@inproceedings{chen-etal-2026-omibench,
title = "{OMIB}ench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models",
author = "Chen, Qiguang and
Luan, Chengyu and
Wu, Jiajun and
Yu, Qiming and
Yang, Yi and
Li, Yizhuo and
Tong, Jingqi and
Feng, Xiachong and
Qin, Libo and
Che, Wanxiang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2090/",
pages = "45100--45135",
ISBN = "979-8-89176-390-6",
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, 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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<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, 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.</abstract>
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%0 Conference Proceedings
%T OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models
%A Chen, Qiguang
%A Luan, Chengyu
%A Wu, Jiajun
%A Yu, Qiming
%A Yang, Yi
%A Li, Yizhuo
%A Tong, Jingqi
%A Feng, Xiachong
%A Qin, Libo
%A Che, Wanxiang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-omibench
%X 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.
%U https://aclanthology.org/2026.acl-long.2090/
%P 45100-45135
Markdown (Informal)
[OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models](https://aclanthology.org/2026.acl-long.2090/) (Chen et al., ACL 2026)
ACL
- Qiguang Chen, Chengyu Luan, Jiajun Wu, Qiming Yu, Yi Yang, Yizhuo Li, Jingqi Tong, Xiachong Feng, Libo Qin, and Wanxiang Che. 2026. OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45100–45135, San Diego, California, United States. Association for Computational Linguistics.