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 ...
| Authors | Jiachun Li, Shaoping Huang, Zhuoran Jin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
Models and datasets referencing this paper
- Septzzz/MMR-LifeDataset
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