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过去两个月出现质量下降。根本原因是三个独立变…","tags":["Claude Code","Anthropic","Postmortem","AI 工具质量","Prompt 工程","上下文管理"]},{"id":"blog:blog-054","type":"blog","title":"GPT-5.5 vs DeepSeek V4 终极对比：OpenAI 旗舰与中国开源先锋的正面交锋","href":"/blog/blog-054","subtitle":"2026 年 4 月 23-24 日，OpenAI GPT-5.5 与 DeepSeek V4 系列（V4-Pro + V4-Flash）在不到 24 小时内…","tags":["GPT-5.5","DeepSeek V4","模型对比","API 实战","成本分析","混合策略"]},{"id":"blog:blog-051","type":"blog","title":"自进化 AI Agent 双路线深度对比：GenericAgent 技能树生长 vs Evolver GEP 基因组进化——架构、代码与实战指南","href":"/blog/blog-051","subtitle":"2026 年 4 月，GitHub 上两款自进化 Agent 项目同时进入 Trending：GenericAgent（6,726 星，周增 3,536）和 …","tags":["自进化Agent","GenericAgent","Evolver","GEP","技能树","架构对比"]},{"id":"blog:blog-050","type":"blog","title":"GPT-5.5 深度解读：OpenAI 的半官方旗舰模型——Codex 通道抢先体验与竞争格局分析","href":"/blog/blog-050","subtitle":"2026 年 4 月 23 日，OpenAI 正式发布 GPT-5.5，率先登陆 Codex 编码助手。Simon Willison 评价其\"快速、高效、高度…","tags":["GPT-5.5","OpenAI","Codex","模型评测","Agentic 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实战指南","href":"/blog/blog-046","subtitle":"2026 年 4 月 22 日，通义千问发布 Qwen3.6-27B——一款仅 27B 参数的密集模型，在所有主要编程基准上超越了前代 397B MoE 旗舰…","tags":["Qwen3.6","开源模型","本地部署","Agentic Coding","llama.cpp","AI Agent"]},{"id":"blog:blog-045","type":"blog","title":"AI 安全的里程碑：Claude Mythos 在 Firefox 中发现 271 个漏洞——从 Bobby Holley 的「Defender's Moment」看 AI 安全评估新纪元","href":"/blog/blog-045","subtitle":"2026 年 4 月，Anthropic 与 Mozilla 合作，使用 Claude Mythos Preview 对 Firefox 进行安全评估，单次发…","tags":["AI 安全","Claude Mythos","Firefox","漏洞挖掘","安全审计","Anthropic"]},{"id":"blog:blog-044","type":"blog","title":"开源语音 AI 革命 2026：Voicebox + VoxCPM2 实战指南，从零搭建自己的语音合成系统","href":"/blog/blog-044","subtitle":"2026 年 4 月，开源语音 AI 迎来爆发——Voicebox（22K 星）和 VoxCPM2（15K 星）两大项目引领开源 TTS 革命。本文深度解析 …","tags":["TTS","语音合成","Voicebox","VoxCPM","声音克隆","开源 AI"]},{"id":"blog:blog-043","type":"blog","title":"2026 AI 编码工具横评：OpenAI Codex vs Claude Code vs GitHub Copilot，谁最值得用？","href":"/blog/blog-043","subtitle":"2026 年 4 月 AI 编码工具定价大地震后的终极横评。从编码能力、架构设计、定价策略、安全特性四个维度深度对比 OpenAI Codex、Claude …","tags":["AI 编码","OpenAI Codex","Claude Code","GitHub Copilot","横评","定价分析"]},{"id":"blog:blog-042","type":"blog","title":"AI 编码工具定价战争：Claude Code、Copilot、Codex 的 48 小时定价地震与开发者应对指南","href":"/blog/blog-042","subtitle":"2026 年 4 月，三大 AI 编码工具在 48 小时内相继调整定价策略：GitHub Copilot 暂停注册并引入 token 用量限制、Anthrop…","tags":["AI 编码","Claude Code","GitHub Copilot","OpenAI Codex","Cursor","SpaceX"]},{"id":"blog:blog-041","type":"blog","title":"推测解码（Speculative Decoding）2026 全景解析：从 Medusa 到 DFlash，LLM 推理加速的四大技术路线与 Python 实战","href":"/blog/blog-041","subtitle":"2026 年 4 月，推测解码成为 LLM 推理加速最活跃的研究方向。DFlash（z-lab/dflash）以 Block Diffusion 新范式单周 …","tags":["推测解码","推理加速","DFlash","LLM 优化","Speculative Decoding"]},{"id":"blog:blog-040","type":"blog","title":"Claude Opus 4.7 Tokenizer 变革全解析：成本暴涨 46% 背后的技术原理、多模型对比与智能路由实战","href":"/blog/blog-040","subtitle":"Anthropic Claude Opus 4.7 首次更换 tokenizer，导致 token 消耗系统性增长 46%。本文深度解析 BPE 分词器原理、…","tags":["LLM","Claude","Tokenizer","成本优化","模型选型","智能路由"]},{"id":"blog:blog-039","type":"blog","title":"自进化 AI Agent 全景解析：Hermes 107K 星背后的三大技术路线与混合架构实战","href":"/blog/blog-039","subtitle":"2026 年 4 月，自进化 AI Agent 成为 GitHub 最热赛道——Hermes Agent 一周暴涨 30,630 星突破 107K，Gener…","tags":["行业洞察","前沿动态"]},{"id":"blog:blog-038","type":"blog","title":"Multi-Agent Orchestration 崛起 2026：从 Hermes 107K 星到 Multica 托管平台，多智能体如何重塑 AI 应用架构","href":"/blog/blog-038","subtitle":"2026 年 4 月，Multi-Agent 项目集体爆发：Hermes Agent 107K 星、ai-hedge-fund 56K 星、Multica 1…","tags":["Multi-Agent","Agent 编排","Hermes Agent","Multica","LangGraph","CrewAI"]},{"id":"blog:blog-037","type":"blog","title":"Voice AI 全面爆发：从 Voicebox 到 VoxCPM2，2026 年语音 AI 技术全景与实战","href":"/blog/blog-037","subtitle":"2026 年 4 月，Voice AI 成为 GitHub 增长最快的赛道。Voicebox（21K 星）、VoxCPM2（15K 星）、Omi（11K 星）…","tags":["Voice AI","TTS","语音合成","Voicebox","VoxCPM","Omi"]},{"id":"blog:blog-036","type":"blog","title":"AI Agent 记忆系统爆发：Claude-Mem 单周 14K 星 + MemPalace 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万美元融资，用于构建一个没有算法推荐和广告的私密社交网络，押注「小规模真实连接…","tags":["融资"],"extraHaystack":"techcrunch"},{"id":"news:news-6708","type":"news","title":"Monday.com 裁员约 630 人（20%），全面重组聚焦 AI","href":"/news/news-6708","subtitle":"TechCrunch 7 月 22 日报道，以色列办公软件公司 Monday.com 宣布裁员约 630 人，占总员工数 20%，作为重组计划的一部分，将投资…","tags":["行业"],"extraHaystack":"techcrunch"},{"id":"news:news-6709","type":"news","title":"美图拿出 1 亿元发起 Meitu Hatch Catch，全球寻找 AI 影像 Builder","href":"/news/news-6709","subtitle":"量子位 7 月 23 日报道，美图发起产品挑战赛 Meitu Hatch Catch，投入 1 亿元面向全球寻找已上线并拥有种子用户的 AI 原生影像应用，与…","tags":["应用"],"extraHaystack":"量子位"},{"id":"news:news-6710","type":"news","title":"百度文心助手任务 Agent 登顶 PinchBench v2，超越 Claude、GPT 拿下全球第一","href":"/news/news-6710","subtitle":"量子位 7 月 22 日报道，百度文心助手任务 Agent 以最高分 94.6%、平均分 94.4% 登顶全球工程向 AI 智能体评测榜单 PinchBenc…","tags":["Agent"],"extraHaystack":"量子位"},{"id":"news:news-6711","type":"news","title":"物理 AI 闭环跑通：日冕开物联手远图宣布万台级具身智能部署计划","href":"/news/news-6711","subtitle":"量子位 7 月 22 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网络安全模型 Antares-350M 与 Antares-1B，用于发现代…","tags":["安全"],"extraHaystack":"the decoder"},{"id":"news:news-6694","type":"news","title":"美国开源 AI 实验室 Arcee：中国开源模型并非天生危险","href":"/news/news-6694","subtitle":"TechCrunch 7 月 22 日报道，随着中国开源权重模型能力与人气攀升，围绕其安全性的争论再度升温。美国开源 AI 实验室 Arcee CTO Luc…","tags":["政策"],"extraHaystack":"techcrunch"},{"id":"news:news-6695","type":"news","title":"三星 × Google 智能眼镜实图曝光：每副配摄像头，续航超 Meta，联名 Gentle Monster 与 Warby Parker","href":"/news/news-6695","subtitle":"The Verge 7 月 22 日报道，三星与 Google 合作的 AI 智能眼镜真实外观与首批规格曝光。每副眼镜均配备摄像头，续航优于 Meta 同类产…","tags":["芯片"],"extraHaystack":"the verge"},{"id":"news:news-6696","type":"news","title":"美军 AI token 配额告急：「无限量」套餐并非真正无限量","href":"/news/news-6696","subtitle":"Ars Technica 7 月 22 日报道，美国陆军士兵收到邮件通知，告知其正在快速耗尽 AI token 配额。此事暴露出所谓「无限量」AI 套餐的实际…","tags":["政策"],"extraHaystack":"ars technica"},{"id":"news:news-6697","type":"news","title":"OpenAI Project Camellia 锁定 3.2 吉瓦电力协议，承诺 8000 万美元社区投入","href":"/news/news-6697","subtitle":"The Decoder 7 月 22 日报道，OpenAI 位于佐治亚州 Effingham 县的 Project Camellia 数据中心与 Georgi…","tags":["芯片"],"extraHaystack":"the decoder"},{"id":"news:news-6698","type":"news","title":"GitHub 重构漏洞赏金计划：聚焦提升安全研究员体验","href":"/news/news-6698","subtitle":"GitHub 官方博客 7 月 22 日宣布对其漏洞赏金计划进行重大调整，将重心转向为安全研究员提供更优质的体验。这是 GitHub 安全运营策略的一次结构性…","tags":["行业"],"extraHaystack":"github blog"},{"id":"news:news-6690","type":"news","title":"AMD 拟向 Anthropic 投资至多 50 亿美元，并供给 2GW Instinct MI450 算力","href":"/news/news-6690","subtitle":"The Verge 7 月 22 日报道，AMD 宣布将向 Anthropic 投资至多 50 亿美元，并扩大这家 AI 公司的算力。根据合作，Anthrop…","tags":["融资"],"extraHaystack":"the verge"},{"id":"news:news-6684","type":"news","title":"美国威胁以知识产权窃取为由对中国 AI 模型实施制裁","href":"/news/news-6684","subtitle":"TechCrunch 7 月 21 日报道，美国政府官员暗示可能对部分中国 AI 模型祭出制裁，理由指向知识产权窃取问题。此举被视为继芯片出口管制之后，美方在…","tags":["政策"],"extraHaystack":"techcrunch"},{"id":"news:news-6685","type":"news","title":"Galaxy Unpacked 2026：谷歌 Android 与三星深化端侧 AI 协同","href":"/news/news-6685","subtitle":"谷歌 7 月 22 日在 Android 官方博客发文，配合三星 Galaxy Unpacked 2026 发布，介绍 Android 平台与三星硬件在端侧 …","tags":["产品发布"],"extraHaystack":"google"},{"id":"news:news-6686","type":"news","title":"GitHub Copilot 推出 Canvases：构建交互式 AI 体验的新画布","href":"/news/news-6686","subtitle":"GitHub 7 月 21 日在官方博客介绍 Copilot 的 Canvases 能力，开发者可借助画布构建交互式 AI 体验。该能力将 Copilot 从…","tags":["产品发布"],"extraHaystack":"github blog"},{"id":"news:news-6687","type":"news","title":"OpenAI 推出 ChatGPT 小企业计划，加速 AI 在中小企业落地","href":"/news/news-6687","subtitle":"OpenAI 7 月 21 日通过官方博客宣布推出 ChatGPT 小企业计划，面向中小企业提供针对性的产品与支持，降低 AI 使用门槛。这是 OpenAI …","tags":["产品发布"],"extraHaystack":"openai"},{"id":"news:news-6688","type":"news","title":"2026 浏览器大战升温：AI 原生浏览器扎堆挑战 Chrome 与 Safari","href":"/news/news-6688","subtitle":"TechCrunch 7 月 22 日盘点了 2026 年最受关注的 Chrome 与 Safari 替代浏览器。随着 AI 原生浏览器密集涌现，浏览器市场正…","tags":["行业趋势"],"extraHaystack":"techcrunch"},{"id":"news:news-6689","type":"news","title":"OpenAI 携手埃芬汉县社区共建 AI 基础设施，强化算力本地化布局","href":"/news/news-6689","subtitle":"OpenAI 7 月 22 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官方观点：Agentic AI 时代的科学计算","href":"/news/news-6807","subtitle":"OpenAI 发文阐述 Agent 时代科学计算的变革方向，发布仅 3 小时即获高热度。文章探讨 AI Agent 如何改变科研工作流程，从假设生成到实验设计…","tags":["行业观点"],"extraHaystack":"openai"},{"id":"news:news-6808","type":"news","title":"Flashpaper：无数据库的自毁秘密分享工具","href":"/news/news-6808","subtitle":"Show HN 项目 Flashpaper 实现无需数据库的自毁秘密分享，发布 4.8 小时获关注。采用纯客户端加密，链接访问后自动销毁，适合敏感信息的一次性…","tags":["安全工具"],"extraHaystack":"hacker news"},{"id":"news:news-6809","type":"news","title":"为什么我更喜欢 Opus 5 而非 Fable 5：一位开发者的深度对比","href":"/news/news-6809","subtitle":"HN 热帖（9.7 小时，高热度）从开发者视角深度对比 Claude Opus 5 与 Fable 5，在代码生成、推理深度和指令遵循等维度给出实测结论。","tags":["模型对比"],"extraHaystack":"hacker news"},{"id":"news:news-6810","type":"news","title":"arxiv 研究：\"Uncensored\" 开放 LLM 比基础模型更可观测地\"乐观\"","href":"/news/news-6810","subtitle":"arxiv 论文（4.3 小时前发布）发现经过去审查处理的开放 LLM 在行为评估中表现出可测量的乐观倾向，揭示 RLHF 对齐过程可能引入的行为偏差。","tags":["学术研究"],"extraHaystack":"arxiv"},{"id":"news:news-6811","type":"news","title":"BrowserAct：为 AI Agent 打造浏览器操作层","href":"/news/news-6811","subtitle":"Show HN 项目 BrowserAct 提供 AI Agent 的浏览器操作抽象层，11.4 小时获 11 分。