See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis

Researchers introduce ArtiAgent, an automated system that creates pairs of real and artifact-injected images to help AI models better detect and fix visual artifacts in generated content. The system uses three specialized agents to synthesize 100K annotated images, addressing the costly and scaling challenges of human-labeled artifact datasets.