MultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification

• URL: http://arxiv.org/abs/2604.03586v1
• Date: Sat, 04 Apr 2026 04:25:26 GMT
• Title: MultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification
• Abstract summary: MultiPress is a novel three-stage multi-agent framework for multimodal news classification.<n>It integrates specialized agents for multimodal perception, retrieval-augmented reasoning, and gated fusion scoring.<n>We validate MultiPress on a newly constructed large-scale multimodal news dataset.
• Score: 9.440872951369467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing prevalence of multimodal news content, effective news topic classification demands models capable of jointly understanding and reasoning over heterogeneous data such as text and images. Existing methods often process modalities independently or employ simplistic fusion strategies, limiting their ability to capture complex cross-modal interactions and leverage external knowledge. To overcome these limitations, we propose MultiPress, a novel three-stage multi-agent framework for multimodal news classification. MultiPress integrates specialized agents for multimodal perception, retrieval-augmented reasoning, and gated fusion scoring, followed by a reward-driven iterative optimization mechanism. We validate MultiPress on a newly constructed large-scale multimodal news dataset, demonstrating significant improvements over strong baselines and highlighting the effectiveness of modular multi-agent collaboration and retrieval-augmented reasoning in enhancing classification accuracy and interpretability.

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