MMBench

MMBench is a benchmark suite designed to evaluate the performance of machine learning models, particularly in the context of mobile devices. It aims to provide a comprehensive and standardized way to measure and compare the efficiency and effectiveness of various machine learning algorithms on mobile platforms.
### Key Features of MMBench:
1. "Diverse Task Set": MMBench includes a variety of tasks that are representative of common machine learning applications on mobile devices. These tasks cover different domains such as computer vision, natural language processing, and speech recognition.
2. "Mobile-Focused": The benchmark is tailored to the constraints and capabilities of mobile devices, ensuring that the evaluated models are practical for real-world deployment on smartphones and tablets.
3. "Performance Metrics": MMBench provides a set of metrics to evaluate model performance, including accuracy, latency, memory usage, and energy consumption. These metrics help in understanding the trade-offs between different models.
4. "Standardized Evaluation": By providing a standardized set of tasks and evaluation procedures, MMBench allows researchers and developers to compare their models fairly and consistently.
5. "Open Source": MMBench is typically open-source, allowing the community to contribute to its development, share their results, and build upon the existing framework.
### Example Tasks in MMBench:
- "Image Classification": Classifying images into predefined categories. - "Object Detection": Identifying and localizing objects within images. - "Image Segmentation": Segmenting images into different regions or classes

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lass="xiangguan" id="content">7月25日，《MMBench-GUI：层次化多平台评估框架用于GUI代理》新研究提出了MMBench-GUI，这是一个多平台、多层级的 GUI代理能力测试系统。 MMBench-GUI测什么？它分了4个层次，逐步拔高挑战 GUI内容理解：能不能看懂界面上的字和图标？ 元素精准定位：找对按钮了吗？点对地方了吗？ 任务自动化：能否执行如“打开+编辑+保存”的复合操作？ 任务协作：多个App间跳转协作，AI能搞定吗？ 引入创新指标EQA很多模型完成了任务，但做了大量“废操作”？ 就像一个人绕了好几圈才找到厕所……于是 MMBench-GUI提出了新指标 EQA（Efficiency-Quality Area），不仅看你“完成没有”，还看你“做得高效不高效”！ 实验怎么做的？✅ 用真实App截图（不是模拟！）✅ 涵盖全平台：Win / Mac / Linux / Android / iOS / Web✅ 对每一个截图界面都加了标注，精准打点✅ 任务设计参考了多个权威数据集，例如WebArena、WindowsAgentArena、OSWorld等✅ 涵盖100+常见桌面+移动任务，细粒度评估每个模型 哪些模型被测试了？测试模型包括多个GUI领域的代表：模型名称→特点UI-TARS-72B-DPO→定位能力优秀UI-TARS-1.5-7B→轻量高效的桌面代理InternVL3-72B→多模态理解强测试发现，最关键的不是语言模型的理解能力，而是“视觉定位”的精准性！✅ 点错位置 → 一切都白搭✅ 冗余操作 → 效率低得吓人 发现了哪些问题？ 当前GUI代理的通病：❌ 点错按钮（无法精确定位）❌ 没记性（缺乏上下文记忆）❌ 做事啰嗦（多余步骤太多）❌ 动作空间太小（缺乏灵活性）❌ 跨平台泛化差（只能适应特定平台） 哪怕任务最终完成了，整个过程常常绕远路，浪费大量操作步骤。 那该怎么改进？ 想让 AI 更像“数字员工”而不是“指令机器人”，必须：✅ 引入 模块化定位系统✅ 加强 长上下文记忆与多工具配合✅ 优化 提前终止机制，避免不必要操作✅ 打造 跨平台泛化能力（别只会在Win系统上干活） 一句话总结：MMBench-GUI 把 GUI 代理的“动手能力”从表面理解推向实战细节，是所有做“可执行AI”项目的必备测试！