Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models

• URL: http://arxiv.org/abs/2506.06242v1
• Date: Fri, 06 Jun 2025 17:06:25 GMT
• Title: Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models
• Authors: Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu,
• Abstract summary: We introduce the Visual Graph Arena (VGA) to evaluate and improve AI systems' capacity for visual abstraction.<n>Humans achieve near-perfect accuracy across tasks, while models totally failed on isomorphism detection and showed limited success in path/cycle tasks.<n>By isolating the challenge of representation-invariant reasoning, the VGA provides a framework to drive progress toward human-like conceptualization in AI visual models.
• Score: 51.900488744931785
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in multimodal large language models have driven breakthroughs in visual question answering. Yet, a critical gap persists, `conceptualization'-the ability to recognize and reason about the same concept despite variations in visual form, a basic ability of human reasoning. To address this challenge, we introduce the Visual Graph Arena (VGA), a dataset featuring six graph-based tasks designed to evaluate and improve AI systems' capacity for visual abstraction. VGA uses diverse graph layouts (e.g., Kamada-Kawai vs. planar) to test reasoning independent of visual form. Experiments with state-of-the-art vision models and multimodal LLMs reveal a striking divide: humans achieved near-perfect accuracy across tasks, while models totally failed on isomorphism detection and showed limited success in path/cycle tasks. We further identify behavioral anomalies suggesting pseudo-intelligent pattern matching rather than genuine understanding. These findings underscore fundamental limitations in current AI models for visual understanding. By isolating the challenge of representation-invariant reasoning, the VGA provides a framework to drive progress toward human-like conceptualization in AI visual models. The Visual Graph Arena is available at: \href{https://vga.csail.mit.edu/}{vga.csail.mit.edu}

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