Reproduce “ChartAgent: A Chart Understanding Framework with Tool Integrated Reasoning” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Boran Wang, Xinming Wang, Yi Chen, Xiang Li, Jian Xu, Jing Yuan, Chenglin Liu
ChartAgent is a framework that leverages tool-integrated reasoning and a Chart Evidence Package to achieve robust and transparent chart understanding. It addresses the limitations of existing MLLMs by accurately interpreting charts even with sparse annotations, providing verifiable outputs for complex queries.
Key insights
- Mimicking human-like decomposed cognitive processes through tool-integrated reasoning provides superior robustness to sparse annotations in charts, drastically outperforming end-to-end MLLMs.
- The systematic aggregation of intermediate visual and computational evidence within a Chart Evidence Package (CEP) enables unprecedented transparency and verifiability of AI-driven chart analysis.
- A multi-expert collaboration mechanism combined with EIG-based tool scheduling enhances both the accuracy and efficiency of complex multimodal reasoning tasks by optimizing tool selection and validating outputs.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.