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MMR-Life: Piecing Together Real-life Scenes for Multimodal Multi-image Reasoning
arXiv – CS AI|Jiachun Li, Shaoping Huang, Zhuoran Jin, Chenlong Zhang, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao||3 views
🤖AI Summary
Researchers introduced MMR-Life, a comprehensive benchmark with 2,646 questions and 19,108 real-world images to evaluate multimodal reasoning capabilities of AI models. Even top models like GPT-5 achieved only 58% accuracy, highlighting significant challenges in real-world multimodal reasoning across seven different reasoning types.
Key Takeaways
- →MMR-Life benchmark tests AI models on seven reasoning types using real-world images and scenarios rather than domain-specific expertise.
- →The benchmark includes 2,646 multiple-choice questions based on 19,108 images from real-world contexts.
- →Top AI models like GPT-5 achieved only 58% accuracy, showing substantial room for improvement in multimodal reasoning.
- →The evaluation covered 37 advanced models and revealed considerable variance in performance across different reasoning types.
- →The research establishes a foundation for evaluating and improving next-generation multimodal reasoning systems.
Read Original →via arXiv – CS AI
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