[2603.02024] MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning

Computer Science > Computation and Language arXiv:2603.02024 (cs) [Submitted on 2 Mar 2026] Title:MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning Authors:Jiachun Li, Shaoping Huang, Zhuoran Jin, Chenlong Zhang, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao View a PDF of the paper titled MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning, by Jiachun Li and 7 other authors View PDF HTML (experimental) Abstract:Recent progress in the reasoning capabilities of multimodal large language models (MLLMs) has empowered them to address more complex tasks such as scientific analysis and mathematical reasoning. Despite their promise, MLLMs' reasoning abilities across different scenarios in real life remain largely unexplored and lack standardized benchmarks for evaluation. To address this gap, we introduce MMR-Life, a comprehensive benchmark designed to evaluate the diverse multimodal multi-image reasoning capabilities of MLLMs across real-life scenarios. MMR-Life consists of 2,646 multiple-choice questions based on 19,108 images primarily sourced from real-world contexts, comprehensively covering seven reasoning types: abductive, analogical, causal, deductive, inductive, spatial, and temporal. Unlike existing reasoning benchmarks, MMR-Life does not rely on domain-specific expertise but instead requires models to integrate information across multiple images and apply diverse reasoning abilities. The evaluation of 37 advanced ...