Evaluating Cross-Modal Reasoning Ability and Problem Characteristics with Multimodal Item Response Theory

• URL: http://arxiv.org/abs/2603.02663v1
• Date: Tue, 03 Mar 2026 06:51:08 GMT
• Title: Evaluating Cross-Modal Reasoning Ability and Problem Characteristics with Multimodal Item Response Theory
• Authors: Shunki Uebayashi, Kento Masui, Kyohei Atarashi, Han Bao, Hisashi Kashima, Naoto Inoue, Mayu Otani, Koh Takeuchi,
• Abstract summary: Benchmarks for Multimodal Large Language Models should measure their ability for cross-modal integration.<n>Current benchmarks are filled with shortcut questions, which can be solved using only a single modality.<n>We introduce a multi-modal and multidimensional item response theory framework (M3IRT) that extends classical IRT.<n>M3IRT estimates cross-modal ability of MLLMs and each question's cross-modal difficulty, enabling compact, high-quality subsets.
• Score: 22.63245796446805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have recently emerged as general architectures capable of reasoning over diverse modalities. Benchmarks for MLLMs should measure their ability for cross-modal integration. However, current benchmarks are filled with shortcut questions, which can be solved using only a single modality, thereby yielding unreliable rankings. For example, in vision-language cases, we can find the correct answer without either the image or the text. These low-quality questions unnecessarily increase the size and computational requirements of benchmarks. We introduce a multi-modal and multidimensional item response theory framework (M3IRT) that extends classical IRT by decomposing both model ability and item difficulty into image-only, text-only, and cross-modal components. M3IRT estimates cross-modal ability of MLLMs and each question's cross-modal difficulty, enabling compact, high-quality subsets that better reflect multimodal reasoning. Across 24 VLMs on three benchmarks, M3IRT prioritizes genuinely cross-modal questions over shortcuts and preserves ranking fidelity even when 50% of items are artificially generated low-quality questions, thereby reducing evaluation cost while improving reliability. M3IRT thus offers a practical tool for assessing cross-modal reasoning and refining multimodal benchmarks.

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