OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917429840445440 |
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| author | Chen, Qiguang Luan, Chengyu Wu, Jiajun Yu, Qiming Yang, Yi Li, Yizhuo Tong, Jingqi Feng, Xiachong Qin, Libo Che, Wanxiang |
| author_facet | Chen, Qiguang Luan, Chengyu Wu, Jiajun Yu, Qiming Yang, Yi Li, Yizhuo Tong, Jingqi Feng, Xiachong Qin, Libo Che, Wanxiang |
| contents | Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks for these models often emphasize single-image analysis and fail to exploit contextual information across multiple images. We present OMIBench, a benchmark designed to evaluate Olympiad-level reasoning when the required evidence is distributed over multiple images. It contains problems from biology, chemistry, mathematics, and physics Olympiads, together with manually annotated rationales and evaluation protocols for both exact and semantic answer matching. Across extensive experiments on OMIBench, we observe meaningful performance gaps in existing models. Even the strongest LVLMs, such as Gemini-3-Pro, attain only about 50% on the benchmark. These results position OMIBench as a focused resources for studying and improving multi-image reasoning in LVLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20806 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model Chen, Qiguang Luan, Chengyu Wu, Jiajun Yu, Qiming Yang, Yi Li, Yizhuo Tong, Jingqi Feng, Xiachong Qin, Libo Che, Wanxiang Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks for these models often emphasize single-image analysis and fail to exploit contextual information across multiple images. We present OMIBench, a benchmark designed to evaluate Olympiad-level reasoning when the required evidence is distributed over multiple images. It contains problems from biology, chemistry, mathematics, and physics Olympiads, together with manually annotated rationales and evaluation protocols for both exact and semantic answer matching. Across extensive experiments on OMIBench, we observe meaningful performance gaps in existing models. Even the strongest LVLMs, such as Gemini-3-Pro, attain only about 50% on the benchmark. These results position OMIBench as a focused resources for studying and improving multi-image reasoning in LVLMs. |
| title | OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.20806 |