UnAC: Adaptive Visual Prompting with Abstraction and Stepwise Checking for Complex Multimodal Reasoning
Year: 2026
Type: conference-paper
Abstract: Although recent LMMs have become much stronger at visual perception, they remain unreliable on problems that require multi-step reasoning over visual evidence.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.
Authors: Yifan Wang, Yun Fu
Institution: Northeastern University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166798998