ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
arXiv:2510.04514v3 Announce Type: replace-cross Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts. To address this, we introduce ChartAgent, a novel agentic framework that explicitly performs visual reasoning directly within the chart's spatial domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively decomposes queries into visual subtasks and actively manipulates and interacts with chart
The proliferation of multimodal LLMs and the recognition of their limitations in visually complex, unannotated charts has created a pressing need for more robust visual reasoning capabilities.
Improving AI's ability to precisely interpret visual data, especially complex charts, is crucial for automating analytics, data-driven decision making, and expanding AI's cognitive reach beyond purely textual understanding.
This research introduces a novel agentic framework that explicitly performs visual reasoning within a chart's spatial domain, moving beyond textual chain-of-thought to direct visual interaction and manipulation.
- · AI agents developers
- · Data analytics platforms
- · Business intelligence software
- · Research institutions in AI/ML
- · Traditional chart annotation services
- · AI models relying solely on text-based approaches for visual VQA
- · Manual data interpretation roles
The new ChartAgent framework significantly enhances the accuracy of chart-based visual question answering by employing iterative visual subtask decomposition.
This improved visual reasoning capability could accelerate the automation of data analysis and reporting functions across various industries, making insights more accessible and faster to generate.
The underlying methodology of spatially-grounded visual reasoning might generalize to other complex visual interpretation tasks, potentially leading to more sophisticated and reliable AI agents for diverse domain-specific applications.
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Read at arXiv cs.CL