6 papers
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language
Peijie Wang, Ming-Liang Zhang, Jun Cao +10
Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding f…
From System 1 to System 2: A Survey of Reasoning Large Language Models
Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang +18
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in qu…
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…
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…
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…
LANS: A Layout-Aware Neural Solver for Plane Geometry Problem
Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin +1
Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision…