The field of multimodal mathematical reasoning and visual understanding is rapidly advancing, with a focus on developing models that can effectively integrate textual and visual information to solve complex problems. Researchers are exploring new approaches, such as leveraging executable code and visual aids, to improve the accuracy and verifiability of multimodal reasoning. Notable papers in this area include CodePlot-CoT, which proposes a code-driven Chain-of-Thought paradigm for mathematical visual reasoning, and MathCanvas, which introduces a comprehensive framework for intrinsic Visual Chain-of-Thought capabilities in mathematics. These innovative approaches are advancing the field and opening up new directions for research.
Multimodal Mathematical Reasoning and Visual Understanding
Sources
GeoVLMath: Enhancing Geometry Reasoning in Vision-Language Models via Cross-Modal Reward for Auxiliary Line Creation
Text-Enhanced Panoptic Symbol Spotting in CAD Drawings
InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models
CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images
Automated document processing system for government agencies using DBNET++ and BART models
RECODE: Reasoning Through Code Generation for Visual Question Answering
MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning