A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025)

Abstract: Mathematical reasoning, a core aspect of human cognition, is vital across many domains, from educational problem-solving to scientific advancements. As artificial general intelligence (AGI) progresses, integrating large language models (LLMs) with mathematical reasoning tasks is becoming increasingly significant. This survey provides **the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models (MLLMs)**. We review over 200 studies published since 2021, and examine the state-of-the-art developments in Math-LLMs, with a focus on multimodal settings. We categorize the field into three dimensions: benchmarks, methodologies, and challenges. In particular, we explore multimodal mathematical reasoning pipeline, as well as the role of (M)LLMs and the associated methodologies. Finally, we identify five major challenges hindering the realization of AGI in this domain, offering insights into the future direction for enhancing multimodal reasoning capabilities. This survey serves as a critical resource for the research community in advancing the capabilities of LLMs to tackle complex multimodal reasoning tasks.

## introduction mathematical reasoning is a critical aspect of human cognitive ability, involving the process of deriving conclusions from a set of premises through logical and systematic thinking @xcite . it plays an essential role in a wide range of applications, from problem-solving in education to advanced scientific discoveries. as artificial general intelligence (agi) continues to advance @xcite , the integration of large language models (llms) with mathematical reasoning tasks becomes increasingly significant. these models, with their impressive capabilities in * corresponding author survey venue & year scope multimodal llm (o'halloran, 2015) jmb'15 mm4math ✔ (hegedus and tall, 2015) irme'15 mm4math ✔ @xcite acl'22 dl4math @xcite arxiv'23 llm4edu ✔ @xcite arxiv'23 llm4edu ✔ @xcite colm'24 dl4tp @xcite eacl'24 llm4math ✔ @xcite ijmlc'24 llm4edu ✔ @xcite arxiv'24 llm4edu ✔ ours acl'25 mllm4math ✔ ✔ math-llm progress. figure 2 illustrates that, driven by the rapid development of llms since 2021, the number of math-specific llms (math-llms) has grown steadily, alongside enhanced support for multilingual and multimodal capabilities (more details in appendix a). the landscape was marked by the introduction of models like @xcite and minerva @xcite , with hypertree proof search @xcite and jiuzhang 1.0 @xcite highlighting advancements in theorem proving and mathematical question understanding capabilities, respectively. year 2023 saw a surge in diversity and specialization, alongside multimodal support from models like skywork-math @xcite . in year 2024, there was a clear focus on enhancing mathematical instruction (e.g., qwen2.5-math @xcite ) and proof (e.g., deepseek-proof @xcite ) capabilities. the year also witnessed the emergence of math-llms with a vision component, such as mathglm-vision @xcite . scope. previous surveys have not fully captured the progress and challenges of mathematical reasoning in the age of mllms. as indicated in structure. in this paper, we survey over 200 publications from the ai community since 2021 related to (m)llm-based mathematical reasoning, and summarize the progress of math-llms. we first approach the field from the benchmark perspective, analyzing the llm-based mathematical reasoning task through four key aspects: basic focus, task, evaluation, and training data (section 2). subsequently, we explore the roles that (m)llms play in mathematical reasoning, categorizing them as reasoners, enhancers, and planners (section 3). finally, we identify seven core challenges that the mathematical reasoning faces in the era of mllms (section 4). this survey aims to provide the com-munity with comprehensive insights for advancing multimodal reasoning capabilities of llms. ## challenges in the realm of mllms for mathematical reasoning, the following key challenges persist that hinder their full potential. addressing these challenges is essential for advancing mllms toward more robust and flexible systems that can better support mathematical reasoning in real-world settings. ## conclusion in this survey, we have provided a comprehensive overview of the progress and challenges in mathematical reasoning within the context of mllms. we highlighted the significant advances in the development of math-llms and the growing importance of multimodal integration for solving complex reasoning tasks. we identified five key challenges that are crucial for the continued development of agi systems capable of performing sophisticated mathematical reasoning tasks. as research continues to advance, it is essential to focus on these challenges to unlock the full potential of llms in multimodal settings. we hope this survey provides insights to guide future llm research, ultimately leading to more effective and human-like mathematical reasoning capabilities in ai systems.