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This article provides a comprehensive analysis of Microsoft's AI Agent framework architecture design, covering its technical foundations, core components, implementation strategies, and practical applications in the Chinese market. (本文全面解析微软AI Agent框架的架构设计，涵盖其技术基础、核心组件、实施策略及在中国市场的实际应用。)

Edge AI enables autonomous vehicles to process sensor data locally for real-time object detection, path planning, and decision-making, reducing latency and enhancing safety in complex driving conditions. (边缘AI使自动驾驶汽车能够在本地处理传感器数据，实现实时物体检测、路径规划和决策，在复杂路况下降低延迟并提升安全性。)

Edge computing enables autonomous vehicles to process data locally through dynamic scheduling strategies, overcoming limited computational power by prioritizing tasks, partitioning models, and adapting to network conditions for real-time decision-making. (边缘计算通过动态调度策略，使自动驾驶车辆能够本地处理数据，通过任务优先级调度、模型分区和网络状态适应，克服有限算力，实现实时决策。)

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This article examines documented cases of human manipulation in Google's search algorithms, analyzing technical methods, detection challenges, and implications for search neutrality and digital governance. (本文研究谷歌搜索算法人为操控的案例，分析技术手段、检测挑战以及对搜索中立性和数字治理的影响。)

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FlashMLA is an efficient MLA decoding kernel optimized for NVIDIA Hopper GPUs, delivering up to 3000 GB/s memory bandwidth and 580 TFLOPS compute performance while reducing KV cache requirements by 93.3% for faster, more cost-effective AI inference. (FlashMLA是DeepSeek针对NVIDIA Hopper GPU优化的高效MLA解码内核，在内存受限配置下可达3000 GB/s带宽，计算受限配置下可达580 TFLOPS峰值性能，同时将KV缓存需求减少93.3%，实现更快、更经济的AI推理。)