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This article explores an alternative approach to the Model Context Protocol (MCP) for integrating tools with Large Language Models (LLMs) by leveraging existing OpenAPI servers. It proposes a simpler, more intuitive method that uses structured HTTP API definitions as tool inputs, requiring only minimal authentication flow additions. The implementation is demonstrated through a concise Scala script, focusing on core tool integration while omitting MCP's broader features like prompts and resources. 原文翻译： 本文探讨了一种替代模型上下文协议（MCP）的方法，通过利用现有的OpenAPI服务器为大型语言模型（LLM）集成工具。它提出了一种更简单、更直观的方法，使用结构化的HTTP API定义作为工具输入，仅需添加最小的身份验证流程。通过一个简洁的Scala脚本演示了实现，专注于核心工具集成，同时省略了MCP更广泛的功能，如提示和资源。

This survey provides a comprehensive overview of recent progress in LLM-empowered knowledge graph construction, systematically analyzing how LLMs reshape the classical three-layered pipeline of ontology engineering, knowledge extraction, and knowledge fusion. 原文翻译： 本综述全面概述了LLM赋能知识图谱构建的最新进展，系统分析了LLM如何重塑本体工程、知识抽取和知识融合的经典三层流程。

This research paper reveals new indirect prompt injection attack vectors that enable adversaries to remotely exploit LLM-integrated applications by injecting malicious prompts into retrieved data, demonstrating practical attacks against systems like Bing Chat and highlighting significant security vulnerabilities in current LLM deployments. 原文翻译： 本研究论文揭示了新的间接提示注入攻击向量，攻击者可通过在检索数据中注入恶意提示来远程利用LLM集成应用，展示了针对Bing Chat等系统的实际攻击，并突显了当前LLM部署中的重大安全漏洞。

This guide explores how Large Language Models (LLMs) can accelerate GPU kernel optimization, bridging the gap between research algorithms and production-ready implementations. It covers the computational foundations of matrix multiplication, the mathematical framework from Strassen to tensor decomposition, and DeepMind's evolution from AlphaTensor to AlphaEvolve, providing a technical roadmap for self-improving AI infrastructure. 原文翻译： 本指南探讨了大型语言模型（LLM）如何加速GPU内核优化，弥合研究算法与生产就绪实现之间的差距。它涵盖了矩阵乘法的计算基础、从Strassen到张量分解的数学框架，以及DeepMind从AlphaTensor到AlphaEvolve的演进历程，为自改进AI基础设施提供了技术路线图。

Dive into LLMs is a comprehensive, practice-oriented tutorial series for large language models, developed from Shanghai Jiao Tong University's AI Security Technology course with Huawei Ascend support. It covers the full technical stack from fine-tuning to security alignment, offering complete learning resources and domestic hardware adaptation. 原文翻译： Dive into LLMs 是由上海交通大学《人工智能安全技术》课程拓展而来的大模型编程实践教程系列，获得华为昇腾社区支持。它以实践为导向，覆盖从微调到安全对齐的全技术栈，提供完整学习资源并适配国产硬件。

This article explores how large language models (LLMs) function as document completion engines and demonstrates how to build practical applications by mapping between user and document domains, using GitHub Copilot's prompt engineering pipeline as a detailed case study. 原文翻译： 本文探讨了大型语言模型（LLM）如何作为文档完成引擎工作，并通过在用户领域和文档领域之间建立映射来构建实际应用，以GitHub Copilot的提示工程流程作为详细案例研究。

Structured generation in LLMs consistently improves performance on the GSM8K benchmark, with up to 70%+ gains, and offers additional benefits like prompt consistency and thought-control. 原文翻译： LLM中的结构化生成在GSM8K基准测试中持续提升性能，最高可达70%以上的增益，并带来提示一致性和思维控制等额外优势。

Leading AI researchers identify four key innovations—steering, memory, tool integration, and multimodality—that will transform LLM capabilities over the next 6-12 months, enabling more reliable, personalized, and actionable AI applications for both enterprise and consumer use cases. 原文翻译： 顶尖AI研究人员确定了四大关键创新——控制、记忆、工具集成和多模态——这些将在未来6-12个月内改变大语言模型的能力，为企业和消费者应用场景提供更可靠、个性化和可操作的AI解决方案。

The OpenAI Agents SDK is a lightweight, provider-agnostic Python framework for building multi-agent workflows with features like sandbox agents, tools, guardrails, and real-time voice support. 原文翻译： OpenAI Agents SDK 是一个轻量级、提供商无关的 Python 框架，用于构建具有沙盒代理、工具、护栏和实时语音支持等功能的多智能体工作流。