封装 DOM 交互、等待策略和错误恢复，让…","tags":["Agent 工具"],"extraHaystack":"hacker news"},{"id":"news:news-6812","type":"news","title":"AMD MI355X Day 0 部署 Kimi K3：Atom 推理引擎实测","href":"/news/news-6812","subtitle":"AMD 官方发布 MI355X 上 Day 0 部署 Kimi K3 的实测报告，使用 Atom 推理引擎。这是 AMD 在 AI 推理硬件领域的重要进展，直…","tags":["推理硬件"],"extraHaystack":"amd"},{"id":"news:news-6813","type":"news","title":"AI Agent 沙箱替代方案：Podman、bwrap 与 Firejail 实战对比","href":"/news/news-6813","subtitle":"grigio.org 博文系统对比了 Docker 之外的三种 AI Agent 沙箱方案：Podman（无守护进程）、bwrap（轻量 namespace）…","tags":["安全部署"],"extraHaystack":"grigio.org"},{"id":"news:news-6814","type":"news","title":"Minute：macOS 离线会议笔记，Whisper + llama.cpp 本地运行","href":"/news/news-6814","subtitle":"Show HN 项目 Minute 实现 macOS 上的离线会议笔记，使用 Whisper 语音识别 + llama.cpp 本地推理，发布仅 0.9 小时…","tags":["本地 AI"],"extraHaystack":"hacker news"},{"id":"news:news-6815","type":"news","title":"MIT Technology Review：为企业级 Agentic AI 构建生产环境","href":"/news/news-6815","subtitle":"MIT Technology Review 深度报道企业如何为 Agent AI 构建生产级环境，涵盖安全边界、成本控制、审计追踪和合规要求。发布 9.4 小…","tags":["企业 AI"],"extraHaystack":"mit technology review"},{"id":"news:news-6816","type":"news","title":"工具链假设单一人类作者，Agent 打破了这个幻觉","href":"/news/news-6816","subtitle":"开发者 Christopher Meiklejohn 撰文指出，现有内容工具链默认假设单一人类作者，AI Agent 的协作模式正在打破这一设计前提。HN 2…","tags":["行业观点"],"extraHaystack":"hacker news"},{"id":"news:news-6817","type":"news","title":"AI 公司被曝粉碎稀有书籍引发文化争议","href":"/news/news-6817","subtitle":"HN 高热度讨论（785 pts）揭示多家 AI 公司为训练数据获取而粉碎稀有书籍，引发版权与文化保护的激烈辩论。多源验证，可信度高。","tags":["AI 伦理"],"extraHaystack":"hacker news"},{"id":"news:news-6818","type":"news","title":"AI 公司在华盛顿的游说支出创历史新高","href":"/news/news-6818","subtitle":"Financial Times 报道，AI 巨头在华盛顿的游说支出打破历史记录，引发对 AI 政治影响力的深度审视。HN 274 pts。","tags":["AI 政治"],"extraHaystack":"financial times"},{"id":"news:news-6819","type":"news","title":"Ed Zitron 警告：AI 泡沫破裂时 Apple 将「坐视一切燃烧」","href":"/news/news-6819","subtitle":"知名科技评论人 Ed Zitron 撰文分析 Apple 在 AI 泡沫中的风险敞口，认为 Apple 的硬件依赖模式使其在 AI 泡沫破裂时尤为脆弱。Mac…","tags":["AI 泡沫"],"extraHaystack":"macrumors"},{"id":"news:news-6820","type":"news","title":"AI 的新超能力：专注力与执行力","href":"/news/news-6820","subtitle":"Rick Manelius 提出 AI 使用的新范式——不是「更快」而是「更专注」和「更有执行力」。HN 214 pts，引发关于 AI 生产力方法论的深度讨…","tags":["AI 生产力"],"extraHaystack":"rick manelius blog"},{"id":"news:news-6821","type":"news","title":"Cloudflare 推出企业客户 AI 流量管控新工具","href":"/news/news-6821","subtitle":"Cloudflare 官方博客发布面向企业客户的 AI 流量分类管控工具，支持对 Search Agent、Training Crawler、User Age…","tags":["企业 AI"],"extraHaystack":"cloudflare blog"},{"id":"news:news-6822","type":"news","title":"陶哲轩 ICM 2026 报告：AI 时代的数学","href":"/news/news-6822","subtitle":"菲尔兹奖得主陶哲轩在国际数学家大会（ICM 2026）发表大会报告「Mathematics in the Age of AI」，系统阐述 AI 与数学的交叉前…","tags":["AI 学术"],"extraHaystack":"icm 2026"},{"id":"news:news-6823","type":"news","title":"Google BeyondZero：面向 AI 时代的企业安全架构","href":"/news/news-6823","subtitle":"ACM 论文发布 Google BeyondZero 安全框架，为零信任架构下的 AI 工作负载提供系统性安全方案。与 Project Glasswing 互…","tags":["AI 安全"],"extraHaystack":"acm"},{"id":"news:news-6824","type":"news","title":"LearnVector：Andrew Ng 打造 AI 一对一学习体验","href":"/news/news-6824","subtitle":"AI 教育先驱 Andrew Ng 推出 LearnVector 平台，提供 AI 驱动的一对一学习体验。产品发布 12 小时，HN 140 pts。","tags":["AI 教育"],"extraHaystack":"hacker news"},{"id":"news:news-6825","type":"news","title":"教授的「隐形提示」陷阱：32/35 名学生使用 AI 作弊被抓","href":"/news/news-6825","subtitle":"TechSpot 报道，一位教授通过在作业中嵌入隐形提示（invisible prompt）成功识别 32/35 名使用 AI 作弊的学生。HN 105 pt…","tags":["AI 伦理"],"extraHaystack":"techspot"},{"id":"news:news-6826","type":"news","title":"Nvidia 7500 亿美元交易重启循环 AI 恐惧","href":"/news/news-6826","subtitle":"Bloomberg 调查揭示 Nvidia 通过 7500 亿美元的交易网络形成的生态闭环引发市场担忧，涉及客户集中度、循环交易和估值可持续性。HN 81 p…","tags":["AI 投资"],"extraHaystack":"bloomberg"},{"id":"news:news-6827","type":"news","title":"AI 开发者能从 Charles Bukowski 身上学到什么？","href":"/news/news-6827","subtitle":"一篇独特视角的文章探讨 AI 开发者能从「脏现实主义」作家 Bukowski 身上学到的创作哲学——真实、简洁、不修饰。HN 67 pts。","tags":["行业观点"],"extraHaystack":"galjot.si"},{"id":"news:news-6828","type":"news","title":"AI 收入增长快，但还不够快","href":"/news/news-6828","subtitle":"The Economist 分析指出，AI 收入虽然快速增长，但尚不足以支撑当前估值水平。文章系统对比了 AI 投入与产出的时间差。极高可信度。","tags":["AI 商业化"],"extraHaystack":"the economist"},{"id":"news:news-6829","type":"news","title":"芯片股在美亚两市下滑，AI 焦虑冲击投资者","href":"/news/news-6829","subtitle":"BBC 报道，美国和亚洲芯片股因 AI 市场焦虑而下滑，反映投资者对 AI 投资回报的担忧加剧。12 小时内发布，高时效性。","tags":["AI 市场"],"extraHaystack":"bbc"},{"id":"news:news-6830","type":"news","title":"antirez：AI 的真正风险在实验室内部","href":"/news/news-6830","subtitle":"Redis 创始人 antirez 发文指出，AI 的最大风险不是外部滥用，而是来自实验室内部的决策和文化。HN 41 pts，业内深度共鸣。","tags":["AI 安全"],"extraHaystack":"antirez.com"},{"id":"news:news-6831","type":"news","title":"Meta 发布 AI 乐观广告，配乐却是人类灭绝之歌","href":"/news/news-6831","subtitle":"TechCrunch 报道，Meta 推出的 AI 乐观主义广告使用了关于人类灭绝的歌曲配乐，引发舆论哗然。这一公关失误成为 AI 行业叙事管理的反面教材。","tags":["AI 公关"],"extraHaystack":"techcrunch"},{"id":"news:news-6832","type":"news","title":"BEAM 基准 SOTA：小模型在 10M token 记忆任务上取得突破","href":"/news/news-6832","subtitle":"Exabase M-1 在最难的 AI 记忆基准 BEAM（10M token 规模）上取得 SOTA 结果，证明小模型通过专门架构也能处理超长上下文。Sho…","tags":["LLM 技术"],"extraHaystack":"exabase"},{"id":"news:news-6833","type":"news","title":"DeltaNet 家族深度解析：线性注意力变体的演进之路","href":"/news/news-6833","subtitle":"doubleword.ai 技术博客深度解析 DeltaNet 家族（含 Kimi Delta Attention），系统阐述线性注意力变体如何结合 Delt…","tags":["AI 架构"],"extraHaystack":"doubleword.ai"},{"id":"news:news-6834","type":"news","title":"Microsoft 发布 MAI-Cyber-1-Flash：网络安全专用 AI 模型","href":"/news/news-6834","subtitle":"Microsoft 官方发布 MAI-Cyber-1-Flash，集成在 MDASH 安全平台中。这是 Microsoft 自研的网络安全专用模型，与通用 L…","tags":["AI 安全"],"extraHaystack":"microsoft"},{"id":"news:news-6835","type":"news","title":"Bun Rust 重写进展深度分析：Claude Code 后续","href":"/news/news-6835","subtitle":"Lockwood 深度分析 Bun 的 Rust 重写进展，这是 Claude Code 9 天重写 100 万行代码后的后续跟踪。HN 489 pts 超高…","tags":["Agent"],"extraHaystack":"lockwood blog"},{"id":"news:news-6836","type":"news","title":"Jensen Huang 首次发推：为开源 AI 模型辩护","href":"/news/news-6836","subtitle":"Nvidia CEO Jensen Huang 在 X (Twitter) 上的首条帖子是为开源 AI 模型的可访问性辩护，引发行业广泛关注。PCGamer …","tags":["开源 AI"],"extraHaystack":"pcgamer"},{"id":"news:news-6837","type":"news","title":"Stanford SIEPR：AI 就业影响——分离炒作与现实","href":"/news/news-6837","subtitle":"Stanford 经济政策研究所发布深度政策简报，系统分析 AI 对就业的真实影响，区分炒作与现实。HN 299 pts，极高可信度。","tags":["AI 就业"],"extraHaystack":"stanford siepr"},{"id":"news:news-6838","type":"news","title":"形式化验证 3D CSG：信任 93 行规范，而非 1000 行 AI 代码","href":"/news/news-6838","subtitle":"Show HN 项目展示了对 3D CSG 算法的形式化验证——用 93 行规范证明正确性，而非依赖 1000 行 AI 生成的代码。HN 108 pts。","tags":["AI 代码质量"],"extraHaystack":"show hn"},{"id":"news:news-6839","type":"news","title":"Google DeepMind 解散 AlphaFold 团队：AI 战略从科学转向 Gemini","href":"/news/news-6839","subtitle":"据雅虎财经报道，Google DeepMind 已解散其明星 AlphaFold 团队，资源将集中转向 Gemini 大模型。此前 AlphaFold 联合创…","tags":["行业"],"extraHaystack":"yahoo finance"},{"id":"news:news-6840","type":"news","title":"MCP 安全生态爆发：一天涌现 4 款安全审计与门控工具","href":"/news/news-6840","subtitle":"Koodisi MCP（API 安全暴露）、Mcploitable（OWASP 漏洞测试服务器）、Canopii CLI（终端安全评估）和 Release-g…","tags":["MCP 安全"],"extraHaystack":"show hn"},{"id":"news:news-6841","type":"news","title":"AI 记忆基准 BEAM 突破：更小模型在 1000 万 token 测试中达到 SOTA","href":"/news/news-6841","subtitle":"Show HN 项目在 BEAM——业界最难的 AI 记忆基准（1000 万 token 上下文）上达到 SOTA，且使用了更小的模型。这一突破挑战了「越大模…","tags":["基准测试"],"extraHaystack":"show hn"},{"id":"news:news-6842","type":"news","title":"Forbes：最危险的 AI 看起来像你信任的那个","href":"/news/news-6842","subtitle":"Forbes 刊文指出，最危险的 AI 不是那些明显有缺陷的系统，而是那些用户过度信任的 AI。文章探讨了 AI 信任危机中的「隐性风险」——当用户放松警惕时…","tags":["AI 安全"],"extraHaystack":"forbes"},{"id":"news:news-6843","type":"news","title":"Salience Labs：用硅光子学光交换扩展 AI 算力","href":"/news/news-6843","subtitle":"The Next Platform 报道，Salience Labs 正在用硅光子学光学交换技术扩展 AI 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后通过并行注意力层处理，允许模型放大关键 token 信号、减弱不重要 token，是 BERT、…","tags":["dl","nlp"]},{"id":"glossary:attention","type":"glossary","title":"Attention（注意力）","href":"/glossary/attention","subtitle":"根据 Query/Key/Value 计算 token 间相关性权重，使模型动态聚焦上下文中的关键信息，是 Transformer 架构的核心机制。","tags":["dl","llm"],"aliases":["注意力","注意力"]},{"id":"glossary:pretrain","type":"glossary","title":"预训练（Pre-training）","href":"/glossary/pretrain","subtitle":"在大规模无标注语料上做自监督学习（如下一词预测），得到具备通用知识的基座模型。","tags":["llm"],"aliases":["Pre-training","Pre-training"]},{"id":"glossary:sft","type":"glossary","title":"SFT（有监督微调）","href":"/glossary/sft","subtitle":"用指令-回答对微调基座模型，使其从「续写」变为「按指令完成任务」。","tags":["llm"],"aliases":["有监督微调","有监督微调"]},{"id":"glossary:rlhf","type":"glossary","title":"RLHF","href":"/glossary/rlhf","subtitle":"用人类偏好数据训练奖励模型，再以强化学习优化策略模型，使输出更符合人类价值观。","tags":["llm","ethics"],"aliases":["人类反馈强化学习"]},{"id":"glossary:dpo","type":"glossary","title":"DPO","href":"/glossary/dpo","subtitle":"Direct Preference 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Adaptation，只训练低秩分解矩阵，大幅减少可训练参数与显存占用。","tags":["llm","mlops"]},{"id":"glossary:quantization","type":"glossary","title":"量化（Quantization）","href":"/glossary/quantization","subtitle":"降低权重精度（如 