MultiPress: A Multi-Agent Framework for Interpretable Multimodal News Classification
arXiv:2604.03586v1 Announce Type: new 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-aug
arXiv:2604.03586v1 Announce Type: new 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.
Executive Summary
The article introduces MultiPress, a novel three-stage multi-agent framework designed to enhance multimodal news classification by integrating specialized agents for perception, retrieval-augmented reasoning, and gated fusion scoring. The framework leverages modular collaboration and iterative optimization to address limitations in existing methods, which often process modalities independently or use simplistic fusion strategies. Validated on a newly constructed large-scale dataset, MultiPress demonstrates superior classification accuracy and interpretability compared to strong baselines, underscoring the efficacy of multi-agent systems and cross-modal reasoning in handling heterogeneous data such as text and images.
Key Points
- ▸ MultiPress employs a three-stage architecture with specialized agents for multimodal perception, retrieval-augmented reasoning, and gated fusion scoring to enhance news classification.
- ▸ The framework introduces a reward-driven iterative optimization mechanism to refine cross-modal interactions and improve classification accuracy.
- ▸ A newly constructed large-scale multimodal news dataset is used to validate MultiPress, demonstrating its superiority over existing baselines in terms of both performance and interpretability.
Merits
Innovative Multi-Agent Architecture
MultiPress's modular design with specialized agents for distinct tasks (perception, reasoning, fusion) enables more sophisticated handling of multimodal data compared to traditional monolithic or simplistic fusion-based approaches.
Retrieval-Augmented Reasoning
The integration of retrieval-augmented reasoning allows the framework to leverage external knowledge, enhancing its ability to interpret complex cross-modal interactions and improve classification accuracy.
Interpretability and Explainability
The gated fusion scoring and modular design of MultiPress inherently support interpretability, providing clearer insights into how decisions are made compared to end-to-end black-box models.
Empirical Validation
The use of a newly constructed large-scale dataset and comparison against strong baselines substantiates the framework's effectiveness, offering robust evidence of its superiority in real-world scenarios.
Demerits
Computational Overhead
The multi-agent framework and iterative optimization mechanism may introduce significant computational overhead, potentially limiting scalability and practical deployment in resource-constrained environments.
Dataset Dependence
The framework's performance is contingent on the quality and representativeness of the newly constructed dataset, which may not generalize to all multimodal news classification scenarios or domains.
Complexity of Deployment
The modular design, while advantageous for interpretability, may pose challenges in deployment and integration with existing systems, requiring careful orchestration of agents and fusion mechanisms.
Expert Commentary
The introduction of MultiPress represents a significant advancement in the field of multimodal news classification, particularly in its adoption of a multi-agent framework to address the inherent complexities of cross-modal reasoning. The modular design, which separates perception, reasoning, and fusion into specialized agents, is a departure from traditional approaches that often treat modalities as independent or employ simplistic fusion strategies. This separation not only enhances performance but also improves interpretability—a critical factor in domains where decisions must be explainable. The retrieval-augmented reasoning component further distinguishes MultiPress by enabling the framework to draw on external knowledge, a feature that is increasingly vital in an era of information overload and misinformation. However, the computational demands of such a system cannot be overlooked, particularly in resource-constrained settings. Additionally, while the newly constructed dataset provides a robust benchmark, the framework's generalizability to other domains or languages remains an open question. Overall, MultiPress sets a new benchmark for multimodal news classification, but its real-world adoption will depend on addressing scalability and deployment challenges.
Recommendations
- ✓ Further research should explore methods to reduce the computational overhead of MultiPress, such as model distillation or lightweight agent architectures, to enhance scalability for large-scale deployment.
- ✓ Investigate the generalizability of MultiPress across diverse datasets and domains, including non-English news sources, to validate its robustness and adaptability beyond the current benchmark.
- ✓ Develop standardized evaluation metrics for interpretability in multimodal classification systems to ensure consistent and meaningful comparisons across frameworks.
- ✓ Explore the integration of MultiPress with existing newsroom technologies and content management systems to assess its practical feasibility and user adoption in real-world settings.
- ✓ Address potential biases in the dataset and retrieval mechanisms through rigorous fairness audits and the incorporation of diverse training data to ensure equitable performance across different demographic and geographic contexts.
Sources
Original: arXiv - cs.CL