arXiv:2606.16262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as UX judges that inspect interfaces, diagnose usability problems, and propose repairs. Yet no controlled benchmark measures whether the resulting critiques are reliable and actionable across heterogeneous product surfaces. We introduce UXBench, a benchmark for evaluating LLMs as interaction-grounded UX judges. UXBench comprises local-first runnable web fixtures spanning ten product-surface families, paired with coverage-gated browser exploration that forces models to collect interaction ev
The proliferation of LLMs capable of interface critique necessitates standardized benchmarks to assess their practical utility and actionability, especially as they move into deployment.
A strategic reader should care because the reliability of AI as a UX judge directly impacts the efficiency of product development, the quality of user experiences, and the potential for AI to automate critical design functions.
The introduction of UXBench provides the first controlled, interaction-grounded benchmark for evaluating LLMs in a UX critique role, enabling a more objective assessment of their capabilities beyond subjective human feedback.
- · AI developers
- · Product teams
- · UX researchers
- · Software quality assurance
- · Manual UX testing services
- · Companies relying on unvalidated AI critique
- · Inefficient UX design processes
This benchmark will drive improvements in LLM capabilities for specific, applied tasks like UX critique, increasing their utility.
Improved AI-driven UX critique could accelerate product development cycles and enhance user satisfaction across digital interfaces.
The success of such benchmarks might inspire a broader shift towards standardized, interaction-grounded evaluations for other AI agentic tasks, strengthening the AI agent ecosystem.
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Read at arXiv cs.AI