Lune

ACL Findings2025

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

Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu

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-theart 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 seven 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.

Ask Lune

Ask anything. The answer quotes the paper.

Lune reads all of it and the papers around it, then shows you the passages behind every answer.

Questions to start from

Lune answers from

the full paper · 11 top-tier papers that cite it · related work

Get the answer

Free to start. No credit card required.

Cited by top-tier papers11

  1. MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical ProblemsShuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu et al.ACL 2026 · 9 citations
  2. A Survey of Deep Learning for Geometry Problem SolvingJianzhe Ma, Wenxuan Wang, Qin JinACL 2026 · 5 citations
  3. CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem SolvingShuhang Chen, Yunqiu Xu, Junjie Xie, Aojun Lu et al.ICLR 2026 · 4 citations
  4. GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary LinesYumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu et al.ACL 2026 · 3 citations
  5. Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative SignalsZihan Dong, Zhixian Zhang, Yang Zhou, Can Jin et al.ICML 2026 · 2 citations

Builds on89

  1. Solving Quantitative Reasoning Problems with Language ModelsAitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer et al.NeurIPS 2022 · 2,039 citations
  2. MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual ContextsPan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu et al.ICLR 2024 · 1,472 citations
  3. ViperGPT: Visual Inference via Python Execution for ReasoningDídac Surís, Sachit Menon, Carl VondrickICCV 2023 · 732 citations
  4. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsLonghui Yu, Weisen Jiang, Han Shi, Jincheng Yu et al.ICLR 2024 · 637 citations
  5. MAmmoTH: Building Math Generalist Models through Hybrid Instruction TuningXiang Yue, Xingwei Qu, Ge Zhang, Yao Fu et al.ICLR 2024 · 558 citations

Related papers

  1. MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGIHuanjin Yao, Jiaxing Huang, Yawen Qiu, Michael K. Chen et al.ICCV 2025 · 4 citations
  2. ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error DetectionYibo Yan, Shen Wang, Jiahao Huo, Hang Li et al.ACL Findings 2026 · 1 citation
  3. A Survey of Deep Learning for Mathematical ReasoningPan Lu, Liang Qiu, Wenhao Yu, Sean Welleck et al.ACL 2023 · 43 citations
  4. Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning BenchmarkYunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li et al.ICML 2025
  5. Position: Multimodal Large Language Models Can Significantly Advance Scientific ReasoningYibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye et al.ACL Findings 2026 · 2 citations
Dusk over the sea between two cliffs drawn in fine vertical lines