title: MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical
    Problems
summary: Despite impressive performance across diverse tasks, Multimodal Large
  Language Models (MLLMs) have yet to fully demonstrate their potential in visual
  mathematical problem-solving, particularly in accurately perceiving and
  interpreting diagrams. Inspired by typical processes of humans, we hypothesize
  that the perception capabilities to extract meaningful information from
  diagrams is crucial, as it directly impacts subsequent inference processes. To
  validate this hypothesis, we developed FlowVerse, a comprehensive benchmark
  that categorizes all information used during problem-solving into four
  components, which are then combined into six problem versions for evaluation.
  Our preliminary results on FlowVerse reveal that existing MLLMs exhibit
  substantial limitations when extracting essential information and reasoned
  property from diagrams and performing complex reasoning based on these visual
  inputs. In response, we introduce MathFlow, a modular problem-solving pipeline
  that decouples perception and inference into distinct stages, thereby
  optimizing each independently. Given the perceptual limitations observed in
  current MLLMs, we trained MathFlow-P-7B as a dedicated perception model.
  Experimental results indicate that MathFlow-P-7B yields substantial performance
  gains when integrated with various closed-source and open-source inference
  models. This demonstrates the effectiveness of the MathFlow pipeline and its
  compatibility to diverse inference frameworks. The FlowVerse benchmark and code
  are available at https://github.com/MathFlow-zju/MathFlow.
paper_authors._id: 67e0d11eb04d9e83682f222a
paper_authors.name: Felix Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:43:40.264Z
paper_authors._id: 642c1581fa6e17ba1e28f075
paper_authors.avatarUrl: /avatars/e57ffb69a4882ff95717b484bcfb71ff.svg
paper_authors.fullname: Felix Chen
paper_authors.type: user
paper_authors.user: felixchen
paper_authors._id: 67e0d11eb04d9e83682f222b
paper_authors.name: Hangjie Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:06:59.943Z
paper_authors._id: 649d54b314afbb10ce2a9eeb
paper_authors.avatarUrl: /avatars/15c325d8c2273ff63569f23015e98486.svg
paper_authors.fullname: Hangjie Yuan
paper_authors.type: user
paper_authors.user: JacobYuan
paper_authors._id: 67e0d11eb04d9e83682f222c
paper_authors.name: Yunqiu Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:45:05.982Z
paper_authors._id: 646c77911ee398a4e9404b8b
paper_authors.avatarUrl: /avatars/05d1ea421dd4f3e2fd47cbe99fc52933.svg
paper_authors.fullname: Yunqiu Xu
paper_authors.type: user
paper_authors.user: Yunqiu
paper_authors._id: 67e0d11eb04d9e83682f222d
paper_authors.name: Tao Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:44:22.418Z
paper_authors._id: 66269e8b3385ba4e6ed75834
paper_authors.avatarUrl: /avatars/a3188fbb4526f62db92dc65ce42598bc.svg
paper_authors.fullname: Tao Feng
paper_authors.type: user
paper_authors.user: taofeng
paper_authors._id: 67e0d11eb04d9e83682f222e
paper_authors.name: Jun Cen
paper_authors._id: 67e0d11eb04d9e83682f222f
paper_authors.name: Pengwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:44:07.900Z
paper_authors._id: 66f0e9bb16362f8e97b601e7
paper_authors.avatarUrl: /avatars/2d76a832dd1b8708ac2f3c168cb2ceaf.svg
paper_authors.fullname: pengwei liu
paper_authors.type: user
paper_authors.user: wayyyy
paper_authors._id: 67e0d11eb04d9e83682f2230
paper_authors.name: Zeying Huang
paper_authors._id: 67e0d11eb04d9e83682f2231
paper_authors.name: Yi Yang
paper_title: MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical
    Problems
paper_summary: Despite impressive performance across diverse tasks, Multimodal Large
  Language Models (MLLMs) have yet to fully demonstrate their potential in visual
  mathematical problem-solving, particularly in accurately perceiving and
  interpreting diagrams. Inspired by typical processes of humans, we hypothesize
  that the perception capabilities to extract meaningful information from
  diagrams is crucial, as it directly impacts subsequent inference processes. To
  validate this hypothesis, we developed FlowVerse, a comprehensive benchmark
  that categorizes all information used during problem-solving into four
  components, which are then combined into six problem versions for evaluation.
  Our preliminary results on FlowVerse reveal that existing MLLMs exhibit
  substantial limitations when extracting essential information and reasoned
  property from diagrams and performing complex reasoning based on these visual
  inputs. In response, we introduce MathFlow, a modular problem-solving pipeline
  that decouples perception and inference into distinct stages, thereby
  optimizing each independently. Given the perceptual limitations observed in
  current MLLMs, we trained MathFlow-P-7B as a dedicated perception model.
  Experimental results indicate that MathFlow-P-7B yields substantial performance
  gains when integrated with various closed-source and open-source inference
  models. This demonstrates the effectiveness of the MathFlow pipeline and its
  compatibility to diverse inference frameworks. The FlowVerse benchmark and code
  are available at https://github.com/MathFlow-zju/MathFlow.
paper_ai_summary: MathFlow, a modular problem-solving pipeline, improves MLLMs' performance in visual mathematical problem-solving by decoupling perception and reasoning.
paper_ai_keywords: Multimodal Large Language Models MLLMs visual mathematical problem-solving diagrams FlowVerse MathFlow perception inference MathFlow-P-7B