collaborators

6 papers

cs.AI2025

MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts

Peijie Wang, Zhong-Zhi Li, Fei Yin +3

Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimod…

cs.AI2025

LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating

Chao Deng, Jiale Yuan, Pi Bu +8

Large vision language models (LVLMs) have improved the document understanding capabilities remarkably, enabling the handling of complex document elements, longer contexts, and a wi…

cs.CG2025

SOLIDGEO: Measuring Multimodal Spatial Math Reasoning in Solid Geometry

Peijie Wang, Chao Yang, Zhong-Zhi Li +6

Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing mul…

cs.AI2024

Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram

Ming-Liang Zhang, Zhong-Zhi Li, Fei Yin +2

Geometry problem solving (GPS) requires capacities of multi-modal understanding, multi-hop reasoning and theorem knowledge application. In this paper, we propose a neural-symbolic…

cs.CL2024

CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models

Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin +7

Due to the rapid advancements in multimodal large language models, evaluating their multimodal mathematical capabilities continues to receive wide attention. Despite the datasets l…

cs.AI2024

GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving

Jiaxin Zhang, Zhongzhi Li, Mingliang Zhang +3

Recent advancements in large language models (LLMs) and multi-modal models (MMs) have demonstrated their remarkable capabilities in problem-solving. Yet, their proficiency in tackl…