ChartREG++: New Benchmark and Methods for Improving Chart Understanding in AI Models
Researchers have introduced ChartAgent, an agentic AI framework that reasons directly within a chart's visual space to answer complex chart-based questions. Unlike prior multimodal large language models that rely on textual shortcuts, ChartAgent iteratively decomposes queries into visual subtasks and uses specialized tools such as annotation drawing, region cropping, and axis localization. The system achieves state-of-the-art results on two major benchmarks, with gains of up to 17.31% on the most challenging unannotated, numerically intensive queries.
ChartAgent is a novel multimodal agentic framework designed to address a key weakness in existing large language models: their sharp performance drop on unannotated charts that require genuine visual interpretation rather than pattern-matching to text. The system works by iteratively breaking down a user query into a sequence of visual subtasks, then actively manipulating chart images using a library of chart-specific vision tools — including drawing annotations, cropping regions such as pie slices or individual bars, and localizing axes. This approach mirrors human cognitive strategies for reading charts and operates directly in the chart's spatial domain rather than converting visual content to text first. ChartAgent achieves state-of-the-art accuracy on both the ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07% overall and 17.31% on unannotated, numerically intensive queries. The framework is also shown to be effective across diverse chart types and varying levels of visual and reasoning complexity, and functions as a plug-and-play module that improves performance regardless of the underlying LLM used. The paper was accepted at ACL 2026 and presented as an oral paper at the NeurIPS 2025 Multimodal Algorithmic Reasoning Workshop.
What's missing
The paper does not detail computational cost or inference latency compared to baseline models, which is relevant for real-world deployment. It is also unclear how ChartAgent performs on charts from domains outside its benchmark datasets, or whether the tool library requires manual extension for novel chart types.
What different sources said
- arXiv cs.AICenter
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
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