Title: CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: CogFlow, MLLM, RLHF, visual mathematics problem

Abstract: Despite recent advances, multimodal large language models continue to struggle with visual mathematical problem solving.
Some recent works recognize that visual perception is a bottleneck in visual mathematical reasoning, but their solutions are limited to improving the extraction and interpretation of visual inputs.
Notably, they all ignore the key issue of whether the extracted visual cues are faithfully integrated and properly utilized in subsequent reasoning.
Motivated by this, we present \textsc{CogFlow}, a novel cognitive-inspired three-stage framework that incorporates a knowledge internalization stage, explicitly simulating the hierarchical flow of human reasoning: perception $\Rightarrow$ internalization $\Rightarrow$ reasoning.
In line with this hierarchical flow, we holistically enhance all its stages.
We devise synergistic visual rewards to boost perception capabilities in parametric and semantic spaces, jointly improving visual information extraction from symbols and diagrams.
To guarantee faithful integration of extracted visual cues into subsequent reasoning, we introduce a visual-anchored reward model in the internalization stage, bridging perception and reasoning.
Moreover, we design a visual grated policy optimization algorithm to further enforce the reasoning is grounded with the visual knowledge, preventing models seeking shortcuts that produce appear coherent but are visually ungrounded reasoning chains.
Moreover, we contribute a new dataset \textsc{MathCog} for model training, which contains samples with over 120K high-quality perception-reasoning aligned annotations.
Comprehensive experiments and analysis on three commonly used visual mathematical reasoning benchmarks validate the superiority of the proposed \textsc{CogFlow}.
Our project page will be publicly available.