Papers with ChartBench
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering (2026.acl-long)
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| Challenge: | Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts. |
| Approach: | They propose a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on numerically intensive queries. |