Mv-Math: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts

Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encoun...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 19541 - 19551
Hauptverfasser:	Wang, Peijie, Li, Zhong-Zhi, Yin, Fei, Ran, Dekang, Liu, Cheng-Lin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 10.06.2025
Schlagworte:	Benchmark testing Codes Cognition Computational modeling Computer vision Large language models math reasoning Mathematical models multi-image reasoning multimodal reasoning Pattern recognition Systematics Visualization
ISSN:	1063-6919
Online-Zugang:	Volltext
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