AI RESEARCH
SO-Bench: A Structural Output Evaluation of Multimodal LLMs
arXiv CS.CV
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ArXi:2511.21750v3 Announce Type: replace 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.