@inproceedings{ding-etal-2026-decoding,
title = "Decoding Scientific Experimental Images: The {SPUR} Benchmark for Perception, Understanding, and Reasoning",
author = "Ding, Junpeng and
Tang, Zichen and
E, Haihong and
Ji, Mengyuan and
Liu, Yang and
Tian, Haolin and
Sun, Haiyang and
Sun, Pengqi and
Xu, Yang and
Liu, Yichen and
Gao, Haocheng and
Xi, Zijie and
Jiang, Ruomeng and
Zhao, Peizhi and
Li, Rongjin and
Li, Yuanze and
Liu, Jiacheng and
Yang, Zhongjun and
Chen, Jintong and
Lin, Siying",
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.585/",
pages = "12859--12882",
ISBN = "979-8-89176-390-6",
abstract = "We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. SPUR features three key innovations: (1) Panel-Level Fine-Grained Perception: evaluating the visual perception of multimodal large language models (MLLMs) across three dimensions (numerical, morphological, and information localization) on six fine-grained panel types; (2) Cross-Panel Relation Understanding: utilizing complex images with an average of 14.3 panels per sample to evaluate MLLMs' ability to decipher intricate cross-panel relations; (3) Expert-Level Reasoning: assessment of qualitative and quantitative reasoning across five experimental paradigms to determine if models can infer conclusions from evidence as human experts do. Comprehensive evaluation of 20 MLLMs and four multimodal Chain-of-Thought (MCoT) methods reveals that current models fall significantly short of the expert-level requirements for scientific image interpretation, underscoring a critical bottleneck in AI for Science (AI4S) research."
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<abstract>We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. SPUR features three key innovations: (1) Panel-Level Fine-Grained Perception: evaluating the visual perception of multimodal large language models (MLLMs) across three dimensions (numerical, morphological, and information localization) on six fine-grained panel types; (2) Cross-Panel Relation Understanding: utilizing complex images with an average of 14.3 panels per sample to evaluate MLLMs’ ability to decipher intricate cross-panel relations; (3) Expert-Level Reasoning: assessment of qualitative and quantitative reasoning across five experimental paradigms to determine if models can infer conclusions from evidence as human experts do. Comprehensive evaluation of 20 MLLMs and four multimodal Chain-of-Thought (MCoT) methods reveals that current models fall significantly short of the expert-level requirements for scientific image interpretation, underscoring a critical bottleneck in AI for Science (AI4S) research.</abstract>
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%0 Conference Proceedings
%T Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning
%A Ding, Junpeng
%A Tang, Zichen
%A E, Haihong
%A Ji, Mengyuan
%A Liu, Yang
%A Tian, Haolin
%A Sun, Haiyang
%A Sun, Pengqi
%A Xu, Yang
%A Liu, Yichen
%A Gao, Haocheng
%A Xi, Zijie
%A Jiang, Ruomeng
%A Zhao, Peizhi
%A Li, Rongjin
%A Li, Yuanze
%A Liu, Jiacheng
%A Yang, Zhongjun
%A Chen, Jintong
%A Lin, Siying
%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 ding-etal-2026-decoding
%X We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. SPUR features three key innovations: (1) Panel-Level Fine-Grained Perception: evaluating the visual perception of multimodal large language models (MLLMs) across three dimensions (numerical, morphological, and information localization) on six fine-grained panel types; (2) Cross-Panel Relation Understanding: utilizing complex images with an average of 14.3 panels per sample to evaluate MLLMs’ ability to decipher intricate cross-panel relations; (3) Expert-Level Reasoning: assessment of qualitative and quantitative reasoning across five experimental paradigms to determine if models can infer conclusions from evidence as human experts do. Comprehensive evaluation of 20 MLLMs and four multimodal Chain-of-Thought (MCoT) methods reveals that current models fall significantly short of the expert-level requirements for scientific image interpretation, underscoring a critical bottleneck in AI for Science (AI4S) research.
%U https://aclanthology.org/2026.acl-long.585/
%P 12859-12882
Markdown (Informal)
[Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning](https://aclanthology.org/2026.acl-long.585/) (Ding et al., ACL 2026)
ACL
- Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, and Siying Lin. 2026. Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12859–12882, San Diego, California, United States. Association for Computational Linguistics.