📢[2026-02-27] Officially open-sourced the paper, dataset, and code.

🎉[2026-01-26] Our paper was accepted to ICLR 2026.

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 models highlights the substantial challenge posed by MMR-Life. Even top models like GPT-5 achieve only 58% accuracy and display considerable variance in performance across reasoning types. Moreover, we analyze the reasoning paradigms of existing MLLMs, exploring how factors such as thinking length, reasoning method, and reasoning type affect their performance. In summary, MMR-Life establishes a comprehensive foundation for evaluating, analyzing, and improving the next generation of multimodal reasoning systems.

MMR-Life consists of 2,646 multiple-choice questions based on 19,108 images, comprehensively covering 7 reasoning types (i.e., abductive, analogical, causal, deductive, inductive, spatial, and temporal) and 21 tasks. Each task is based on a set of multi-images, predominantly sourced from real-life contexts, such as domestic life, daily dining, and sports activities. The questions in our benchmark do not require domain-specific expertise but instead ask models to extract key information from multiple real-life images and derive new conclusions. This design aligns MMR-Life more closely with the reasoning types found in everyday life.