Paper Detail
Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, Wanxiang Che
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.
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@article{chen2026omibench,
title = {OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model},
author = {Qiguang Chen and Chengyu Luan and Jiajun Wu and Qiming Yu and Yi Yang and Yizhuo Li and Jingqi Tong and Xiachong Feng and Libo Qin and Wanxiang Che},
year = {2026},
abstract = {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, mathematic},
url = {https://arxiv.org/abs/2604.20806},
keywords = {cs.CV, cs.AI, cs.CL},
eprint = {2604.20806},
archiveprefix = {arXiv},
}
{}