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PerceptUI uses LLM agents for realistic UI/UX evaluation

Researchers have developed PerceptUI, a new framework designed to improve UI/UX evaluation by using LLM agents as synthetic users. This system is trained to predict how specific user personas would respond to interface-related questions and generate natural language rationales. PerceptUI employs a two-stage training process involving contrastive reflection fine-tuning and a reflective prompt-evolution step to achieve human-level realism and generalize across various scenarios. AI

IMPACT This framework could significantly speed up and reduce the cost of UI/UX evaluation by providing realistic synthetic user feedback.

RANK_REASON The cluster contains an academic paper detailing a new framework for UI/UX evaluation using LLM agents.

Read on Hugging Face Daily Papers →

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nicolas Bougie, Xiaotong Ye, Gian Maria Marconi, Narimasa Watanabe ·

    PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

    arXiv:2606.05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

    User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly. In light of this, recent work has explored Mul…