MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
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.
69a66f9d663a122a7ce66822
Jiachun Li
claimed_verified
2026-03-03T10:37:37.131Z
64b89dfa6a68a9a715df407e
https://cdn-avatars.huggingface.co/v1/production/uploads/64b89dfa6a68a9a715df407e/FpBAdClhr-oVAv11Bjwjs.jpeg
Jiachun Li
Septzzz
user
Septzzz
69a66f9d663a122a7ce66823
Shaoping Huang
69a66f9d663a122a7ce66824
Zhuoran Jin
69a66f9d663a122a7ce66825
Chenlong Zhang
69a66f9d663a122a7ce66826
Pengfei Cao
69a66f9d663a122a7ce66827
Yubo Chen
69a66f9d663a122a7ce66828
Kang Liu
69a66f9d663a122a7ce66829
Jun Zhao
MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
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 benchmark evaluates multimodal large language models' reasoning capabilities across real-life scenarios using diverse multimodal multi-image questions.
multimodal large language models multimodal multi-image reasoning comprehensive benchmark reasoning capabilities real-life scenarios multiple-choice questions reasoning types abductive reasoning analogical reasoning causal reasoning deductive reasoning inductive reasoning spatial reasoning temporal reasoning