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        <title>Probing Logical Reasoning of MLLMs in Scientific Diagrams</title>
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    <abstract>We examine how multimodal large language models (MLLMs) perform logical inference grounded in visual information. We first construct a dataset of food web/chain images, along with questions that follow seven structured templates with progressively more complex reasoning involved. We show that complex reasoning about entities in the images remains challenging (even with elaborate prompts) and that visual information is underutilized.</abstract>
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%0 Conference Proceedings
%T Probing Logical Reasoning of MLLMs in Scientific Diagrams
%A Wang, Yufei
%A Kovashka, Adriana
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F wang-kovashka-2025-probing
%X We examine how multimodal large language models (MLLMs) perform logical inference grounded in visual information. We first construct a dataset of food web/chain images, along with questions that follow seven structured templates with progressively more complex reasoning involved. We show that complex reasoning about entities in the images remains challenging (even with elaborate prompts) and that visual information is underutilized.
%U https://aclanthology.org/2025.emnlp-main.542/
%P 10717-10729

Markdown (Informal)

[Probing Logical Reasoning of MLLMs in Scientific Diagrams](https://aclanthology.org/2025.emnlp-main.542/) (Wang & Kovashka, EMNLP 2025)

• Probing Logical Reasoning of MLLMs in Scientific Diagrams (Wang & Kovashka, EMNLP 2025)

ACL

• Yufei Wang and Adriana Kovashka. 2025. Probing Logical Reasoning of MLLMs in Scientific Diagrams. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 10717–10729, Suzhou, China. Association for Computational Linguistics.