FP16→INT8→INT4），减少显存与推理延迟，略损精度。","tags":["llm","mlops"],"aliases":["Quantization","Quantization"]},{"id":"glossary:speculative-decoding","type":"glossary","title":"推测解码（Speculative Decoding）","href":"/glossary/speculative-decoding","subtitle":"Google Research 于 2022 年提出的推理优化技术：小模型（Draft Model）快速生成候选 Token 序列，大模型（Target Mo…","tags":["llm","infer"],"aliases":["Speculative Decoding","Spec Decode","Speculative Decoding"]},{"id":"glossary:fine-tuning","type":"glossary","title":"微调（Fine-tuning）","href":"/glossary/fine-tuning","subtitle":"在特定任务/领域数据上继续训练，使通用模型适配垂直场景。","tags":["llm"],"aliases":["Fine-tuning","Fine-tuning"]},{"id":"glossary:peft","type":"glossary","title":"PEFT（参数高效微调）","href":"/glossary/peft","subtitle":"Parameter-Efficient Fine-Tuning 统称，包括 LoRA、Adapter、Prefix Tuning 等低资源微调方法。","tags":["llm"],"aliases":["参数高效微调","参数高效微调"]},{"id":"glossary:eval-benchmark","type":"glossary","title":"评测基准（Benchmark）","href":"/glossary/eval-benchmark","subtitle":"如 MMLU、HumanEval、GSM8K 等标准化测试集，用于横向对比模型能力。","tags":["llm","mlops"],"aliases":["Benchmark","Benchmark"]},{"id":"glossary:bleu","type":"glossary","title":"BLEU","href":"/glossary/bleu","subtitle":"基于 n-gram 重叠的机器翻译评估指标，越高表示与参考译文越接近。","tags":["nlp"]},{"id":"glossary:perplexity","type":"glossary","title":"Perplexity（困惑度）","href":"/glossary/perplexity","subtitle":"语言模型对测试集的预测不确定性指标，越低表示对数据分布拟合越好；注意与 Perplexity.ai 搜索产品无关。","tags":["nlp","llm"],"aliases":["困惑度","困惑度"]},{"id":"glossary:bert","type":"glossary","title":"BERT","href":"/glossary/bert","subtitle":"基于 Masked LM 的 Encoder-only 模型，擅长分类、NER 等理解任务，不直接做生成。","tags":["nlp"]},{"id":"glossary:gpt","type":"glossary","title":"GPT 系列","href":"/glossary/gpt","subtitle":"Generative Pre-trained Transformer，Decoder-only 自回归架构，GPT-3/4 等是对话与 Agent 的基础。","tags":["llm","nlp"]},{"id":"glossary:rl","type":"glossary","title":"强化学习（RL）","href":"/glossary/rl","subtitle":"智能体通过与环境交互获得奖励信号，学习最大化长期回报的策略。","tags":["rl"],"aliases":["RL","RL"]},{"id":"glossary:ppo","type":"glossary","title":"PPO","href":"/glossary/ppo","subtitle":"Proximal Policy Optimization，通过裁剪目标函数稳定策略更新，广泛用于 LLM 对齐训练。","tags":["rl","llm"]},{"id":"glossary:reward-model","type":"glossary","title":"奖励模型（RM）","href":"/glossary/reward-model","subtitle":"基于人类偏好数据训练，用于 RLHF 中为策略模型生成的回复提供标量奖励。","tags":["llm","rl"],"aliases":["RM","RM"]},{"id":"glossary:alignment","type":"glossary","title":"对齐（Alignment）","href":"/glossary/alignment","subtitle":"使模型行为与人类价值观、指令意图一致，包括 SFT、RLHF、Constitutional AI 等方法。","tags":["ethics","llm"],"aliases":["Alignment","Alignment"]},{"id":"glossary:jailbreak","type":"glossary","title":"Jailbreak（越狱）","href":"/glossary/jailbreak","subtitle":"通过特殊 Prompt 绕过模型的安全护栏，诱导输出违规内容，是红队测试的重点。","tags":["ethics","security"],"aliases":["越狱","越狱"]},{"id":"glossary:prompt-injection","type":"glossary","title":"Prompt 注入","href":"/glossary/prompt-injection","subtitle":"2022 年 5 月由 Jonathan Cefalu 首次报告的网络安全漏洞，攻击者在输入中嵌入恶意指令，利用模型无法区分开发者指令与用户输入的缺陷，劫持 …","tags":["security","agent"]},{"id":"glossary:mlops","type":"glossary","title":"MLOps","href":"/glossary/mlops","subtitle":"机器学习系统的工程化实践，涵盖数据版本、训练流水线、模型部署、监控与回滚。","tags":["mlops"]},{"id":"glossary:vector-db","type":"glossary","title":"向量数据库","href":"/glossary/vector-db","subtitle":"专为高维向量相似度检索优化，RAG 系统的核心组件（如 Pinecone、Milvus、Qdrant）。","tags":["llm","agent"]},{"id":"glossary:chunking","type":"glossary","title":"分块（Chunking）","href":"/glossary/chunking","subtitle":"RAG 中将文档切分为适合 Embedding 与检索的片段，块大小与重叠策略影响召回质量。","tags":["llm"],"aliases":["Chunking","Chunking"]},{"id":"glossary:reranker","type":"glossary","title":"Reranker（重排序）","href":"/glossary/reranker","subtitle":"对向量检索的 Top-K 结果用交叉编码器重新打分，提升最终上下文相关性。","tags":["llm"],"aliases":["重排序","重排序"]},{"id":"glossary:temperature","type":"glossary","title":"Temperature（温度）","href":"/glossary/temperature","subtitle":"采样时调整 logits 分布的平滑度：低温度更确定，高温度更多样但可能更离谱。","tags":["llm","prompt"],"aliases":["温度","温度"]},{"id":"glossary:top-p","type":"glossary","title":"Top-p（Nucleus Sampling）","href":"/glossary/top-p","subtitle":"动态选择累积概率达 p 的最小词集进行采样，平衡多样性与质量。","tags":["llm"],"aliases":["Nucleus Sampling","Nucleus Sampling"]},{"id":"glossary:system-prompt","type":"glossary","title":"System Prompt","href":"/glossary/system-prompt","subtitle":"对话最前端的系统级指令，定义角色、边界与行为准则，用户通常不可见。","tags":["prompt","agent"]},{"id":"glossary:in-context-learning","type":"glossary","title":"In-Context Learning","href":"/glossary/in-context-learning","subtitle":"通过 Prompt 中的示例或指令，模型在推理时适应新任务，是 LLM 涌现能力之一。","tags":["llm"]},{"id":"glossary:emergent-ability","type":"glossary","title":"涌现能力","href":"/glossary/emergent-ability","subtitle":"模型规模达到阈值后出现的未显式训练能力（如 CoT、多步推理），是 scaling law 讨论焦点。","tags":["llm"]},{"id":"glossary:scaling-law","type":"glossary","title":"Scaling Law","href":"/glossary/scaling-law","subtitle":"模型性能与参数量、数据量、算力之间的幂律关系，指导大模型训练资源分配。","tags":["llm"]},{"id":"glossary:multimodal","type":"glossary","title":"多模态（Multimodal）","href":"/glossary/multimodal","subtitle":"统一处理文本、图像、音频等多种模态输入输出的模型（如 GPT-4V、Gemini）。","tags":["multimodal"],"aliases":["Multimodal","Multimodal"]},{"id":"glossary:clip","type":"glossary","title":"CLIP","href":"/glossary/clip","subtitle":"对比学习训练的图文联合嵌入模型，实现零样本图像分类与文生图的基础。","tags":["multimodal","cv"]},{"id":"glossary:whisper","type":"glossary","title":"Whisper","href":"/glossary/whisper","subtitle":"大规模弱监督训练的 ASR 模型，支持多语言语音转文字。","tags":["multimodal"]},{"id":"glossary:tts","type":"glossary","title":"TTS（文字转语音）","href":"/glossary/tts","subtitle":"Text-to-Speech，将文本合成为自然语音，包括 CosyVoice、ElevenLabs 等方案。","tags":["multimodal"],"aliases":["文字转语音","文字转语音"]},{"id":"glossary:gpu-memory","type":"glossary","title":"显存（VRAM）","href":"/glossary/gpu-memory","subtitle":"GPU 专用内存，模型权重 + 激活 + 优化器状态 + KV Cache 共同决定所需显存量。","tags":["mlops","infer"],"aliases":["VRAM","VRAM"]},{"id":"glossary:throughput","type":"glossary","title":"吞吐量（Throughput）","href":"/glossary/throughput","subtitle":"推理服务的关键指标，受 batch size、量化、KV Cache、硬件等影响。","tags":["infer","mlops"],"aliases":["Throughput","Throughput"]},{"id":"glossary:latency","type":"glossary","title":"延迟（Latency）","href":"/glossary/latency","subtitle":"TTFT（Time To First Token）衡量用户感知响应速度，与 Prefill 阶段计算量相关。","tags":["infer"],"aliases":["Latency","Latency"]},{"id":"glossary:open-weights","type":"glossary","title":"开源权重（Open Weights）","href":"/glossary/open-weights","subtitle":"发布可下载的模型权重供本地部署与微调（如 LLaMA、Qwen、DeepSeek）；通常不含完整训练数据，许可证限制商用与再分发。2026 年 7 月更新：A…","tags":["llm"],"aliases":["Open Weights","Open Weights"]},{"id":"glossary:api-inference","type":"glossary","title":"API 推理","href":"/glossary/api-inference","subtitle":"通过云端 API 调用闭源或托管模型，按 token 计费，免运维但需考虑数据隐私与供应商锁定。","tags":["llm","aieng"]},{"id":"glossary:distillation","type":"glossary","title":"知识蒸馏（Knowledge Distillation）","href":"/glossary/distillation","subtitle":"训练小模型（学生模型）模仿大模型（教师模型）的输出分布或中间表示，在更小体积下保留部分性能，是模型压缩和部署的核心技术。","tags":["llm","compression"],"aliases":["Knowledge Distillation","Knowledge Distillation"]},{"id":"glossary:data-flywheel","type":"glossary","title":"数据飞轮","href":"/glossary/data-flywheel","subtitle":"产品使用产生反馈数据 → 改进模型 → 更好产品体验的正向循环。","tags":["aieng","practice"]},{"id":"glossary:agentic-rag","type":"glossary","title":"Agentic RAG","href":"/glossary/agentic-rag","subtitle":"Agent 自主规划检索策略、多轮查询与结果整合，比固定 RAG 流水线更灵活。","tags":["agent","llm"]},{"id":"glossary:memory","type":"glossary","title":"Agent 记忆","href":"/glossary/memory","subtitle":"短期：对话上下文；长期：向量库存储用户偏好与历史，支持跨会话个性化。","tags":["agent"]},{"id":"glossary:planning","type":"glossary","title":"规划（Planning）","href":"/glossary/planning","subtitle":"Agent 将复杂目标分解为子任务序列，ReAct、Plan-and-Execute 等是常见模式。","tags":["agent"],"aliases":["Planning","Planning"]},{"id":"glossary:react","type":"glossary","title":"ReAct","href":"/glossary/react","subtitle":"Reasoning + Acting 框架，交替生成推理步骤与工具调用，是 Agent 的经典范式。","tags":["agent"]},{"id":"glossary:observability","type":"glossary","title":"可观测性（Observability）","href":"/glossary/observability","subtitle":"通过 trace、log、metric 监控 LLM 调用链、延迟、成本与错误，生产部署必备。","tags":["mlops","agent"],"aliases":["Observability","Observability"]},{"id":"glossary:guardrails","type":"glossary","title":"Guardrails（护栏）","href":"/glossary/guardrails","subtitle":"在模型输出前后加入规则/分类器/二次模型，拦截有害、违规或偏离业务的内容。","tags":["ethics","mlops"],"aliases":["护栏","护栏"]},{"id":"glossary:red-teaming","type":"glossary","title":"红队测试","href":"/glossary/red-teaming","subtitle":"模拟对抗性输入发现模型漏洞，是对齐与安全发布前的标准流程。","tags":["ethics","security"]},{"id":"glossary:ai-act","type":"glossary","title":"EU AI Act","href":"/glossary/ai-act","subtitle":"全球首部综合性 AI 立法，按风险分级监管，高风险系统需合规评估与透明度。","tags":["ethics"]},{"id":"glossary:linear-algebra","type":"glossary","title":"线性代数","href":"/glossary/linear-algebra","subtitle":"向量、矩阵、特征值等是理解 Embedding、Attention、梯度计算的数学语言。","tags":["math"]},{"id":"glossary:probability","type":"glossary","title":"概率论","href":"/glossary/probability","subtitle":"贝叶斯推断、期望、方差等是理解损失函数、生成模型与 RL 