MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical
  Problems
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.
67e0d11eb04d9e83682f222a
Felix Chen
admin_assigned
2025-03-24T12:43:40.264Z
642c1581fa6e17ba1e28f075
/avatars/e57ffb69a4882ff95717b484bcfb71ff.svg
Felix Chen
user
felixchen
67e0d11eb04d9e83682f222b
Hangjie Yuan
claimed_verified
2025-03-24T08:06:59.943Z
649d54b314afbb10ce2a9eeb
/avatars/15c325d8c2273ff63569f23015e98486.svg
Hangjie Yuan
user
JacobYuan
67e0d11eb04d9e83682f222c
Yunqiu Xu
admin_assigned
2025-03-24T12:45:05.982Z
646c77911ee398a4e9404b8b
/avatars/05d1ea421dd4f3e2fd47cbe99fc52933.svg
Yunqiu Xu
user
Yunqiu
67e0d11eb04d9e83682f222d
Tao Feng
admin_assigned
2025-03-24T12:44:22.418Z
66269e8b3385ba4e6ed75834
/avatars/a3188fbb4526f62db92dc65ce42598bc.svg
Tao Feng
user
taofeng
67e0d11eb04d9e83682f222e
Jun Cen
67e0d11eb04d9e83682f222f
Pengwei Liu
admin_assigned
2025-03-24T12:44:07.900Z
66f0e9bb16362f8e97b601e7
/avatars/2d76a832dd1b8708ac2f3c168cb2ceaf.svg
pengwei liu
user
wayyyy
67e0d11eb04d9e83682f2230
Zeying Huang
67e0d11eb04d9e83682f2231
Yi Yang
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical
  Problems
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.
MathFlow, a modular problem-solving pipeline, improves MLLMs' performance in visual mathematical problem-solving by decoupling perception and reasoning.
Multimodal Large Language Models MLLMs visual mathematical problem-solving diagrams FlowVerse MathFlow perception inference MathFlow-P-7B