arXiv:2509.14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fine tuning to achieve competitive performance. While recent vision language models (VLMs) alleviate some of these constraints, they remain limited by their reliance on single pass representations, often failing to capture complementary aspects of visual content. In this paper, we introduce Multi Agent based Reasoning for Image Classification (MARIC), a multi agent framework that reformulates image
The proliferation of advanced AI research and the limitations of existing single-pass vision models necessitate innovative approaches like multi-agent systems for image classification to improve efficiency and accuracy.
This development indicates a move towards more sophisticated, reasoning-based AI systems that can reduce reliance on massive datasets and extensive fine-tuning, thus lowering computational barriers and enhancing model robustness.
Image classification models are evolving from parameter-heavy, single-pass systems to more agentic, reasoning-based frameworks, potentially making AI more adaptable and less data-hungry.
- · AI researchers
- · Developers of multi-agent systems
- · Industries requiring high-precision image analysis
- · Legacy image classification models
- · Companies relying solely on massive, fine-tuned datasets
Improved accuracy and efficiency in image classification tasks due to multi-agent reasoning.
Reduced computational costs and data requirements for deploying advanced computer vision applications.
Acceleration of AI development in fields beyond image classification, leveraging similar multi-agent architectures for complex problem-solving.
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