Yutong Bian

Yutong Bian

Xidian & Heriot-Watt

My core passion lies in the development and study of Computer Using Agents (CUAs), specifically focusing on MLLM-based Graphical User Interface (GUI) Agents. I aim to elevate these agents from mere task executors to sophisticated systems exhibiting human-like reasoning, generalization, and robust decision-making across complex digital environments.

My work spans the practical building and deployment of these agents and, crucially, the pursuit of multi-dimensional and reliable evaluation methodologies(Agent-as-a-Judge).

Currently a final-year Undergraduate student in Communication Engineering at Xidian University and Heriot-Watt University, I am actively seeking a PhD/Intern Opportunity.

Curriculum Vitae

  • 2025.10:  🎉🎉 Our paper “You Don’t Know Until You Click: Automated GUI Testing for Production-Ready Software Evaluation” has been accepted by Scaling Environments for Agents (SEA) Workshop at NeurIPS 2025.

📝 Publications

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You Don’t Know Until You Click: Automated GUI Testing for Production-Ready Software Evaluation

Yutong Bian*, Xianhao Lin, Yupeng Xie et al.

Project

  • As the lead for AppEvalPilot, I designed and implemented a system to dynamically assess software functionality through UI interaction, moving beyond the limitations of static analysis for LLM-based software engineers.
  • My work involved creating automated test case generation and a test execution agent capable of complex GUI interactions.
  • The experimental results demonstrated a high correlation (0.91) between AppEvalPilot’s assessments and those of human experts, while also being 55% faster and 94.8% cheaper.

📖 Educations

  • 2021.09 - 2026.06, B.Eng. in Communication Engineering, Xidian University, China & Heriot-Watt University, UK.
    • GPA: 3.8/4.0

💻 Internship

🚀 Projects

  • OSAgent: Cross-platform Intelligent Assistant (Sep. 2024 - May. 2025)
    • Focused on developing a universal, stable, and efficient GUI agent framework.
    • Contributed to the architecture design, perception, planning, and execution modules.
    • Achieved state-of-the-art performance on SpaBench (mobile) cross-application tasks (26.7% vs. 13.3% by the previous SOTA).
  • R1-Like GUI Agent Training (Apr. 2024 - May. 2025)
    • Focused on enhancing the core element grounding capability of GUI agents using GRPO.
    • Designed a data collection and refinement pipeline and a multi-component reward function.
    • Demonstrated that 1k meticulously selected data points can achieve performance comparable to SOTA models trained on millions of samples, significantly improving GUI grounding accuracy on benchmarks like ScreenSpot and ScreenSpotPro.