Automatic multimodal UX improvement recommendations from LLM agent user simulations
Quick summary
arXiv:2609.22971v1 Announce Type: cross Abstract: Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale. LLM agents offer a promising route to automating UX testing by simulating realistic user behaviour. However, existing simulation approaches typically lack multimodality and require time-consuming manual review to extract actionable insights. We formalise UX improvement recommendation from simulation data as a structured natural language generation and ranking problem, and establish an evaluation protocol using expert annotatio
Key takeaways
- arXiv:2609.22971v1 Announce Type: cross Abstract: Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale.
- LLM agents offer a promising route to automating UX testing by simulating realistic user behaviour.
- However, existing simulation approaches typically lack multimodality and require time-consuming manual review to extract actionable insights.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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