的基础。","tags":["math"]},{"id":"glossary:calculus","type":"glossary","title":"微积分","href":"/glossary/calculus","subtitle":"偏导数与链式法则是反向传播的理论基础，理解梯度即理解学习过程。","tags":["math"]},{"id":"glossary:loss-function","type":"glossary","title":"损失函数","href":"/glossary/loss-function","subtitle":"衡量预测与真实值差距的标量，训练目标是最小化损失（如 CE、MSE、对比损失）。","tags":["ml","dl"]},{"id":"glossary:epoch","type":"glossary","title":"Epoch","href":"/glossary/epoch","subtitle":"完整遍历训练集一次的训练轮次，过多可能过拟合，需配合 early stopping。","tags":["ml"]},{"id":"glossary:learning-rate","type":"glossary","title":"学习率","href":"/glossary/learning-rate","subtitle":"控制梯度下降步长的超参数，过大震荡、过小收敛慢，常用 warmup + cosine decay。","tags":["ml","dl"]},{"id":"glossary:gpu-cluster","type":"glossary","title":"GPU 集群","href":"/glossary/gpu-cluster","subtitle":"多 GPU 通过数据并行/张量并行/流水线并行协同训练大模型，需高速互联（NVLink/InfiniBand）。","tags":["mlops","llm"]},{"id":"glossary:checkpoint","type":"glossary","title":"Checkpoint","href":"/glossary/checkpoint","subtitle":"定期保存模型权重与优化器状态，支持断点续训与回滚到历史版本。","tags":["mlops"]},{"id":"glossary:a-b-test","type":"glossary","title":"A/B 测试","href":"/glossary/a-b-test","subtitle":"将流量分流对比不同模型/Prompt 的业务指标，是 LLM 产品迭代的科学方法。","tags":["aieng","practice"]},{"id":"glossary:copilot","type":"glossary","title":"Copilot 模式","href":"/glossary/copilot","subtitle":"AI 作为副驾驶辅助人类决策与执行（代码补全、写作建议），人保留最终控制权。","tags":["aieng"]},{"id":"glossary:autonomous-agent","type":"glossary","title":"自主 Agent","href":"/glossary/autonomous-agent","subtitle":"给定目标后可长时间自主运行、调用工具、处理异常的 Agent，需严格安全边界。","tags":["agent"]},{"id":"glossary:skill","type":"glossary","title":"Skill（技能包）","href":"/glossary/skill","subtitle":"结构化的领域知识与操作指南，注入 Agent 上下文使其具备特定能力（如 Cursor Skills、Claude Skills）。","tags":["agent"],"aliases":["技能包","技能包"]},{"id":"glossary:mechanistic-interpretability","type":"glossary","title":"机制可解释性","href":"/glossary/mechanistic-interpretability","subtitle":"分析神经网络内部表征与电路，理解特定行为（如幻觉、欺骗）的 mechanistic 原因。","tags":["ethics","dl"]},{"id":"glossary:agi","type":"glossary","title":"AGI","href":"/glossary/agi","subtitle":"Artificial General Intelligence，在广泛认知任务上达到或超越人类水平的 hypothetical 系统，尚无共识定义。","tags":["ethics"]},{"id":"glossary:slm","type":"glossary","title":"SLM（小语言模型）","href":"/glossary/slm","subtitle":"参数量较小（如 1B–8B）的 LLM，针对边缘部署优化，能力弱于 frontier 但延迟低。","tags":["llm","infer"],"aliases":["小语言模型","小语言模型"]},{"id":"glossary:reasoning-model","type":"glossary","title":"推理模型","href":"/glossary/reasoning-model","subtitle":"如 o1、DeepSeek-R1，通过延长内部推理链（test-time compute）提升复杂任务准确率。","tags":["llm"]},{"id":"glossary:voice-ai","type":"glossary","title":"Voice AI","href":"/glossary/voice-ai","subtitle":"结合 ASR + LLM + TTS 的全双工语音交互系统，低延迟是核心挑战。","tags":["multimodal"]},{"id":"glossary:graph-rag","type":"glossary","title":"Graph RAG","href":"/glossary/graph-rag","subtitle":"将实体关系构建为图结构，结合图遍历与向量检索，适合多跳推理与复杂关系查询。","tags":["llm","agent"]},{"id":"glossary:long-context","type":"glossary","title":"长上下文","href":"/glossary/long-context","subtitle":"通过 RoPE 扩展、Ring Attention 等技术支持 100K–1M token 上下文，减少 RAG 依赖。","tags":["llm"]},{"id":"glossary:ai-chip","type":"glossary","title":"AI 芯片","href":"/glossary/ai-chip","subtitle":"GPU（NVIDIA）、TPU（Google）、NPU 等针对矩阵运算优化的加速器，算力是 scaling 的基础。","tags":["aieng"]},{"id":"glossary:flash-attention","type":"glossary","title":"Flash Attention","href":"/glossary/flash-attention","subtitle":"通过分块计算与 IO 感知优化，在 GPU 显存层次结构上高效实现 Self-Attention，降低长序列训练与推理的显存占用与时延。","tags":["llm","infer"],"aliases":["FlashAttention","闪存注意力"]},{"id":"glossary:zero-shot","type":"glossary","title":"Zero-shot（零样本）","href":"/glossary/zero-shot","subtitle":"不提供任务示例、仅通过自然语言指令让模型完成任务，依赖预训练知识与指令对齐质量。","tags":["llm","prompt"],"aliases":["零样本","零样本学习","Zero-shot Learning","零样本"]},{"id":"glossary:self-supervised","type":"glossary","title":"自监督学习","href":"/glossary/self-supervised","subtitle":"从数据本身构造监督信号（如掩码预测、下一词预测、对比学习），是 BERT、GPT 与 MAE 等预训练范式的核心。","tags":["dl","cv"],"aliases":["自监督","Self-supervised Learning"]},{"id":"glossary:gan","type":"glossary","title":"GAN（生成对抗网络）","href":"/glossary/gan","subtitle":"Generator 与 Discriminator 对抗训练，生成器学习逼真样本，判别器学习区分真伪，曾主导图像生成，现部分被扩散模型取代。","tags":["genai","dl"],"aliases":["生成对抗网络","Generative Adversarial Network","生成对抗网络"]},{"id":"glossary:vae","type":"glossary","title":"VAE（变分自编码器）","href":"/glossary/vae","subtitle":"Variational Autoencoder，将输入编码为潜变量分布并解码重建，提供可采样的连续表示，是扩散模型与表示学习的基础组件之一。","tags":["genai","dl"],"aliases":["变分自编码器","Variational Autoencoder","变分自编码器"]},{"id":"glossary:neural-network","type":"glossary","title":"神经网络","href":"/glossary/neural-network","subtitle":"由可微分的层与激活函数堆叠而成的函数逼近器，通过反向传播与梯度下降从数据中学习参数，是深度学习的基本单元。","tags":["dl","math"],"aliases":["NN","人工神经网络"]},{"id":"glossary:cross-entropy","type":"glossary","title":"交叉熵损失","href":"/glossary/cross-entropy","subtitle":"衡量预测分布与真实标签分布的差异，是分类与语言模型 next-token 训练最常用的损失函数。","tags":["ml","math"],"aliases":["交叉熵","Cross Entropy Loss"]},{"id":"glossary:bf16","type":"glossary","title":"BF16（Brain Float16）","href":"/glossary/bf16","subtitle":"16 位浮点格式，指数位与 FP32 相同，牺牲部分尾数精度以换取更大动态范围，广泛用于大模型训练与推理。","tags":["llm","mlops"],"aliases":["Brain Float16","Brain Float 16","bfloat16","Brain Float16"]},{"id":"glossary:test-time-compute","type":"glossary","title":"Test-time Compute","href":"/glossary/test-time-compute","subtitle":"推理阶段投入更多计算（延长思维链、采样多条路径、自洽投票），以提升复杂任务准确率，o1/R1 等推理模型的核心思路。","tags":["llm","infer"],"aliases":["推理时计算","Test Time Compute","TTC"]},{"id":"glossary:softmax","type":"glossary","title":"Softmax","href":"/glossary/softmax","subtitle":"将 logits 向量归一化为概率分布，温度参数控制尖锐程度，是分类、Attention 权重与语言模型采样输出的基础运算。","tags":["math","dl"],"aliases":["Softmax 函数"]},{"id":"glossary:dropout","type":"glossary","title":"Dropout","href":"/glossary/dropout","subtitle":"以概率 p 随机丢弃神经元输出，迫使网络学习冗余表示，是缓解过拟合的经典正则化手段。","tags":["ml","dl"],"aliases":["随机失活"]},{"id":"glossary:constitutional-ai","type":"glossary","title":"Constitutional AI","href":"/glossary/constitutional-ai","subtitle":"Anthropic 提出的对齐方法：用书面原则指导模型自我修订有害输出，减少对大量人工有害样本标注的依赖。","tags":["ethics","llm"],"aliases":["CAI","宪法 AI"]},{"id":"glossary:grounding","type":"glossary","title":"Grounding（接地）","href":"/glossary/grounding","subtitle":"让模型生成内容锚定在检索证据、传感器数据或知识图谱上，减少幻觉，多模态与 RAG 系统的关键目标。","tags":["llm","multimodal"],"aliases":["接地","Grounded Generation","接地"]},{"id":"glossary:rnn","type":"glossary","title":"RNN（循环神经网络）","href":"/glossary/rnn","subtitle":"Recurrent Neural Network 通过循环状态处理序列数据，曾广泛用于语音、文本与时间序列建模，后多被 Transformer 替代。","tags":["dl","nlp"],"aliases":["循环神经网络","Recurrent Neural Network","循环神经网络"]},{"id":"glossary:lstm","type":"glossary","title":"LSTM（长短期记忆网络）","href":"/glossary/lstm","subtitle":"Long Short-Term Memory 在 RNN 中引入门控机制，缓解长序列训练中的梯度消失问题，常用于语音识别与序列预测。","tags":["dl","nlp"],"aliases":["长短期记忆网络","Long Short-Term Memory","长短期记忆网络"]},{"id":"glossary:svm","type":"glossary","title":"SVM（支持向量机）","href":"/glossary/svm","subtitle":"Support Vector Machine 通过最大化分类间隔学习决策边界，可结合核函数处理非线性分类，是传统机器学习经典算法。","tags":["ml","math"],"aliases":["支持向量机","Support Vector Machine","支持向量机"]},{"id":"glossary:pca","type":"glossary","title":"PCA（主成分分析）","href":"/glossary/pca","subtitle":"Principal Component Analysis 通过线性投影保留数据方差最大的方向，用于降维、可视化、压缩与噪声过滤。","tags":["ml","math"],"aliases":["主成分分析","Principal Component Analysis","主成分分析"]},{"id":"glossary:sgd","type":"glossary","title":"SGD（随机梯度下降）","href":"/glossary/sgd","subtitle":"Stochastic Gradient Descent 用随机样本或小批量估计梯度并更新参数，是深度学习优化器的基础形式。","tags":["ml","dl"],"aliases":["随机梯度下降","Stochastic Gradient Descent","随机梯度下降"]},{"id":"glossary:onnx","type":"glossary","title":"ONNX","href":"/glossary/onnx","subtitle":"Open Neural Network Exchange 是跨框架模型表示格式，可在 PyTorch、TensorFlow、TensorRT、Core ML …","tags":["mlops","aieng"],"aliases":["Open Neural Network Exchange"]},{"id":"glossary:cuda","type":"glossary","title":"CUDA","href":"/glossary/cuda","subtitle":"NVIDIA 的并行计算平台与编程模型，让深度学习框架能够高效调用 GPU 执行矩阵运算与自定义算子。","tags":["mlops","aieng"],"aliases":["Compute Unified Device Architecture"]},{"id":"glossary:svd","type":"glossary","title":"SVD（奇异值分解）","href":"/glossary/svd","subtitle":"Singular Value Decomposition 将矩阵分解为左右奇异向量与奇异值，是降维、推荐系统、压缩与低秩近似的基础工具。","tags":["math","ml"],"aliases":["奇异值分解","Singular Value Decomposition","奇异值分解"]},{"id":"glossary:faiss","type":"glossary","title":"FAISS","href":"/glossary/faiss","subtitle":"Facebook AI Similarity Search 是高性能向量相似度检索库，支持 IVF、PQ、HNSW 等索引，常用于大规模 RAG 检索。","tags":["llm","mlops"],"aliases":["Facebook AI Similarity Search"]},{"id":"glossary:flow-matching","type":"glossary","title":"Flow Matching","href":"/glossary/flow-matching","subtitle":"一种连续生成建模方法，通过学习概率分布间的速度场完成采样，正在图像和视频生成中成为扩散模型的重要替代路线。","tags":["genai","dl"],"aliases":["流匹配"]},{"id":"glossary:fid","type":"glossary","title":"FID","href":"/glossary/fid","subtitle":"Fréchet Inception Distance 用生成图像与真实图像在特征空间的分布距离评估生成质量，常用于 GAN 与扩散模型对比。","tags":["genai","cv"],"aliases":["Fréchet Inception Distance"]},{"id":"glossary:tf-idf","type":"glossary","title":"TF-IDF","href":"/glossary/tf-idf","subtitle":"Term Frequency-Inverse Document Frequency 根据词频与逆文档频率衡量词对文档的重要性，是传统搜索与文本特征工程基础。","tags":["nlp","ml"],"aliases":["词频-逆文档频率","Term Frequency-Inverse Document Frequency"]},{"id":"glossary:vla","type":"glossary","title":"VLA（视觉-语言-动作模型）","href":"/glossary/vla","subtitle":"Vision-Language-Action 