  This paper tackles a key issue in the interpretation of scientific figures:
the fine-grained alignment of text and figures. It advances beyond prior
research that primarily dealt with straightforward, data-driven visualizations
such as bar and pie charts and only offered a basic understanding of diagrams
through captioning and classification. We introduce a novel task, Figure
Integrity Verification, designed to evaluate the precision of technologies in
aligning textual knowledge with visual elements in scientific figures. To
support this, we develop a semi-automated method for constructing a large-scale
dataset, Figure-seg, specifically designed for this task. Additionally, we
propose an innovative framework, Every Part Matters (EPM), which leverages
Multimodal Large Language Models (MLLMs) to not only incrementally improve the
alignment and verification of text-figure integrity but also enhance integrity
through analogical reasoning. Our comprehensive experiments show that these
innovations substantially improve upon existing methods, allowing for more
precise and thorough analysis of complex scientific figures. This progress not
only enhances our understanding of multimodal technologies but also stimulates
further research and practical applications across fields requiring the
accurate interpretation of complex visual data.
