CompAgent: An Agentic Framework for Visual Compliance Verification

ArXi:2511.00171v3 Announce Type: replace Visual compliance verification is a critical yet underexplored problem in computer vision, especially in domains such as media, entertainment, and advertising where content must adhere to complex and evolving policy rules. Existing methods often rely on task-specific deep learning models trained on manually labeled datasets, which are costly to build and limited in generalizability.