模型把视觉输入、语言指令与动作输出统一建模，是具身智能和机器人控制的重要架构。","tags":["multimodal","agent"],"aliases":["视觉-语言-动作模型","Vision-Language-Action","视觉-语言-动作模型"]},{"id":"glossary:ctc","type":"glossary","title":"CTC（连接时序分类）","href":"/glossary/ctc","subtitle":"Connectionist Temporal Classification 允许输入帧与输出标签长度不一致，常用于语音识别、OCR 和手写识别。","tags":["nlp","cv"],"aliases":["连接时序分类","Connectionist Temporal Classification","连接时序分类"]},{"id":"glossary:dbscan","type":"glossary","title":"DBSCAN","href":"/glossary/dbscan","subtitle":"Density-Based Spatial Clustering of Applications with Noise 根据样本密度发现任意形状簇，并能识别噪…","tags":["ml"],"aliases":["Density-Based Spatial Clustering of Applications with Noise"]},{"id":"glossary:mlp","type":"glossary","title":"MLP（多层感知机）","href":"/glossary/mlp","subtitle":"Multi-Layer Perceptron 由多层线性变换和非线性激活组成，是神经网络、Transformer FFN 与许多表格模型的基础结构。","tags":["dl"],"aliases":["多层感知机","Multi-Layer Perceptron","多层感知机"]},{"id":"glossary:dvc","type":"glossary","title":"DVC（数据版本控制）","href":"/glossary/dvc","subtitle":"Data Version Control 用 Git 风格管理数据集、模型与实验产物版本，支持远程存储与可复现实验流水线。","tags":["mlops"],"aliases":["数据版本控制","Data Version Control","数据版本控制"]},{"id":"glossary:qat","type":"glossary","title":"QAT（量化感知训练）","href":"/glossary/qat","subtitle":"Quantization-Aware Training 在训练阶段模拟低比特量化影响，使模型部署到 INT8/INT4 时保持更高精度。","tags":["mlops","infer"],"aliases":["量化感知训练","Quantization-Aware Training","量化感知训练"]},{"id":"glossary:ptq","type":"glossary","title":"PTQ（训练后量化）","href":"/glossary/ptq","subtitle":"Post-Training Quantization 在不重新训练或少量校准的情况下将模型权重量化到低精度，部署成本低但精度损失更依赖校准数据。","tags":["mlops","infer"],"aliases":["训练后量化","Post-Training Quantization","训练后量化"]},{"id":"glossary:rouge","type":"glossary","title":"ROUGE","href":"/glossary/rouge","subtitle":"Recall-Oriented Understudy for Gisting Evaluation 通过 n-gram 或最长公共子序列重叠评估摘要与参考答案…","tags":["nlp"],"aliases":["Recall-Oriented Understudy for Gisting Evaluation"]},{"id":"glossary:vqa","type":"glossary","title":"VQA（视觉问答）","href":"/glossary/vqa","subtitle":"Visual Question Answering 要求模型结合图像内容与自然语言问题生成答案，是多模态理解的经典任务。","tags":["multimodal","cv"],"aliases":["视觉问答","Visual Question Answering","视觉问答"]},{"id":"glossary:variational-quantum-algorithm","type":"glossary","title":"变分量子算法（VQA）","href":"/glossary/variational-quantum-algorithm","subtitle":"Variational Quantum Algorithm 是量子-经典混合算法，在量子设备上评估参数化电路，在经典优化器中更新参数。","tags":["math","aieng"],"aliases":["VQA","Variational Quantum Algorithm","VQA"]},{"id":"glossary:dqn","type":"glossary","title":"DQN（深度 Q 网络）","href":"/glossary/dqn","subtitle":"Deep Q-Network 用神经网络近似 Q 函数，并结合经验回放与目标网络稳定训练，是深度强化学习里程碑算法。","tags":["rl","dl"],"aliases":["深度 Q 网络","Deep Q-Network","深度 Q 网络"]},{"id":"glossary:q-learning","type":"glossary","title":"Q-Learning","href":"/glossary/q-learning","subtitle":"一种无模型强化学习算法，通过更新状态-动作价值函数学习最优策略，是 DQN 等方法的基础。","tags":["rl"],"aliases":["Q Learning","Q 学习"]},{"id":"glossary:ffn","type":"glossary","title":"FFN（前馈网络）","href":"/glossary/ffn","subtitle":"Feed-Forward Network 是 Transformer 每层中逐 token 计算的非线性网络，通常占用大量参数与算力。","tags":["dl","llm"],"aliases":["前馈网络","Feed-Forward Network","前馈网络"]},{"id":"glossary:knn","type":"glossary","title":"KNN（K 近邻）","href":"/glossary/knn","subtitle":"K-Nearest Neighbors 根据样本在特征空间中最近的 K 个邻居进行分类或回归，简单直观但推理成本随数据增长。","tags":["ml"],"aliases":["K 近邻","K-Nearest Neighbors","K 近邻"]},{"id":"glossary:gnn","type":"glossary","title":"GNN（图神经网络）","href":"/glossary/gnn","subtitle":"Graph Neural Network 通过节点与边上的消息传递学习图结构表示，适合分子、社交网络、知识图谱与推荐场景。","tags":["dl","ml"],"aliases":["图神经网络","Graph Neural Network","图神经网络"]},{"id":"glossary:stable-diffusion","type":"glossary","title":"Stable Diffusion","href":"/glossary/stable-diffusion","subtitle":"基于潜空间扩散的图像生成模型，通过文本编码器、U-Net 与 VAE 协同实现高质量文生图和图生图。","tags":["genai"],"aliases":["SD","潜空间扩散模型"]},{"id":"glossary:ddpm","type":"glossary","title":"DDPM","href":"/glossary/ddpm","subtitle":"Denoising Diffusion Probabilistic Models 通过正向加噪和反向去噪学习生成分布，是现代扩散模型的基础形式。","tags":["genai","dl"],"aliases":["Denoising Diffusion Probabilistic Models"]},{"id":"glossary:ddim","type":"glossary","title":"DDIM","href":"/glossary/ddim","subtitle":"Denoising Diffusion Implicit Models 用确定性或少步采样加速扩散模型生成，在质量与速度之间提供灵活权衡。","tags":["genai","infer"],"aliases":["Denoising Diffusion Implicit Models"]},{"id":"glossary:orpo","type":"glossary","title":"ORPO","href":"/glossary/orpo","subtitle":"Odds Ratio Preference Optimization 将监督微调与偏好对齐合并为单阶段目标，减少 DPO/RLHF 的训练流程复杂度。","tags":["llm"],"aliases":["Odds Ratio Preference Optimization"]},{"id":"glossary:gptq","type":"glossary","title":"GPTQ","href":"/glossary/gptq","subtitle":"一种面向 Transformer 权重的训练后量化方法，通过近似二阶信息降低低比特量化误差，常用于 4bit 本地部署。","tags":["llm","infer"],"aliases":["GPT Quantization"]},{"id":"glossary:awq","type":"glossary","title":"AWQ","href":"/glossary/awq","subtitle":"Activation-aware Weight Quantization 根据激活分布保护关键通道，在低比特权重量化时保持大模型精度。","tags":["llm","infer"],"aliases":["Activation-aware Weight Quantization"]},{"id":"glossary:pii","type":"glossary","title":"PII（个人身份信息）","href":"/glossary/pii","subtitle":"Personally Identifiable Information 指姓名、邮箱、手机号、证件号等可定位个人的数据，训练与日志处理中必须脱敏和合规处理。","tags":["security","ethics"],"aliases":["个人身份信息","Personally Identifiable Information","个人身份信息"]},{"id":"glossary:ocr","type":"glossary","title":"OCR（光学字符识别）","href":"/glossary/ocr","subtitle":"Optical Character Recognition 从扫描件、截图或自然场景图像中检测并识别文字，是文档智能与多模态系统基础能力。","tags":["cv","multimodal"],"aliases":["光学字符识别","Optical Character Recognition","光学字符识别"]},{"id":"glossary:gelu","type":"glossary","title":"GELU","href":"/glossary/gelu","subtitle":"Gaussian Error Linear Unit 是平滑非线性激活函数，常用于 BERT、GPT 等 Transformer 的 FFN 中。","tags":["dl","llm"],"aliases":["Gaussian Error Linear Unit"]},{"id":"glossary:hnsw","type":"glossary","title":"HNSW","href":"/glossary/hnsw","subtitle":"Hierarchical Navigable Small World 是高性能近似最近邻搜索索引，常用于向量数据库和大规模语义检索。","tags":["llm","mlops"],"aliases":["Hierarchical Navigable Small World"]},{"id":"glossary:umap","type":"glossary","title":"UMAP","href":"/glossary/umap","subtitle":"Uniform Manifold Approximation and Projection 是非线性降维算法，常用于高维嵌入可视化与聚类探索。","tags":["ml","math"],"aliases":["Uniform Manifold Approximation and Projection"]},{"id":"glossary:vllm","type":"glossary","title":"vLLM","href":"/glossary/vllm","subtitle":"开源 LLM 推理与服务框架，通过 PagedAttention、Continuous Batching 等技术提升吞吐并降低显存碎片。","tags":["infer","llm"],"aliases":["vLLM 推理框架"]},{"id":"glossary:sglang","type":"glossary","title":"SGLang","href":"/glossary/sglang","subtitle":"面向 LLM 的编程与运行时框架，优化批处理调度、前缀缓存与 Agent 多请求场景下的推理效率。","tags":["infer","llm"],"aliases":["Structured Generation Language"]},{"id":"glossary:continuous-batching","type":"glossary","title":"Continuous Batching（连续批处理）","href":"/glossary/continuous-batching","subtitle":"推理服务不按固定 batch 边界等待，而是动态合并新到达请求，提高 GPU 利用率与整体吞吐。","tags":["infer","mlops"],"aliases":["连续批处理","Iteration-level Batching","连续批处理"]},{"id":"glossary:paged-attention","type":"glossary","title":"PagedAttention","href":"/glossary/paged-attention","subtitle":"将 KV Cache 按固定大小分页存储，像操作系统虚拟内存一样减少碎片并支持更长序列与更大并发。","tags":["infer","llm"],"aliases":["分页注意力"]},{"id":"glossary:tree-of-thoughts","type":"glossary","title":"Tree of Thoughts（思维树）","href":"/glossary/tree-of-thoughts","subtitle":"将 CoT 扩展为树状探索，对中间推理步骤分支、评估与回溯，提升复杂规划与推理任务表现。","tags":["agent","prompt"],"aliases":["思维树","ToT","思维树"]},{"id":"glossary:self-consistency","type":"glossary","title":"Self-Consistency（自洽解码）","href":"/glossary/self-consistency","subtitle":"对同一问题多次采样不同推理路径，以多数投票或一致性选择最终答案，无需额外训练即可提升准确率。","tags":["llm","prompt"],"aliases":["自洽解码","Self Consistency","自洽解码"]},{"id":"glossary:model-collapse","type":"glossary","title":"Model Collapse（模型坍缩）","href":"/glossary/model-collapse","subtitle":"模型在合成或反复爬取的低质量数据上持续训练，导致分布漂移、多样性下降与性能退化。","tags":["llm","ethics"],"aliases":["模型坍缩","模型坍缩"]},{"id":"glossary:hybrid-search","type":"glossary","title":"Hybrid Search（混合检索）","href":"/glossary/hybrid-search","subtitle":"结合 BM25 等稀疏检索与向量语义检索，兼顾精确匹配与语义召回，是 RAG 系统的常见增强方案。2026 年 ParadeDB（9100★，Rust/Pos…","tags":["llm","agent","rag"],"aliases":["混合检索","Hybrid Search","混合搜索","Hybrid Retrieval","混合检索"]},{"id":"glossary:semantic-search","type":"glossary","title":"Semantic Search（语义搜索）","href":"/glossary/semantic-search","subtitle":"利用 Embedding 将查询与文档映射到向量空间，按语义相似度检索，适合同义词、跨语言和模糊意图场景。","tags":["llm","nlp"],"aliases":["语义搜索","Semantic Retrieval","语义搜索"]},{"id":"glossary:fairness","type":"glossary","title":"Fairness（公平性）","href":"/glossary/fairness","subtitle":"模型与系统在不同群体、场景下应提供相当的质量与机会，需通过偏见评测、约束训练与流程审计保障。","tags":["ethics"],"aliases":["公平性","公平性"]},{"id":"glossary:bias","type":"glossary","title":"Bias（偏见）","href":"/glossary/bias","subtitle":"训练数据或标注中的系统性偏差导致模型对特定群体、观点产生不公平倾向，是对齐与治理的重点风险。","tags":["ethics","ml"],"aliases":["偏见","Algorithmic Bias","偏见"]},{"id":"glossary:data-poisoning","type":"glossary","title":"Data Poisoning（数据投毒）","href":"/glossary/data-poisoning","subtitle":"攻击者在训练或微调数据中植入恶意样本，使模型在触发条件下产生错误、后门或泄露行为。","tags":["security","ethics"],"aliases":["数据投毒","Training Data Poisoning","数据投毒"]},{"id":"glossary:object-detection","type":"glossary","title":"Object 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的…","tags":["math","ml","practice"],"aliases":["置信区间","CI","信赖区间","置信区间"]},{"id":"glossary:f1-score","type":"glossary","title":"F1 Score（F1 分数）","href":"/glossary/f1-score","subtitle":"F1 分数（F1 Score）是分类模型评估中常用的综合性指标，定义为**精确率**（Precision）与**召回率**（Recall）的调和平均值，公式为…","tags":["ml","nlp","practice"],"aliases":["F1 分数","F1-measure","F-score","F-measure","F1"]},{"id":"glossary:fp8","type":"glossary","title":"FP8（8位浮点）","href":"/glossary/fp8","subtitle":"FP8 是一种使用 8 个比特表示浮点数的低精度数值格式，由 NVIDIA、Arm 和 Intel 于 2022 年联合提出，专为深度学习训练与推理设计。