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

2025-06-03

arXiv.org

AI Summary

Current multimodal large language models (MLLMs) exhibit significant limitations in jointly performing fine-grained visual perception and commonsense causal reasoning. Method: We introduce Argus Inspection, a novel two-tier multimodal benchmark that uniquely integrates fine-grained visual recognition with real-world causal reasoning evaluation; propose the Eye of Panoptes evaluation framework, which innovatively combines a binary-parameterized sigmoid function with an indicator function to holistically quantify opinion-based reasoning; and design a standardized causal reasoning evaluation protocol. Contribution/Results: Empirical evaluation across 26 state-of-the-art MLLMs reveals that the highest fine-grained visual reasoning score is merely 0.46, demonstrating a fundamental deficiency in perception–reasoning synergy. This work establishes the first dedicated benchmark and evaluation framework for diagnosing coupled visual–causal reasoning capabilities in MLLMs, exposing critical gaps in current modeling paradigms.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Evaluating visual fine-grained perception in multimodal models
• Assessing commonsense causal inference capabilities of MLLMs
• Developing holistic evaluation framework for opinion-based reasoning tasks

Innovation

• Binary parametric Sigmoid metric integration
• Multimodal benchmark with dual difficulty levels
• Holistic evaluation framework for opinion reasoning