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ChartAct: A New Benchmark for Dynamic Chart Understanding

Existing chart understanding benchmarks focus on static charts, but real-world charts are often dynamically interactive. The ChartAct benchmark reveals that even the strongest model Claude-Opus-4.7 has a success rate of only 84.5% on dynamic chart tasks.

图表理解多模态模型动态交互数据可视化GUI agent状态跟踪多步骤推理

Published 2026-05-26 21:15Recent activity 2026-05-27 10:30Estimated read 4 min

ChartAct: A New Benchmark for Dynamic Chart Understanding

Section 01

ChartAct Benchmark: New Challenges and Current Status of Dynamic Chart Understanding

Most existing chart understanding benchmarks focus on static charts, but real-world charts are often in dynamically interactive forms. As a new benchmark, ChartAct reveals the limitations of current AI models in dynamic chart tasks: even the strongest model Claude-Opus-4.7 has a success rate of only 84.5%. This benchmark provides an important tool for evaluating dynamic chart understanding capabilities.

Section 02

The Gap Between Static and Dynamic Chart Understanding

Multimodal AI has made significant progress in static chart understanding, but real-world charts often contain dynamic interactive elements such as hover tips, click-to-expand, and drag-to-adjust. Current AI systems do not perform well on such dynamic tasks, and there is an obvious gap between static and dynamic understanding.

Section 03

Design and Core Evaluation Capabilities of the ChartAct Benchmark

ChartAct collected 673 dynamic charts (7 types) from 8 real-world websites and built 1440 Q&A samples. It evaluates in two environments: dynamic charts and dashboards. Core capabilities include: 1. Visible content recognition; 2. Interaction selection (hover/click, etc.); 3. State reasoning (tracking the state after multi-step interactions).

Section 04

Experimental Results: Top Models Still Have Significant Limitations

Evaluation of 11 models found: Claude-Opus-4.7 has a success rate of 84.5%, while most models are below 60%; they perform well on simple interactions, but the success rate of multi-step tasks (such as filtering + comparison) is often below 30%; performance in the dashboard environment (multi-chart linkage) is significantly worse.

Section 05

Failure Cases: Main Bottlenecks of Dynamic Understanding

Failure cases are attributed to three types of problems: 1. Incorrect interaction selection (selecting non-clickable elements or performing invalid operations); 2. State tracking failure (failing to update cognition after interaction); 3. Premature termination (answering without obtaining complete information).

Section 06

Implications and Future Directions of ChartAct

Dynamic chart understanding requires specialized training in interaction selection, state tracking, and multi-step planning; AI needs to improve GUI understanding (including multi-chart linkage); application scenarios include business intelligence, scientific research, news, etc. Current technology requires human supervision, and future efforts need to break through the bottleneck of multi-step reasoning.

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