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    <abstract>Recent large multimodal models (LMMs) have demonstrated impressive capabilities in image understanding, yet they still struggle to perform complex reasoning on challenging multimodal problems. In this paper, we present UnAC (Understanding, Abstracting, and Checking), a multimodal prompting method that strengthens reasoning for complex multimodal tasks in LMMs (e.g., GPT-4o, Gemini 1.5, and GPT-4V). To improve image understanding and capture fine details, we propose an adaptive visual prompting strategy that enables LMMs to focus on salient regions. We further design an image-abstraction prompt to effectively extract key information from images. In addition, we introduce a gradual self-checking scheme that improves reasoning by verifying each decomposed subquestion and its answer. Extensive experiments on three public benchmarks—MathVista, MM-Vet, and MMMU—demonstrate the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning
%A Wang, Yifan
%A Fu, Yun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-fu-2026-unac
%X Recent large multimodal models (LMMs) have demonstrated impressive capabilities in image understanding, yet they still struggle to perform complex reasoning on challenging multimodal problems. In this paper, we present UnAC (Understanding, Abstracting, and Checking), a multimodal prompting method that strengthens reasoning for complex multimodal tasks in LMMs (e.g., GPT-4o, Gemini 1.5, and GPT-4V). To improve image understanding and capture fine details, we propose an adaptive visual prompting strategy that enables LMMs to focus on salient regions. We further design an image-abstraction prompt to effectively extract key information from images. In addition, we introduce a gradual self-checking scheme that improves reasoning by verifying each decomposed subquestion and its answer. Extensive experiments on three public benchmarks—MathVista, MM-Vet, and MMMU—demonstrate the effectiveness of our method.
%U https://aclanthology.org/2026.findings-acl.1196/
%P 23884-23893

Markdown (Informal)

[UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning](https://aclanthology.org/2026.findings-acl.1196/) (Wang & Fu, Findings 2026)

• UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning (Wang & Fu, Findings 2026)

ACL

• Yifan Wang and Yun Fu. 2026. UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23884–23893, San Diego, California, United States. Association for Computational Linguistics.