🧵(5/5) For more details, read the full work incoming at IEEE VIS from @wattenberg.bsky.social, @jlsun.bsky.social and me at arxiv.org/abs/2507.18022!

🧵(4/5) What role could incorporating charts play in AI-driven data analysis workflows?

Visualization is essential for humans to synthesize complex information––can AI shed light on human psychology or the underlying principles of visual knowledge discovery?

And many more!

🧵(3/5) How do graphical variations in graphs affect our results? Could there be a new body of scholarship in visualization for AI-oriented chart design?

We use default Matplotlib scatterplots––how might visualizations be optimized for AIs?

🧵(2/5) Comparison with two baselines––providing a blank chart and a chart with mismatched data––shows that the improved performance is due to the content of the charts.

AI systems benefiting from visualization raises many intriguing questions:

Charts and graphs help people analyze data, but can they also help AI?

In a new paper, we provide initial evidence that it does! GPT 4.1 and Claude 3.5 describe three synthetic datasets more precisely and accurately when raw data is accompanied by a scatter plot. Read more in🧵!

🚨 New preprint! 🚨

Everyone loves causal interp. It’s coherently defined! It makes testable predictions about mechanistic interventions! But what if we had a different objective: predicting model behavior not under mechanistic interventions, but on unseen input data?

Today we're launching a multi-lab open collaboration, the ARBOR project, to accelerate AI interpretability research for reasoning models. Please join us!

github.com/ARBORproject...

(ARBOR = Analysis of Reasoning Behavior through Open Research)