PolyReal: A Benchmark for Real-World Polymer Science Workflows
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908934827147264 |
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| author | Liu, Wanhao Wang, Weida Xie, Jiaqing Yang, Suorong Wang, Jue Chen, Benteng Mei, Guangtao Yang, Zonglin Zhang, Shufei Mo, Yuchun Cheng, Lang Zeng, Jin Li, Houqiang Ouyang, Wanli Li, Yuqiang |
| author_facet | Liu, Wanhao Wang, Weida Xie, Jiaqing Yang, Suorong Wang, Jue Chen, Benteng Mei, Guangtao Yang, Zonglin Zhang, Shufei Mo, Yuchun Cheng, Lang Zeng, Jin Li, Houqiang Ouyang, Wanli Li, Yuqiang |
| contents | Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning chemistry, physics, biology, and engineering, is an ideal high-stakes testbed due to its diverse multimodal data. Yet, existing benchmarks related to polymer science largely overlook real-world workflows, limiting their practical utility and failing to systematically evaluate MLLMs across the full, practice-grounded lifecycle of experimentation. We introduce PolyReal, a novel multimodal benchmark grounded in real-world scientific practices to evaluate MLLMs on the full lifecycle of polymer experimentation. It covers five critical capabilities: (1) foundational knowledge application; (2) lab safety analysis; (3) experiment mechanism reasoning; (4) raw data extraction; and (5) performance & application exploration. Our evaluation of leading MLLMs on PolyReal reveals a capability imbalance. While models perform well on knowledge-intensive reasoning (e.g., Experiment Mechanism Reasoning), they drop sharply on practice-based tasks (e.g., Lab Safety Analysis and Raw Data Extraction). This exposes a severe gap between abstract scientific knowledge and its practical, context-dependent application, showing that these real-world tasks remain challenging for MLLMs. Thus, PolyReal helps address this evaluation gap and provides a practical benchmark for assessing AI systems in real-world scientific workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02934 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PolyReal: A Benchmark for Real-World Polymer Science Workflows Liu, Wanhao Wang, Weida Xie, Jiaqing Yang, Suorong Wang, Jue Chen, Benteng Mei, Guangtao Yang, Zonglin Zhang, Shufei Mo, Yuchun Cheng, Lang Zeng, Jin Li, Houqiang Ouyang, Wanli Li, Yuqiang Computer Vision and Pattern Recognition Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning chemistry, physics, biology, and engineering, is an ideal high-stakes testbed due to its diverse multimodal data. Yet, existing benchmarks related to polymer science largely overlook real-world workflows, limiting their practical utility and failing to systematically evaluate MLLMs across the full, practice-grounded lifecycle of experimentation. We introduce PolyReal, a novel multimodal benchmark grounded in real-world scientific practices to evaluate MLLMs on the full lifecycle of polymer experimentation. It covers five critical capabilities: (1) foundational knowledge application; (2) lab safety analysis; (3) experiment mechanism reasoning; (4) raw data extraction; and (5) performance & application exploration. Our evaluation of leading MLLMs on PolyReal reveals a capability imbalance. While models perform well on knowledge-intensive reasoning (e.g., Experiment Mechanism Reasoning), they drop sharply on practice-based tasks (e.g., Lab Safety Analysis and Raw Data Extraction). This exposes a severe gap between abstract scientific knowledge and its practical, context-dependent application, showing that these real-world tasks remain challenging for MLLMs. Thus, PolyReal helps address this evaluation gap and provides a practical benchmark for assessing AI systems in real-world scientific workflows. |
| title | PolyReal: A Benchmark for Real-World Polymer Science Workflows |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.02934 |