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UltraRAG is a comprehensive RAG framework developed by Tsinghua University and partners, featuring zero-code WebUI, automated knowledge base adaptation, and modular design for both research and practical applications. It integrates innovative technologies like KBAlign and DDR to optimize retrieval and generation performance across various models and tasks. (UltraRAG是由清华大学等团队开发的全面RAG框架，具备零代码WebUI、自动化知识库适配和模块化设计，支持科研与业务应用。它集成了KBAlign、DDR等创新技术，优化了多模型和多任务的检索与生成性能。)

OpenBMB (Open Lab for Big Model Base) is an open-source initiative aimed at building a comprehensive ecosystem for large-scale pre-trained language models. It provides a full suite of tools covering data processing, model training, fine-tuning, compression, and inference, significantly reducing the cost and technical barriers of working with billion-parameter models. The framework includes specialized tools like BMTrain for efficient training, BMCook for model compression, BMInf for low-cost inference, OpenPrompt for prompt learning, and OpenDelta for parameter-efficient fine-tuning. OpenBMB fosters a collaborative community to standardize and democratize large model development and application. (OpenBMB（大模型开源基础实验室）是一个旨在构建大规模预训练语言模型生态系统的开源项目。它提供了一套覆盖数据处理、模型训练、微调、压缩和推理全流程的工具链，显著降低了百亿参数模型的使用成本和技术门槛。该框架包含BMTrain（高效训练）、BMCook（模型压缩）、BMInf（低成本推理）、OpenPrompt（提示学习）和OpenDelta（参数高效微调）等专用工具。OpenBMB致力于通过开源社区协作，推动大模型的标准化、普及化和实用化。)

UltraRAG is a low-code RAG development framework based on Model Context Protocol (MCP) architecture, emphasizing visual orchestration and reproducible evaluation workflows. It modularizes core components like retrieval, generation, and evaluation as independent MCP Servers, providing transparent and repeatable development processes through interactive UI and pipeline builders. (UltraRAG是一个基于模型上下文协议（MCP）架构的低代码检索增强生成（RAG）开发框架，强调可视化编排与可复现的评估流程。它将检索、生成与评估等核心组件封装为独立的MCP服务器，通过交互式UI和流水线构建器提供透明且可重复的研发流程。)

English Summary: UltraRAG 2.0 is an open-source framework based on Model Context Protocol (MCP) architecture that simplifies complex RAG system development through YAML configuration, enabling low-code implementation of multi-step reasoning, dynamic retrieval, and modular workflows. It addresses engineering bottlenecks in research and production RAG applications. 中文摘要翻译: UltraRAG 2.0是基于Model Context Protocol（MCP）架构的开源框架，通过YAML配置文件简化复杂RAG系统开发，实现低代码构建多轮推理、动态检索和模块化工作流。它解决了研究和生产环境中RAG应用的工程瓶颈问题。

AirLLM is an open-source framework that enables running 70B-parameter large language models on a single 4GB GPU through layer-wise offloading and memory optimization techniques, democratizing access to cutting-edge AI without traditional compression methods. (AirLLM是一个开源框架，通过分层卸载和内存优化技术，使700亿参数的大语言模型能够在单个4GB GPU上运行，无需传统压缩方法即可实现前沿AI的普及化访问。)

Generative Engine Optimization (GEO) is a new digital marketing paradigm emerging from generative AI and large language models (LLMs). Unlike traditional Search Engine Optimization (SEO), which focuses on ranking and traffic through keywords and links, GEO aims to make brands and content directly referenced in AI-generated answers by prioritizing semantic understanding, authority building, and structured content. This report systematically explains GEO's core concepts, contrasts it with SEO across goals, mechanisms, content strategies, and metrics, and provides actionable guidance for technical professionals to adapt to the AI-driven search era. (生成式引擎优化（GEO）是由生成式AI和大语言模型（LLMs）兴起催生的数字营销新范式。与专注于通过关键词和链接获取排名和流量的传统搜索引擎优化（SEO）不同，GEO旨在通过优先考虑语义理解、权威性构建和结构化内容，使品牌和内容在AI生成的答案中被直接引用。本报告系统阐述GEO核心概念，从目标、机制、内容策略和指标等多维度对比GEO与SEO，并为技术专业人士适应AI驱动搜索时代提供行动指南。)

GEO (Generative Engine Optimization) represents the 'new SEO of the AI era,' enabling brands to embed content directly into AI-generated answers for 'clickless customer acquisition.' This technology is reshaping traffic distribution and creating investment opportunities in China's A-share market, with 62 GEO concept stocks spanning media, technology, and retail sectors. (GEO（生成式引擎优化）是“AI时代的新SEO”，通过让品牌内容直接嵌入AI生成的答案中，实现“无需点击即获客”。这项技术正在重塑流量分配格局，并在中国A股市场催生了62只概念股，覆盖传媒、科技和零售等行业，带来新的投资机遇。)

English Summary: GEO (Generative Engine Optimization) is an emerging marketing strategy that optimizes content for generative AI engines, aiming to have brands mentioned positively in AI-generated responses rather than just ranking high in search results. As AI becomes a primary information source, GEO represents a shift from traditional SEO, focusing on semantic depth, data support, and authoritative sources to influence AI recommendations. The market is rapidly growing, with applications in e-commerce and content creation, though it remains an early-stage industry with evolving practices. 中文摘要翻译: GEO（生成式引擎优化）是一种新兴的营销策略，针对生成式AI引擎优化内容，目标是让品牌在AI生成的回答中被正面提及，而不仅仅是在搜索结果中排名靠前。随着AI成为主要信息来源，GEO代表了从传统SEO的转变，专注于语义深度、数据支持和权威来源，以影响AI推荐。市场正在快速增长，应用于电子商务和内容创作，尽管它仍是一个早期行业，实践在不断演变。

This guide provides comprehensive information about LLM (Master of Laws) programs in the United Kingdom, featuring 113 results from top institutions including Oxford, Cambridge, King's College London, and Edinburgh. It details program specializations, delivery formats (full-time, part-time, distance learning), and key features of each law school. (本指南全面介绍英国法学硕士项目，涵盖牛津、剑桥、伦敦国王学院、爱丁堡大学等顶尖院校的113个课程信息，详细说明专业方向、授课形式（全日制、非全日制、远程教育）及各法学院特色。)