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arXiv · 2506.14805

Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?

Yang Yao·Lingyu Li·Jiaxin Song·Chiyu Chen·Zhenqi He·Yixu Wang·Xin Wang·Tianle Gu·Jie Li·Yan Teng·Yingchun Wang
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Abstract

As Multimodal Large Language Models (MLLMs) continue to evolve, their cognitive and reasoning capabilities have seen remarkable progress. However, challenges in visual fine-grained perception and commonsense causal inference persist. This paper introduces Argus Inspection, a multimodal benchmark with two levels of difficulty, emphasizing detailed visual recognition while incorporating real-world commonsense understanding to evaluate causal reasoning abilities. Expanding on it, we present the Eye of Panoptes framework, which integrates a binary parametric Sigmoid metric with an indicator function, enabling a more holistic evaluation of MLLMs' responses in opinion-based reasoning tasks. Experiments conducted on 26 mainstream MLLMs reveal that the highest performance in visual fine-grained reasoning reaches only 0.46, highlighting considerable potential for enhancement. Our research offers valuable perspectives for the continued refinement of MLLMs.

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Yang Yao, Lingyu Li, Jiaxin Song, Chiyu Chen, Zhenqi He, Yixu Wang, Xin Wang, Tianle Gu, Jie Li, Yan Teng, Yingchun Wang. 2025-06-03. Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?. https://arxiv.org/abs/2506.14805

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