UXBench: Measuring the Actionability of LLM-Generated UX Critiques
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 evidence before reporting. Each judge model produces a structured UX report over seven rubric dimensions; report quality is measured by whether a fix...
| Authors | Wenjie Wang, Yue Huang, Zipeng Ling et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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