AgentPS: Agentic Process Supervision for Content Moderation with Multimodal LLMs

• URL: http://arxiv.org/abs/2412.15251v2
• Date: Fri, 04 Jul 2025 00:16:22 GMT
• Title: AgentPS: Agentic Process Supervision for Content Moderation with Multimodal LLMs
• Authors: Mingchao Liu, Yu Sun, Ruixiao Sun, Xin Dong, Xiang Shen, Hongyu Xiong,
• Abstract summary: We introduce AgentPS, a framework that integrates Agentic Process Supervision into large language models.<n>We show that AgentPS achieves substantial improvements over baseline MLLMs on public benchmarks and proprietary datasets.<n>These results establish AgentPS as a scalable and effective solution for complex multimodal classification in large-scale industrial applications.
• Score: 9.35901507816989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advanced processing and reasoning capabilities of multimodal large language models (MLLMs) have driven substantial progress in vision-language (VL) understanding tasks. However, while effective for tasks governed by straightforward logic, MLLMs often struggle with reasoning complex, detail-intensive logical structures. To address this limitation, we introduce AgentPS, a novel framework that integrates Agentic Process Supervision into MLLMs by sequentially reasoning over ancillary questions during fine-tuning. AgentPS achieves substantial improvements over baseline MLLMs on both public benchmarks and proprietary datasets. Notably, we show that using MLLM-generated ancillary labels in place of human annotations yields only minimal performance degradation, highlighting the method's scalability. These results establish AgentPS as a scalable and effective solution for complex multimodal classification in large-scale industrial applications.

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