semantics. This iterative process holds promise for delivering robust, aesthetically sound simulations, offering a potential solution to the data scarcity challenge in multimedia safety applications.
Expert Commentary: Bridging the Data Gap in Safety-Critical AI Systems
In the realm of AI-driven safety applications, the scarcity of real-world data depicting dangerous situations poses a significant challenge for training systems to effectively identify and respond to potential risks. The traditional approach of using real-life data for training is often limited by ethical considerations, as well as the practical difficulties of collecting diverse and representative datasets.
This article highlights the importance of developing an innovative framework for generating synthetic data that can simulate safety-critical scenarios with the necessary semantic depth. The proposed multi-agent framework, which leverages the collaboration between an Evaluator Agent and an Editor Agent, marks a significant step forward in addressing this data scarcity challenge.
One key aspect of this framework is the use of Language Model (LLM)-based reasoning to enforce semantic consistency and safety-specific constraints in the synthetic scene generation process. By integrating common-sense knowledge and safety guidelines into the AI decision-making process, the system can produce realistic and meaningful scenes that balance safety requirements with visual semantics.
The iterative nature of the collaboration between the Evaluator Agent and the Editor Agent allows for continuous refinement and improvement of the synthetic scenes, ensuring that the final output meets the desired specifications for safety-critical applications. This approach not only enhances the quality of the generated data but also opens up new possibilities for creating robust and visually accurate simulations.
Overall, this multi-disciplinary framework represents a promising solution to the data scarcity challenge in multimedia safety applications. By combining the strengths of AI reasoning, common-sense knowledge, and safety guidelines, this approach has the potential to revolutionize the training of AI systems for construction safety and other critical applications, ultimately leading to safer and more reliable outcomes in real-world scenarios.