SO-Bench: A Structural Output Evaluation of Multimodal LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915870467424256 |
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| author | Feng, Di Ma, Kaixin Nan, Feng Chen, Haofeng Zhai, Bohan Griffiths, David Gao, Mingfei Gan, Zhe Verma, Eshan Yang, Yinfei Chen, Zhifeng Dehghan, Afshin |
| author_facet | Feng, Di Ma, Kaixin Nan, Feng Chen, Haofeng Zhai, Bohan Griffiths, David Gao, Mingfei Gan, Zhe Verma, Eshan Yang, Yinfei Chen, Zhifeng Dehghan, Afshin |
| contents | Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schemas. Despite recent progress in structured generation in textual domain, there is still no benchmark that systematically evaluates schema-grounded information extraction and reasoning over visual inputs. In this work, we conduct a comprehensive study of visual structural output capabilities for MLLMs with our carefully designed SO-Bench benchmark. Covering four visual domains, including UI screens, natural images, documents, and charts, SO-Bench is built from over 6.5K diverse JSON schemas and 1.8K curated image-schema pairs with human-verified quality. Benchmarking experiments on open-sourced and frontier proprietary models reveal persistent gaps in predicting accurate, schema compliant outputs, highlighting the need for better multimodal structured reasoning. Beyond benchmarking, we further conduct training experiments to largely improve the model's structured output capability. We make the benchmark and evaluation publicly available at https://github.com/apple/ml-sobench |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SO-Bench: A Structural Output Evaluation of Multimodal LLMs Feng, Di Ma, Kaixin Nan, Feng Chen, Haofeng Zhai, Bohan Griffiths, David Gao, Mingfei Gan, Zhe Verma, Eshan Yang, Yinfei Chen, Zhifeng Dehghan, Afshin Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Robotics Multimodal large language models (MLLMs) are increasingly deployed in real-world, agentic settings where outputs must not only be correct, but also conform to predefined data schemas. Despite recent progress in structured generation in textual domain, there is still no benchmark that systematically evaluates schema-grounded information extraction and reasoning over visual inputs. In this work, we conduct a comprehensive study of visual structural output capabilities for MLLMs with our carefully designed SO-Bench benchmark. Covering four visual domains, including UI screens, natural images, documents, and charts, SO-Bench is built from over 6.5K diverse JSON schemas and 1.8K curated image-schema pairs with human-verified quality. Benchmarking experiments on open-sourced and frontier proprietary models reveal persistent gaps in predicting accurate, schema compliant outputs, highlighting the need for better multimodal structured reasoning. Beyond benchmarking, we further conduct training experiments to largely improve the model's structured output capability. We make the benchmark and evaluation publicly available at https://github.com/apple/ml-sobench |
| title | SO-Bench: A Structural Output Evaluation of Multimodal LLMs |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Robotics |
| url | https://arxiv.org/abs/2511.21750 |