With the rapid advancements in multimodal large language models, evaluating their multimodal mathematical capabilities continues to receive wide attention. Although datasets such as MathVista have been introduced for evaluating mathematical capabilities in multimodal scenarios, there remains a lack of evaluation tools and datasets tailored for fine-grained assessment in Chinese K12 education. To systematically evaluate the ability of multimodal large models to solve Chinese multimodal mathematical problems, we propose a Chinese Multi-modal Math Skill Evaluation Benchmark (CMMaTH), containing 23,856 multimodal K12 math related questions, making it the largest Chinese multimodal mathematical problem benchmark to date. CMMaTH includes questions ranging from elementary to high school levels, offering greater diversity in problem types, solution goals, visual elements, detailed knowledge points, and standard solution annotations. To facilitate stable, fast, and cost-free model evaluation, we have developed an open-source tool called GradeGPT, which is integrated with the CMMaTH dataset. Our data and code are available at https://github.com/zzli2022/CMMaTH.

Geometry problem solving (GPS) is a high-level mathematical reasoning requiring the capacities of multi-modal fusion and geometric knowledge application. Recently, neural solvers have shown great potential in GPS but still be short in diagram presentation and modal fusion. In this work, we convert diagrams into basic textual clauses to describe diagram features effectively, and propose a new neural solver called PGPSNet to fuse multi-modal information efficiently. Combining structural and semantic pre-training, data augmentation and self-limited decoding, PGPSNet is endowed with rich knowledge of geometry theorems and geometric representation, and therefore promotes geometric understanding and reasoning. In addition, to facilitate the research of GPS, we build a new large-scale and fine-annotated GPS dataset named PGPS9K, labeled with both fine-grained diagram annotation and interpretable solution program. Experiments on PGPS9K and an existing dataset Geometry3K validate the superiority of our method over the state-of-the-art neural solvers. Our code, dataset and appendix material are available at \url{https://github.com/mingliangzhang2018/PGPS}.

Diagram parsing is an important foundation for geometry problem solving, attracting increasing attention in the field of intelligent education and document image understanding. Due to the complex layout and between-primitive relationship, plane geometry diagram parsing (PGDP) is still a challenging task deserving further research and exploration. An appropriate dataset is critical for the research of PGDP. Although some datasets with rough annotations have been proposed to solve geometric problems, they are either small in scale or not publicly available. The rough annotations also make them not very useful. Thus, we propose a new large-scale geometry diagram dataset named PGDP5K and a novel annotation method. Our dataset consists of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types. Different from previous datasets, our PGDP5K dataset is labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships. What is more, combined with above annotations and geometric prior knowledge, it can generate intelligible geometric propositions automatically and uniquely. We performed experiments on PGDP5K and IMP-Geometry3K datasets reveal that the state-of-the-art (SOTA) method achieves only 66.07% F1 value. This shows that PGDP5K presents a challenge for future research. Our dataset is available at http://www.nlpr.ia.ac.cn/databases/CASIA-PGDP5K/.

Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning. Specifically, a modified instance segmentation method is proposed to extract geometric primitives, and the graph neural network (GNN) is leveraged to realize relation parsing and primitive classification incorporating geometric features and prior knowledge. All the modules are integrated into an end-to-end model called PGDPNet to perform all the sub-tasks simultaneously. In addition, we build a new large-scale geometry diagram dataset named PGDP5K with primitive level annotations. Experiments on PGDP5K and an existing dataset IMP-Geometry3K show that our model outperforms state-of-the-art methods in four sub-tasks remarkably. Our code, dataset and appendix material are available at https://github.com/mingliangzhang2018/PGDP.