它定…","tags":["infer","ml","llm"],"aliases":["8位浮点","Float8","E4M3","E5M2","8位浮点"]},{"id":"glossary:swe-bench","type":"glossary","title":"SWE-bench（软件工程任务基准）","href":"/glossary/swe-bench","subtitle":"SWE-bench 是一个用于评估大型语言模型（LLM）解决真实软件工程任务能力的基准测试集，由普林斯顿大学和斯坦福大学研究人员于 2023 年提出，发表于 …","tags":["agent","llm","practice"],"aliases":["软件工程任务基准","Software Engineering Benchmark","SWE-bench Lite","SWE-bench 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等方式显式注入。","tags":["dl","llm"],"aliases":["位置编码","PE","位置编码"]},{"id":"glossary:layer-normalization","type":"glossary","title":"Layer Normalization（层归一化）","href":"/glossary/layer-normalization","subtitle":"对神经网络每一层的激活值进行归一化，使均值为 0、方差为 1，加速训练收敛并提升稳定性。","tags":["dl"],"aliases":["层归一化","LayerNorm","LN","层归一化"]},{"id":"glossary:residual-connection","type":"glossary","title":"Residual Connection（残差连接）","href":"/glossary/residual-connection","subtitle":"将输入直接加到输出上，形成 x + F(x) 的结构，缓解深层网络的梯度消失问题，是 Transformer 和 ResNet 的核心设计。","tags":["dl"],"aliases":["残差连接","Skip Connection","跳跃连接","残差连接"]},{"id":"glossary:reflection","type":"glossary","title":"Reflection（反思）","href":"/glossary/reflection","subtitle":"Agent 对自身输出进行评估和修正的能力，通过自我反馈循环改进结果质量，是提升 Agent 可靠性的关键技术。","tags":["agent"],"aliases":["反思","自我反思","Self-Reflection","反思"]},{"id":"glossary:semantic-memory","type":"glossary","title":"Semantic Memory（语义记忆）","href":"/glossary/semantic-memory","subtitle":"Agent 存储抽象概念、事实和通用知识的能力，与情景记忆（具体事件）互补，支撑长期知识积累。","tags":["agent"],"aliases":["语义记忆","语义记忆"]},{"id":"glossary:working-memory","type":"glossary","title":"Working Memory（工作记忆）","href":"/glossary/working-memory","subtitle":"Agent 在处理当前任务时临时保持和操作信息的容量有限的记忆系统，类似人类的短期记忆。","tags":["agent"],"aliases":["工作记忆","工作记忆"]},{"id":"glossary:sora","type":"glossary","title":"Sora","href":"/glossary/sora","subtitle":"OpenAI 开发的文本生成视频模型，能够根据文本描述生成高质量、长时长的视频，支持复杂场景和物理模拟。","tags":["genai","multimodal"]},{"id":"glossary:bm25","type":"glossary","title":"BM25","href":"/glossary/bm25","subtitle":"Best Matching 25，基于词频和逆文档频率的文本相关性评分算法，是信息检索领域的经典方法，常与向量检索结合使用。","tags":["nlp","llm"],"aliases":["Best Matching 25"]},{"id":"glossary:cosine-similarity","type":"glossary","title":"Cosine Similarity（余弦相似度）","href":"/glossary/cosine-similarity","subtitle":"通过计算两个向量夹角的余弦值衡量相似度，值域 [-1, 1]，广泛用于文本语义相似度计算和推荐系统。","tags":["nlp","llm"],"aliases":["余弦相似度","Cosine Distance","余弦相似度"]},{"id":"glossary:smoothquant","type":"glossary","title":"SmoothQuant","href":"/glossary/smoothquant","subtitle":"通过数学变换将激活值的量化难度迁移到权重量，使 W8A8 量化成为可能，在几乎不损失精度的情况下大幅加速推理。","tags":["infer","llm"]},{"id":"glossary:bitsandbytes","type":"glossary","title":"BitsAndBytes","href":"/glossary/bitsandbytes","subtitle":"Hugging Face 生态中的量化库，支持 4/8 位量化和 NF4 等数据类型，是 QLoRA 微调的核心依赖。","tags":["llm","mlops"],"aliases":["bnb"]},{"id":"glossary:tgi","type":"glossary","title":"TGI（Text Generation Inference）","href":"/glossary/tgi","subtitle":"Hugging Face 官方的高性能 LLM 推理服务器，支持连续批处理、张量并行和水印等功能。","tags":["infer","llm"],"aliases":["Text Generation Inference","Text Generation Inference"]},{"id":"glossary:self-reflection","type":"glossary","title":"Self-Reflection（自我反思）","href":"/glossary/self-reflection","subtitle":"Agent 评估自身生成内容的质量并识别错误的机制，通过自我反馈循环持续改进输出可靠性。","tags":["agent"],"aliases":["自我反思","自我反思"]},{"id":"glossary:adversarial-attack","type":"glossary","title":"Adversarial 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CPU、上下文窗口视为 RAM…","tags":["agent","llm"],"aliases":["上下文工程","Context Eng","上下文工程"]},{"id":"glossary:pd-disaggregation","type":"glossary","title":"Prefill-Decode Disaggregation（PD 分离）","href":"/glossary/pd-disaggregation","subtitle":"将 LLM 推理的 Prefill（预填充，处理输入 prompt，计算密集型）和 Decode（解码，逐 token 生成输出，内存带宽密集型）两个阶段分离…","tags":["infer","llm","mlops"],"aliases":["PD 分离","PD Disaggregation","Prefill-Decode Separation","推理阶段分离","DistServe"]},{"id":"glossary:langfuse","type":"glossary","title":"Langfuse","href":"/glossary/langfuse","subtitle":"开源的 AI 工程平台，提供 LLM 应用的全链路追踪（Tracing）、评估（Evaluation）、Prompt 管理和监控功能，支持自托管，框架无关，2…","tags":["agent","infer","mlops"],"aliases":["LLM Observability Platform","开源 LLM 工程平台"]},{"id":"glossary:weaviate-engram","type":"glossary","title":"Weaviate Engram","href":"/glossary/weaviate-engram","subtitle":"Weaviate 于 2026 年 6 月 15 日正式 GA 的托管记忆服务，构建在 Weaviate 向量数据库之上，通过异步 Pipeline 自动从 …","tags":["agent","llm"],"aliases":["Engram","Agent 记忆服务","Memory as a 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…","tags":["aieng","infer"],"aliases":["Model Router","Model Routing","AI Router","LLM Router","Model Router"]},{"id":"glossary:context-compaction","type":"glossary","title":"上下文压缩（Context Compaction）","href":"/glossary/context-compaction","subtitle":"Agent 在对话超过上下文窗口限制时，使用小型模型将历史对话压缩为摘要，释放上下文空间以继续处理新信息的机制。2026 年 Claude Code 源码泄露…","tags":["agent","llm"],"aliases":["Context Compaction","Memory Compaction","Context Compression","上下文摘要","Context Compaction"]},{"id":"glossary:agentjacking","type":"glossary","title":"Agentjacking（AI 代理劫持）","href":"/glossary/agentjacking","subtitle":"2026 年 6 月 Tenet Security 披露的新型 AI 供应链攻击，通过向 Sentry 错误追踪系统注入精心构造的恶意错误报告，利用 MCP …","tags":["security","agent"],"aliases":["AI 代理劫持","Agent Jacking","MCP 注入攻击","Sentry 注入攻击","AI 代理劫持"]},{"id":"glossary:risc-v","type":"glossary","title":"RISC-V（开放指令集架构）","href":"/glossary/risc-v","subtitle":"基于精简指令集（RISC）原则的开放免费指令集架构（ISA），任何人可以基于它设计、制造和销售处理器而无需支付授权费，2026 年正从嵌入式和物联网领域向数据…","tags":["aieng","chip"],"aliases":["开放指令集架构","RISC Five","RISC-V ISA","开放指令集","开放指令集架构"]},{"id":"glossary:vibe-coding","type":"glossary","title":"Vibe Coding（氛围编码）","href":"/glossary/vibe-coding","subtitle":"2025 年 2 月由 Andrej Karpathy 提出的软件开发实践，开发者通过自然语言提示描述项目或任务，由大语言模型自动生成源代码，开发者可能不仔细…","tags":["agent","practice"],"aliases":["氛围编码","Vibecoding","氛围编程","自然语言编程","氛围编码"]},{"id":"glossary:indirect-prompt-injection","type":"glossary","title":"间接提示注入（Indirect Prompt Injection）","href":"/glossary/indirect-prompt-injection","subtitle":"攻击者将恶意指令嵌入 AI 代理可能读取的外部数据源（网页、数据库、错误报告、邮件等），当 AI 代理处理这些数据时将恶意指令误解为合法指令并执行，是 Age…","tags":["security","agent"],"aliases":["Indirect Prompt Injection","间接注入攻击","数据源提示注入","Indirect Prompt Injection"]},{"id":"glossary:anti-distillation","type":"glossary","title":"反蒸馏（Anti-Distillation）","href":"/glossary/anti-distillation","subtitle":"一系列旨在阻止或降低攻击者通过 API 查询系统性提取闭源模型知识的技术方案，包括对抗性微调（AMFS）、输出扰动、模型水印和对抗性提示检测，2026 年因 …","tags":["security","llm","ethics"],"aliases":["Anti-Distillation","模型反蒸馏","蒸馏防护","Anti-Distillation"]},{"id":"glossary:embodied-intelligence","type":"glossary","title":"具身智能（Embodied Intelligence）","href":"/glossary/embodied-intelligence","subtitle":"将人工智能与物理实体（机器人）结合的技术领域，使 AI 系统能够在真实物理世界中感知、决策和行动。2026 年因荣耀「闪电」机器人以 50 分 26 秒完成半…","tags":["agent","multimodal","robotics"],"aliases":["Embodied Intelligence","Embodied AI","具身 AI","Embodied Intelligence"]},{"id":"glossary:adversarial-machine-learning","type":"glossary","title":"对抗性机器学习（Adversarial Machine Learning）","href":"/glossary/adversarial-machine-learning","subtitle":"研究对机器学习算法的攻击与防御的交叉领域，主要攻击类型包括逃逸攻击、数据投毒、拜占庭攻击和模型提取。2026 年因 AI 模型蒸馏防护（反蒸馏）和提示注入攻击…","tags":["security","ml","dl"],"aliases":["Adversarial Machine Learning","Adversarial ML","对抗式机器学习","Adversarial Machine Learning"]},{"id":"glossary:agentic-rl","type":"glossary","title":"Agentic RL（Agentic Reinforcement Learning）","href":"/glossary/agentic-rl","subtitle":"将强化学习应用于 LLM-based Agent，使其在多轮交互、工具调用、环境反馈中通过奖励信号持续优化决策策略的训练范式，是 2025 年继 RLVR/G…","tags":["rl","agent","llm","training"],"aliases":["Agentic Reinforcement Learning","智能体强化学习","Agent RL","Agentic Reinforcement Learning"]},{"id":"glossary:dspark","type":"glossary","title":"DSpark（DeepSeek 推测解码框架）","href":"/glossary/dspark","subtitle":"DeepSeek 于 2026 年 6 月 27 日开源的推测解码（Speculative Decoding）生产级框架，采用半自回归（Semi-Autore…","tags":["llm","infer","inference"],"aliases":["DeepSeek 推测解码框架","DSpark","DeepSeek DSpark","半自回归推测解码","DeepSeek 推测解码框架"]},{"id":"glossary:agent-harness","type":"glossary","title":"Agent Harness（智能体编排层）","href":"/glossary/agent-harness","subtitle":"Agent Harness 是构建在 AI 模型能力与用户需求之间的可靠编排中间层，负责处理超时重试、权限不足、任务回退、超时关闭等模型无法解决的工程问题，决…","tags":["agent","ai-engineering","production-systems"],"aliases":["智能体编排层","Agent Harness","Harness 工程","编排层","Agentic Orchestration"]},{"id":"glossary:token-economics","type":"glossary","title":"Token 经济学（Token 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与标注流水线？","href":"/interview/aieng-data-pipeline-001","subtitle":"采集→清洗→特征→标注（主动学习/质检/一致性）→版本化，并保证离线在线特征一致。","tags":["数据流水线","ETL","数据标注","特征工程"],"extraHaystack":"中级 系统设计"},{"id":"interview:aieng-debug-llm-quality-001","type":"interview","title":"LLM 