Title: MARIC: Multi-Agent Reasoning for Image Classification Show Chinese title

Title: MARIC：图像分类的多智能体推理

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 classification as a collaborative reasoning process. MARIC first utilizes an Outliner Agent to analyze the global theme of the image and generate targeted prompts. Based on these prompts, three Aspect Agents extract fine grained descriptions along distinct visual dimensions. Finally, a Reasoning Agent synthesizes these complementary outputs through integrated reflection step, producing a unified representation for classification. By explicitly decomposing the task into multiple perspectives and encouraging reflective synthesis, MARIC mitigates the shortcomings of both parameter-heavy training and monolithic VLM reasoning. Experiments on 4 diverse image classification benchmark datasets demonstrate that MARIC significantly outperforms baselines, highlighting the effectiveness of multi-agent visual reasoning for robust and interpretable image classification. Show Chinese abstract

Abstract: 图像分类传统上依赖于参数密集型模型训练，需要大规模标注数据集和广泛的微调才能实现有竞争力的性能。 尽管最近的视觉语言模型（VLMs）减轻了这些限制，但它们仍受限于对单次传递表示的依赖，通常无法捕捉视觉内容的互补方面。 在本文中，我们引入了基于多智能体的图像分类推理（MARIC），这是一个多智能体框架，将图像分类重新表述为协作推理过程。 MARIC首先利用一个轮廓智能体来分析图像的全局主题并生成针对性提示。 基于这些提示，三个方面智能体沿着不同的视觉维度提取细粒度描述。 最后，推理智能体通过集成反思步骤合成这些互补输出，生成用于分类的统一表示。 通过明确将任务分解为多个视角并鼓励反思性合成，MARIC弥补了参数密集型训练和单一VLM推理的不足。 在4个不同的图像分类基准数据集上的实验表明，MARIC显著优于基线，突显了多智能体视觉推理在鲁棒和可解释图像分类中的有效性。