Patronus introduced FigmaTrace, a dataset of 3,469 Figma design trajectories capturing more than 200 hours of human design work across ten creative skills. The tasks are split between close-ended work such as accessibility fixes and design templating, and open-ended work such as prototype wiring, sketch-to-Figma conversion and platform adaptation.
The diagnosis behind it is specific. Vision language models have progressed on objective, verifiable tasks — document understanding, robotics, computer use — while continuing to underperform on subjective creative design work, where design taste and figurative meaning matter. Patronus attributes a major part of that gap to missing high-quality human workflow data that captures the preferences and decisions expert designers actually make. Recording trajectories rather than finished files is the point: the artifact shows the order in which choices were made, which is the part a final Figma file destroys.
Capturing the path instead of the output is the same instinct as treating the accepted artifact and its record as the durable asset, tracked in when proof leaves the executor. For interface generation specifically, the generative UI hub is where this dataset's downstream models would show up.
This is a vendor announcement of the vendor's own dataset: it describes scale and task coverage without published baselines, licence terms or inter-rater agreement on the subjective tasks. The condition to watch is whether models trained on FigmaTrace are evaluated by designers rather than by automated similarity to the recorded trajectory, because taste measured as trajectory-matching rewards imitation rather than judgement.