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AI Agents represent a paradigm shift from passive text generation to active task execution, combining LLMs with planning, tool use, and memory to automate complex workflows. This article explores their architecture, working principles, and practical applications in content creation, highlighting the transition from chatbots to intelligent executors. AI智能体标志着从被动文本生成到主动任务执行的范式转变，它结合了大语言模型、规划、工具使用和记忆功能，能够自动化复杂工作流程。本文探讨了其在内容创作领域的架构、工作原理和实际应用，强调了从聊天机器人到智能执行者的转变。

Bing announces AI-powered Intelligent Search features that provide faster, more comprehensive answers through machine reading comprehension, multi-source validation, and conversational interfaces. (Bing推出AI驱动的智能搜索功能，通过机器阅读理解、多源验证和对话式界面，提供更快、更全面的答案。)

Browser-Use is an open-source AI-powered browser automation platform that enables AI agents to interact with web pages like humans—navigating, clicking, filling forms, and scraping data—through natural language instructions or program logic. It bridges AI models with browsers, supports multiple LLMs, and offers both no-code interfaces and SDKs for technical and non-technical users. (Browser-Use是一个开源的AI驱动浏览器自动化平台，让AI代理能像人类一样与网页交互：导航、点击、填表、抓取数据等。它通过自然语言指令或程序逻辑连接AI与浏览器，支持多款LLM，并提供无代码界面和SDK，适合技术人员和非工程背景人员使用。)

UltraRAG 2.0 is a novel RAG framework built on the Model Context Protocol (MCP) architecture, designed to drastically reduce the engineering overhead of implementing complex multi-stage reasoning systems. It achieves this through componentized encapsulation and YAML-based workflow definitions, enabling developers to build advanced systems with as little as 5% of the code required by traditional frameworks, while maintaining high performance and supporting features like dynamic retrieval and conditional logic. UltraRAG 2.0 是一个基于模型上下文协议（MCP）架构设计的新型RAG框架，旨在显著降低构建复杂多阶段推理系统的工程成本。它通过组件化封装和YAML流程定义，使开发者能够用传统框架所需代码量的5%即可构建高级系统，同时保持高性能，并支持动态检索、条件判断等功能。

LEANN is an innovative vector database and personal AI platform that transforms your notebook into a powerful RAG system, supporting local semantic retrieval of millions of documents with 97% storage savings and no precision loss. (LEANN是一款创新的向量数据库与个人AI平台，可将笔记本变为强大的RAG系统，支持本地语义检索数百万文档，存储节省97%且无精度损失。)

A curated directory showcasing companies and products adopting the llms.txt standard across various sectors like AI, finance, developer tools, and websites, with token counts indicating implementation scale. (中文摘要翻译：一份精选目录，展示在AI、金融、开发者工具和网站等多个领域采用llms.txt标准的企业与产品，token数量反映了实施规模。)

This article explores a novel deep learning model for generating light field views, detailing its neural architecture, training methodology, and applications in computational photography and VR. The model leverages transformer-based attention mechanisms to synthesize high-fidelity multi-view images from sparse inputs, addressing key challenges in angular consistency and computational efficiency. (本文探讨了一种用于生成光场视图的新型深度学习模型，详细介绍了其神经架构、训练方法以及在计算摄影和VR中的应用。该模型利用基于Transformer的注意力机制，从稀疏输入中合成高保真多视图图像，解决了角度一致性和计算效率方面的关键挑战。)

This analysis examines critical AI security vulnerabilities within the ILIAS Learning Management System, highlighting potential risks in data processing, model integrity, and access control mechanisms. The report provides technical insights for security professionals to identify, assess, and mitigate these vulnerabilities in educational technology environments. // 本分析深入探讨ILIAS学习管理系统中的关键AI安全漏洞，重点关注数据处理、模型完整性和访问控制机制中的潜在风险。报告为安全专业人员提供技术见解，帮助识别、评估和缓解教育技术环境中的这些漏洞。

This article introduces a novel deep learning model for generating light field views, which enhances 3D scene reconstruction and immersive visual experiences by simulating multi-perspective light information. The model leverages neural networks to predict light rays from sparse inputs, enabling applications in virtual reality, computational photography, and autonomous systems. (本文介绍了一种新型的深度学习模型，用于生成光场视图，通过模拟多视角光线信息来增强三维场景重建和沉浸式视觉体验。该模型利用神经网络从稀疏输入中预测光线，可应用于虚拟现实、计算摄影和自主系统等领域。)