CompAgent: An Agentic Framework for Visual Compliance Verification

TL;DR

CompAgent introduces an agentic framework that combines multimodal large language models with visual tools and planning to improve the accuracy and scalability of visual compliance verification in complex policy domains.

Contribution

This work presents the first agentic framework that integrates MLLMs with visual tools and dynamic planning for effective visual compliance verification.

Findings

Achieves up to 76% F1 score on public benchmarks.

Outperforms specialized classifiers and baseline methods.

Improves state-of-the-art performance by 10% on UnsafeBench.

Abstract

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. While recent Multimodal Large Language Models (MLLMs) offer broad real-world knowledge and policy understanding, they struggle to reason over fine-grained visual details and apply structured compliance rules effectively on their own. In this paper, we propose CompAgent, the first agentic framework for visual compliance verification. CompAgent augments MLLMs with a suite of visual tools-such as object detectors, face analyzers, NSFW detectors, and captioning models-and introduces a planning agent that dynamically selects appropriate tools based on the compliance policy. A compliance verification agent then integrates image, tool outputs, and policy context to perform multimodal reasoning. Experiments on public benchmarks show that CompAgent outperforms specialized classifiers, direct MLLM prompting, and curated routing baselines, achieving up to 76% F1 score and a 10% improvement over the state-of-the-art on the UnsafeBench dataset. Our results demonstrate the effectiveness of agentic planning and robust tool-augmented reasoning for scalable, accurate, and adaptable visual compliance verification.