应用线上输出质量下降，如何排查？","href":"/interview/aieng-debug-llm-quality-001","subtitle":"先看近期变更（模型/Prompt/上游数据）与日志 trace，再查上下文截断、参数漂移，用回归评测集与 A/B 复现定位。","tags":["LLM可观测性","质量回归","Prompt版本","回归评测"],"extraHaystack":"中级 场景"},{"id":"interview:aieng-debug-rag-retrieval-001","type":"interview","title":"RAG 检索结果不相关，如何逐步定位？","href":"/interview/aieng-debug-rag-retrieval-001","subtitle":"先用 retrieval 指标判断是召回还是排序问题，再排查 embedding 模型、分块、query 改写、索引更新与是否需要 rerank。","tags":["RAG","检索召回","Rerank","评测"],"extraHaystack":"中级 场景"},{"id":"interview:aieng-design-agent-product-001","type":"interview","title":"如何设计一个生产级的 AI Agent 产品？","href":"/interview/aieng-design-agent-product-001","subtitle":"规划+工具调用+记忆为内核，叠加护栏权限、可观测、人在回路与失败降级，核心是可靠性与可控性。","tags":["AI 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4","Leanstral","定理证明","软件正确性"],"extraHaystack":"中级 概念"},{"id":"interview:aieng-llm-eval-001","type":"interview","title":"如何评测 LLM 应用质量（离线 / 在线 / LLM-as-judge）？","href":"/interview/aieng-llm-eval-001","subtitle":"离线基准+LLM-as-judge 快速迭代，在线 A/B 看真实指标，关键场景靠人工标注校准。","tags":["LLM 评测","LLM-as-judge","离线评估","在线 A/B"],"extraHaystack":"中级 系统设计"},{"id":"interview:aieng-llm-gateway-001","type":"interview","title":"如何设计一个多模型 LLM 网关（路由 / 限流 / 计费）？","href":"/interview/aieng-llm-gateway-001","subtitle":"统一 API + 按成本/能力路由 + 限流配额 + 计费 + 缓存 + 可观测 + 故障切换，屏蔽多供应商差异。","tags":["LLM 网关","模型路由","限流计费","可观测"],"extraHaystack":"中级 系统设计"},{"id":"interview:aieng-llm-integration-001","type":"interview","title":"如何实现程序与大模型的集成？有哪些方式？","href":"/interview/aieng-llm-integration-001","subtitle":"程序接入大模型的主流方式包括直接调 API 或本地推理、用框架/SDK、Function Calling 工具调用、MCP 标准化接工具、RAG 接知识、结构…","tags":["LLM集成","API","Function Calling","MCP","RAG"],"extraHaystack":"初级 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系统设计"},{"id":"interview:aieng-ml-team-roles-001","type":"interview","title":"一个机器学习团队有哪些角色？如何协作？","href":"/interview/aieng-ml-team-roles-001","subtitle":"产品、数据工程、算法、ML 工程、MLOps、标注、领域专家分工，靠清晰交接物与共同指标协作。","tags":["团队协作","ML 角色","数据契约"],"extraHaystack":"初级 开放"},{"id":"interview:aieng-model-handoff-001","type":"interview","title":"模型从研究到工程如何顺利交接落地？","href":"/interview/aieng-model-handoff-001","subtitle":"交付可复现训练代码/环境/模型卡/评测与推理契约，对齐特征一致性与 SLA，灰度上线避免扔过墙。","tags":["模型交接","工程化","特征一致性"],"extraHaystack":"中级 开放"},{"id":"interview:aieng-modular-rag-001","type":"interview","title":"什么是 Modular RAG / Advanced RAG？更复杂的 RAG 范式有哪些？","href":"/interview/aieng-modular-rag-001","subtitle":"RAG 从 Naive（检索-生成）演进到 Advanced（加预检索改写/路由、后检索 rerank/压缩），再到 Modular（把检索、记忆、路由、融合…","tags":["Modular RAG","Advanced RAG","Self-RAG","GraphRAG","范式演进"],"extraHaystack":"中级 概念"},{"id":"interview:aieng-multi-tier-gateway-001","type":"interview","title":"如何设计一个支持多档位模型的统一 API 网关？以 GPT-5.6 Sol/Terra/Luna 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系统设计"},{"id":"interview:aieng-rag-doc-parsing-001","type":"interview","title":"RAG 索引流程中的文档解析（Parsing）怎么做？有哪些难点？","href":"/interview/aieng-rag-doc-parsing-001","subtitle":"解析是把 PDF/Word/扫描件等原始文件转成干净、带结构的文本，是 RAG 索引的第一步。难点在复杂版面、扫描质量、公式图表与跨页连续性，解析质量直接决定…","tags":["RAG","文档解析","Parsing","OCR","数据预处理"],"extraHaystack":"中级 系统设计"},{"id":"interview:aieng-rag-metadata-filter-001","type":"interview","title":"RAG 如何利用元数据过滤提升检索精度？","href":"/interview/aieng-rag-metadata-filter-001","subtitle":"给 chunk 打来源/时间/作者/类别/权限等元数据，检索时用结构化条件做 pre-filter 或 post-filter 缩小范围，提升精度，支持「最新…","tags":["RAG","元数据过滤","Self-Query","权限隔离"],"extraHaystack":"中级 概念"},{"id":"interview:aieng-rag-multi-kb-001","type":"interview","title":"有多个知识库时，如何兼顾查询效率与准确性并尽量减少幻觉？","href":"/interview/aieng-rag-multi-kb-001","subtitle":"先路由判断 query 该查哪些库以避免全库扫，再并行检索与融合；准确性靠 rerank、来源引用与无依据拒答，减幻觉靠强制基于检索内容并交叉核对。","tags":["RAG","多知识库","检索路由","Rerank","幻觉"],"extraHaystack":"高级 场景"},{"id":"interview:aieng-rag-multi-recall-001","type":"interview","title":"RAG 多路召回如何实现？多路结果的动态权重如何分配？","href":"/interview/aieng-rag-multi-recall-001","subtitle":"多路召回用向量、BM25、不同 embedding 与 chunk 粒度等多条通路并行检索，再用 RRF 或加权融合去重；权重可按验证集贡献或 query 类…","tags":["RAG","多路召回","检索融合","RRF","重排"],"extraHaystack":"中级 系统设计"},{"id":"interview:aieng-rag-production-001","type":"interview","title":"把 RAG 从 Demo 做到生产，要解决哪些问题？","href":"/interview/aieng-rag-production-001","subtitle":"建评测集与检索质量基线、保证数据新鲜、加引用与护栏、控延迟成本、上线后持续监控幻觉。","tags":["RAG","生产化","检索质量","幻觉监控"],"extraHaystack":"高级 系统设计"},{"id":"interview:aieng-rag-semantic-chunking-001","type":"interview","title":"分块时如何规避语义被切割（语义断裂）？有哪些进阶分块策略？","href":"/interview/aieng-rag-semantic-chunking-001","subtitle":"固定长度硬切会在句中或语义转折处切断完整含义，导致单块语义残缺、召回失真。需用结构感知、递归分割、重叠、语义分块与父子块等策略保证每块语义自洽。","tags":["RAG","Chunking","语义分块","检索质量","父文档"],"extraHaystack":"中级 场景"},{"id":"interview:aieng-rag-similarity-threshold-001","type":"interview","title":"RAG 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概念"},{"id":"interview:llm-instruction-tuning-001","type":"interview","title":"什么是指令微调（Instruction Tuning）？它如何让模型学会听从指令？","href":"/interview/llm-instruction-tuning-001","subtitle":"用多样的（指令,回答）对做有监督微调，让基座模型学会把请求映射为期望回答，并泛化到未见任务。","tags":["指令微调","SFT","对齐","泛化"],"extraHaystack":"中级 概念"},{"id":"interview:llm-kv-cache-001","type":"interview","title":"什么是 KV Cache？它如何加速 LLM 推理？","href":"/interview/llm-kv-cache-001","subtitle":"缓存历史 token 的 Key/Value 向量，生成新 token 时避免重复计算 Attention，是推理加速的核心手段。","tags":["KV Cache","推理优化","Attention"],"extraHaystack":"高级 概念"},{"id":"interview:llm-kv-cache-quant-001","type":"interview","title":"KV Cache 量化如何进一步降低显存占用？","href":"/interview/llm-kv-cache-quant-001","subtitle":"把缓存的 K/V 从 FP16 降到 INT8/INT4/FP8，显存随上下文线性下降；需 per-channel/group 缩放控误差。","tags":["KV Cache","量化","显存优化","长上下文"],"extraHaystack":"高级 概念"},{"id":"interview:llm-linear-attention-001","type":"interview","title":"线性注意力如何把注意力复杂度降到 O(n)？","href":"/interview/llm-linear-attention-001","subtitle":"线性注意力用核函数 φ 近似 softmax，借矩阵乘法结合律先算 φ(K)ᵀV，避开 n×n 矩阵，把复杂度降到 O(n)。","tags":["线性注意力","Attention","复杂度","长序列"],"extraHaystack":"高级 概念"},{"id":"interview:llm-local-vs-cloud-001","type":"interview","title":"本地部署大模型 vs 调用云端大模型 API 各有什么优缺点？如何选择？","href":"/interview/llm-local-vs-cloud-001","subtitle":"本地部署数据可控、可深度定制、长期高频成本可控，但前期硬件投入大、运维重、能力可能不及顶尖闭源；云端 API 免运维、弹性、随时用最新最强模型、起步成本低，但…","tags":["大模型部署","本地部署","云端API","成本","数据合规"],"extraHaystack":"初级 概念"},{"id":"interview:llm-long-context-eval-001","type":"interview","title":"如何评估长上下文模型的有效性（如 Needle-in-a-Haystack）？","href":"/interview/llm-long-context-eval-001","subtitle":"把一条事实插入不同深度的长文本测召回，区分「宣称的窗口长度」与「真正可用的有效上下文」。","tags":["长上下文","Needle-in-a-Haystack","评测","上下文窗口"],"extraHaystack":"中级 场景"},{"id":"interview:llm-lora-001","type":"interview","title":"LoRA 微调的原理和优势是什么？","href":"/interview/llm-lora-001","subtitle":"在冻结权重旁路加低秩矩阵 ΔW=BA，只训练少量参数，显著降低显存与存储，效果接近全量微调。","tags":["LoRA","微调","PEFT"],"extraHaystack":"中级 概念"},{"id":"interview:llm-lora-hyperparams-001","type":"interview","title":"LoRA 的超参数（rank、alpha 等）怎么设置？有什么经验法则？","href":"/interview/llm-lora-hyperparams-001","subtitle":"系统讲解 LoRA 关键超参：rank r 决定容量、alpha 控制缩放（ΔW 实际乘 alpha/r）、target_modules 决定加在哪些层、dr…","tags":["LoRA","超参数","rank","alpha","微调"],"extraHaystack":"中级 场景"},{"id":"interview:llm-lora-math-001","type":"interview","title":"LoRA 的数学原理是什么？为什么低秩分解能近似全量微调？","href":"/interview/llm-lora-math-001","subtitle":"LoRA 冻结预训练权重 W0，把权重更新量 ΔW 拆成两个低秩矩阵 B·A 来训练，依据是大模型微调的有效更新本征秩很低，故低秩即可逼近全量微调。","tags":["LoRA","参数高效微调","低秩分解","PEFT","本征秩"],"extraHaystack":"高级 概念"},{"id":"interview:llm-lora-variants-selection-001","type":"interview","title":"LoRA、QLoRA、DoRA、全量微调在不同场景下如何选型？","href":"/interview/llm-lora-variants-selection-001","subtitle":"从显存预算、数据量、效果上限、是否多任务切换四个维度对比全量微调、LoRA、QLoRA、DoRA：全量上限高但成本大易遗忘，LoRA 省显存可多适配器，QLo…","tags":["LoRA","QLoRA","DoRA","全量微调","选型"],"extraHaystack":"中级 概念"},{"id":"interview:llm-lost-in-middle-001","type":"interview","title":"长上下文的「迷失在中间」（Lost in the Middle）是什么？如何缓解？","href":"/interview/llm-lost-in-middle-001","subtitle":"长上下文中模型对中间位置信息利用率低、呈 U 型，关键信息应放首尾。","tags":["长上下文","位置偏置","RAG","检索重排"],"extraHaystack":"中级 概念"},{"id":"interview:llm-mamba-ssm-001","type":"interview","title":"Mamba / 状态空间模型（SSM）相比 Transformer 有什么优势？","href":"/interview/llm-mamba-ssm-001","subtitle":"Mamba 用选择性状态空间，序列长度上线性复杂度、长程建模强，推理是 O(1) 固定状态、无需 KV Cache。","tags":["Mamba","SSM","状态空间模型","长序列"],"extraHaystack":"高级 概念"},{"id":"interview:llm-million-token-context-001","type":"interview","title":"上下文窗口扩展到 100 万 token 时，哪些现有业务场景会发生质变？","href":"/interview/llm-million-token-context-001","subtitle":"百万 token 上下文让整本书、整库代码、长合同一次性喂入成为可能，跨大量文档综合与超长 Agent 会话出现质变，并弱化对 chunk 式 RAG 的依赖…","tags":["长上下文","百万token","RAG","上下文工程","大海捞针"],"extraHaystack":"中级 开放"},{"id":"interview:llm-model-merging-001","type":"interview","title":"模型合并（Model Merging）的核心原理是什么？TIES 和 DARE 解决了什么问题？","href":"/interview/llm-model-merging-001","subtitle":"模型合并通过算术运算组合多个微调模型的权重，无需额外训练即可获得多任务能力。