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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

摘要

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.

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引用它的顶会 Paper11

  1. MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical ProblemsShuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu 等ACL 2026 · 9 次引用
  2. A Survey of Deep Learning for Geometry Problem SolvingJianzhe Ma, Wenxuan Wang, Qin JinACL 2026 · 5 次引用
  3. CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem SolvingShuhang Chen, Yunqiu Xu, Junjie Xie, Aojun Lu 等ICLR 2026 · 4 次引用
  4. GeoLaux: A Benchmark for Evaluating MLLMs' Geometry Performance on Long-Step Problems Requiring Auxiliary LinesYumeng Fu, Jiayin Zhu, Lingling Zhang, Wenjun Wu 等ACL 2026 · 3 次引用
  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 等ICML 2026 · 2 次引用

它引用的 Paper89

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

相关 Paper

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