MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models

• URL: http://arxiv.org/abs/2510.26937v1
• Date: Thu, 30 Oct 2025 18:49:06 GMT
• Title: MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models
• Authors: Zimeng Huang, Jinxin Ke, Xiaoxuan Fan, Yufeng Yang, Yang Liu, Liu Zhonghan, Zedi Wang, Junteng Dai, Haoyi Jiang, Yuyu Zhou, Keze Wang, Ziliang Chen,
• Abstract summary: We aim to evaluate a fundamental yet underexplored intelligence: association.<n> MM-OPERA is a systematic benchmark with 11,497 instances across two open-ended tasks.<n>It challenges LVLMs to resemble the spirit of divergent thinking and convergent associative reasoning.
• Score: 15.929002709503921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matching. In this work, we aim to evaluate a fundamental yet underexplored intelligence: association, a cornerstone of human cognition for creative thinking and knowledge integration. Current benchmarks, often limited to closed-ended tasks, fail to capture the complexity of open-ended association reasoning vital for real-world applications. To address this, we present MM-OPERA, a systematic benchmark with 11,497 instances across two open-ended tasks: Remote-Item Association (RIA) and In-Context Association (ICA), aligning association intelligence evaluation with human psychometric principles. It challenges LVLMs to resemble the spirit of divergent thinking and convergent associative reasoning through free-form responses and explicit reasoning paths. We deploy tailored LLM-as-a-Judge strategies to evaluate open-ended outputs, applying process-reward-informed judgment to dissect reasoning with precision. Extensive empirical studies on state-of-the-art LVLMs, including sensitivity analysis of task instances, validity analysis of LLM-as-a-Judge strategies, and diversity analysis across abilities, domains, languages, cultures, etc., provide a comprehensive and nuanced understanding of the limitations of current LVLMs in associative reasoning, paving the way for more human-like and general-purpose AI. The dataset and code are available at https://github.com/MM-OPERA-Bench/MM-OPERA.

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