TIES 解决参数冗余和符号冲突，DARE 用随机丢弃+重缩放进一步稀疏化任务向量。","tags":["Model Merging","TIES","DARE","Task Arithmetic","权重融合"],"extraHaystack":"高级 概念"},{"id":"interview:llm-moe-001","type":"interview","title":"混合专家模型（MoE）的原理和优势是什么？","href":"/interview/llm-moe-001","subtitle":"MoE 用门控网络把每个 token 路由到少数专家 FFN，稀疏激活让总参数巨大而单 token 计算量很低，兼顾容量与效率。","tags":["MoE","混合专家","稀疏激活","Gating"],"extraHaystack":"高级 概念"},{"id":"interview:llm-normalization-001","type":"interview","title":"为什么主流大模型多用 RMSNorm 与 Pre-LN？","href":"/interview/llm-normalization-001","subtitle":"RMSNorm 去掉均值中心化只做均方根缩放，更省算更稳；Pre-LN 把归一化放进残差支路前，使深层训练梯度更稳、可免预热。","tags":["RMSNorm","Pre-LN","归一化","Transformer"],"extraHaystack":"高级 概念"},{"id":"interview:llm-orpo-001","type":"interview","title":"ORPO 是什么？它如何把指令微调和偏好对齐合二为一？","href":"/interview/llm-orpo-001","subtitle":"ORPO 是单阶段对齐算法，损失由 SFT 交叉熵加上基于被选/被拒回答几率比的偏好惩罚组成，无需独立的参考模型和奖励模型，把指令微调与偏好对齐合并为一步，比…","tags":["ORPO","偏好对齐","DPO","几率比","对齐"],"extraHaystack":"高级 概念"},{"id":"interview:llm-peft-001","type":"interview","title":"参数高效微调（PEFT）有哪些主流方法？","href":"/interview/llm-peft-001","subtitle":"PEFT 冻结基座只训练少量参数，主流方法含 LoRA/QLoRA、Adapter、Prefix-Tuning、P-Tuning、(IA)³。","tags":["PEFT","LoRA","微调","适配器"],"extraHaystack":"中级 概念"},{"id":"interview:llm-peft-adapter-prefix-001","type":"interview","title":"Adapter Tuning 和 Prefix Tuning 是什么？和 LoRA 有什么区别？","href":"/interview/llm-peft-adapter-prefix-001","subtitle":"Adapter 在 Transformer 层间插入小瓶颈模块只训它；Prefix/Prompt Tuning 在各层前加可训练虚拟前缀；LoRA 给权重加低…","tags":["PEFT","Adapter","Prefix Tuning","LoRA","微调"],"extraHaystack":"中级 概念"},{"id":"interview:llm-perplexity-001","type":"interview","title":"困惑度（Perplexity）衡量的是什么？有哪些局限？","href":"/interview/llm-perplexity-001","subtitle":"困惑度=平均负对数似然取指数，衡量模型对测试文本的预测不确定性；越低越好，但不直接反映下游能力。","tags":["Perplexity","困惑度","语言模型评测","NLL"],"extraHaystack":"中级 概念"},{"id":"interview:llm-prefill-decode-001","type":"interview","title":"LLM 推理的 Prefill 与 Decode 两阶段有什么区别？","href":"/interview/llm-prefill-decode-001","subtitle":"Prefill 并行处理整段 prompt、算力受限、决定首 token 延迟；Decode 逐 token 串行、访存受限、决定吐字速度。","tags":["Prefill","Decode","KV Cache","推理优化"],"extraHaystack":"中级 概念"},{"id":"interview:llm-pretraining-pipeline-001","type":"interview","title":"从预训练到对齐，大模型训练分哪几个阶段？","href":"/interview/llm-pretraining-pipeline-001","subtitle":"预训练学语言与世界知识，SFT 学指令格式，RLHF/DPO 对齐人类偏好，三阶段目标依次递进。","tags":["预训练","SFT","RLHF","对齐"],"extraHaystack":"中级 概念"},{"id":"interview:llm-prompt-caching-001","type":"interview","title":"Prompt Caching（提示缓存）如何降低 LLM 调用成本与延迟？","href":"/interview/llm-prompt-caching-001","subtitle":"缓存固定前缀的 KV 状态，重复请求时跳过该前缀的 prefill 计算，省掉重复算力与延迟。","tags":["Prompt Caching","KV Cache","推理优化","成本"],"extraHaystack":"中级 概念"},{"id":"interview:llm-prompt-cot-001","type":"interview","title":"什么是 Chain-of-Thought（CoT）提示？","href":"/interview/llm-prompt-cot-001","subtitle":"引导模型输出中间推理步骤再给出答案，显著提升复杂推理与数学任务准确率。","tags":["CoT","提示词","推理"],"extraHaystack":"中级 概念"},{"id":"interview:llm-qlora-001","type":"interview","title":"QLoRA 相比 LoRA 有哪些关键改进？","href":"/interview/llm-qlora-001","subtitle":"QLoRA = 4-bit NF4 量化基座 + LoRA 适配器 + 双量化 + 分页优化器，单卡即可微调大模型。","tags":["QLoRA","LoRA","微调","量化"],"extraHaystack":"高级 概念"},{"id":"interview:llm-quantization-001","type":"interview","title":"大模型量化（INT8/INT4）是如何压缩模型的？有什么代价？","href":"/interview/llm-quantization-001","subtitle":"量化把 FP16 权重映射到低比特整数，靠 scale/zero-point 还原，显著降显存与带宽，代价是精度损失，需 GPTQ/AWQ 等算法补偿。","tags":["量化","Quantization","INT8/INT4","推理优化"],"extraHaystack":"中级 概念"},{"id":"interview:llm-reasoning-model-mcp-001","type":"interview","title":"为什么有些推理模型（Reasoning Model）不支持 MCP / Function Calling？","href":"/interview/llm-reasoning-model-mcp-001","subtitle":"不是根本做不到，而是早期推理模型训练聚焦「长链思维再作答」、未做工具调用对齐，叠加格式冲突与产品定位取舍；新一代已逐步补齐。","tags":["推理模型","Function Calling","MCP","CoT"],"extraHaystack":"中级 概念"},{"id":"interview:llm-reasoning-models-001","type":"interview","title":"o1 / R1 这类推理模型与普通 LLM 有什么不同（Test-Time Compute）？","href":"/interview/llm-reasoning-models-001","subtitle":"推理模型在推理时生成长 CoT、用 RL 训练自我探索与验证，拿 test-time compute 换正确率，擅长数学与代码。","tags":["推理模型","Test-Time Compute","长思维链"],"extraHaystack":"高级 概念"},{"id":"interview:llm-reduce-hallucination-prod-001","type":"interview","title":"生产环境中如何系统性降低 LLM 幻觉？","href":"/interview/llm-reduce-hallucination-prod-001","subtitle":"多层组合：RAG 接地+引用、降温度、约束输出、提升检索质量、事实校验/自一致、拒答兜底、评测监控。","tags":["幻觉","RAG","事实性","可靠性"],"extraHaystack":"中级 场景"},{"id":"interview:llm-repetition-001","type":"interview","title":"LLM 重复生成（Repetition）的成因与缓解方法？","href":"/interview/llm-repetition-001","subtitle":"贪心/低温与训练分布致重复，可用 repetition/no-repeat-ngram 惩罚、采样、提示约束缓解。","tags":["重复生成","解码策略","采样","repetition penalty"],"extraHaystack":"中级 概念"},{"id":"interview:llm-rlhf-001","type":"interview","title":"RLHF 和 DPO 有什么区别？各自适用什么场景？","href":"/interview/llm-rlhf-001","subtitle":"RLHF 通过奖励模型 + 强化学习优化偏好；DPO 直接用偏好对优化策略，省去 RM 训练，更稳定易部署。","tags":["RLHF","DPO","对齐","SFT"],"extraHaystack":"高级 概念"},{"id":"interview:llm-rlhf-pipeline-001","type":"interview","title":"RLHF 的完整训练流程是怎样的？从 SFT 到 Reward Model 到 PPO 每阶段做了什么？","href":"/interview/llm-rlhf-pipeline-001","subtitle":"RLHF 分三阶段：先用人工示范数据做 SFT 得到初始策略，再用人工排序数据训练奖励模型给回答打分，最后用 PPO 强化学习按奖励更新策略并加 KL 惩罚防…","tags":["RLHF","SFT","奖励模型","PPO","对齐"],"extraHaystack":"中级 概念"},{"id":"interview:llm-rope-001","type":"interview","title":"旋转位置编码（RoPE）的原理是什么？相比绝对位置编码有何优势？","href":"/interview/llm-rope-001","subtitle":"RoPE 用旋转矩阵对 Q/K 做位置相关旋转，使注意力点积只依赖相对位置，兼具相对编码语义与外推能力。","tags":["RoPE","位置编码","注意力机制","long-context"],"extraHaystack":"高级 概念"},{"id":"interview:llm-sampling-001","type":"interview","title":"解码时 Temperature、Top-k、Top-p 采样有什么区别？","href":"/interview/llm-sampling-001","subtitle":"Temperature 缩放 logits 调整分布陡峭度，Top-k 截断到前 k 个候选，Top-p 按累积概率动态截断，三者常组合使用。","tags":["采样","Temperature","Top-p","Decoding"],"extraHaystack":"中级 概念"},{"id":"interview:llm-scaling-laws-001","type":"interview","title":"什么是大模型的 Scaling Law？Chinchilla 给了什么启示？","href":"/interview/llm-scaling-laws-001","subtitle":"损失随参数、数据、算力以幂律下降；Chinchilla 指出固定算力下应让参数与训练 token 同比放大，约 20 token/参数为最优。","tags":["Scaling Law","Chinchilla","预训练","算力分配"],"extraHaystack":"中级 概念"},{"id":"interview:llm-sft-dataset-001","type":"interview","title":"如何构建高质量的 SFT 微调数据集？数据质量和数量哪个更重要？","href":"/interview/llm-sft-dataset-001","subtitle":"SFT 数据要覆盖足够的任务分布与指令多样性，并保证答案高质量、格式统一、去重去噪。质量远比数量重要，但前提是多样性足够。","tags":["SFT","数据质量","指令微调","数据蒸馏","LIMA"],"extraHaystack":"中级 场景"},{"id":"interview:llm-speculative-decoding-001","type":"interview","title":"投机解码（Speculative Decoding）是如何加速 LLM 推理的？","href":"/interview/llm-speculative-decoding-001","subtitle":"小草稿模型连续猜多个 token，大模型一次前向并行验证，接受的直接用、首个被拒处回退，输出分布与大模型一致。","tags":["投机解码","Speculative Decoding","推理加速","Draft Model"],"extraHaystack":"高级 概念"},{"id":"interview:llm-streaming-001","type":"interview","title":"LLM 流式输出（Streaming）如何实现？有什么好处？","href":"/interview/llm-streaming-001","subtitle":"流式用 SSE 逐 token 返回，降低首字延迟（TTFT）、改善交互体验。","tags":["Streaming","SSE","TTFT","延迟"],"extraHaystack":"初级 概念"},{"id":"interview:llm-temperature-tuning-001","type":"interview","title":"调参时 Temperature 设高设低分别适合什么任务？","href":"/interview/llm-temperature-tuning-001","subtitle":"Temperature 低（0~0.3）适合事实/抽取/代码，高（0.7~1）适合创意发散。","tags":["Temperature","采样","解码","调参"],"extraHaystack":"初级 场景"},{"id":"interview:llm-test-time-scaling-001","type":"interview","title":"为什么「推理时扩展」（Test-Time Scaling）能提升模型能力？","href":"/interview/llm-test-time-scaling-001","subtitle":"推理时增加采样/搜索/思考步数（best-of-n、MCTS、长 CoT）提升正确率，与训练时 scaling 互补。","tags":["Test-Time Scaling","推理时计算","长思维链"],"extraHaystack":"高级 概念"},{"id":"interview:llm-tokenizer-001","type":"interview","title":"大模型中的 Tokenizer 是什么？BPE 如何工作？","href":"/interview/llm-tokenizer-001","subtitle":"Tokenizer 将文本切分为 token 并映射为 ID；BPE 迭代合并高频字符对，平衡词表大小与未登录词问题。","tags":["Tokenizer","BPE","Token"],"extraHaystack":"中级 概念"},{"id":"interview:llm-training-data-quality-001","type":"interview","title":"为什么少量高质量数据有时比海量新数据更重要？","href":"/interview/llm-training-data-quality-001","subtitle":"数据规模重要，但真正决定训练效率的是信号密度、去重质量、覆盖结构、标注一致性和任务相关性。","tags":["训练数据","数据质量","大模型训练","代码模型"],"extraHaystack":"中级 概念"},{"id":"interview:llm-watermark-001","type":"interview","title":"LLM 文本水印（Watermark）如何实现与检测？","href":"/interview/llm-watermark-001","subtitle":"生成时按密钥把词表分 green/red 并偏置采样 green-list；检测统计 green 比例做假设检验，改写攻击会削弱。","tags":["文本水印","AI 内容检测","采样偏置","溯源"],"extraHaystack":"中级 概念"},{"id":"interview:math-bayesian-frequentist-001","type":"interview","title":"贝叶斯学派与频率学派有什么区别？","href":"/interview/math-bayesian-frequentist-001","subtitle":"频率派视参数为固定常数靠抽样分布推断；贝叶斯派视参数为随机变量，后验∝似然×先验。","tags":["贝叶斯","频率学派","统计推断"],"extraHaystack":"中级 概念"},{"id":"interview:math-bootstrap-001","type":"interview","title":"Bootstrap 重采样如何估计统计量的不确定性？","href":"/interview/math-bootstrap-001","subtitle":"Bootstrap 通过有放回重采样近似统计量的抽样分布，无需分布假设即可估计标准误与置信区间。","tags":["Bootstrap","重采样","置信区间"],"extraHaystack":"中级 概念"},{"id":"interview:math-covariance-correlation-001","type":"interview","title":"协方差与相关系数有什么区别？","href":"/interview/math-covariance-correlation-001","subtitle":"协方差有量纲、范围无界；相关系数归一化到[-1,1]、无量纲，仅刻画线性相关。","tags":["协方差","相关系数","统计基础"],"extraHaystack":"初级 概念"},{"id":"interview:math-define-and-distinguish-between-popul-047","type":"interview","title":"统计学中总体与样本如何定义与区分？","href":"/interview/math-define-and-distinguish-between-popul-047","subtitle":"总体（population）是关心的全部个体及其分布参数；样本（sample）是从总体抽取的子集，用统计量估计未知参数。","tags":["math","Define"],"extraHaystack":"高级 概念"},{"id":"interview:math-define-confidence-interval-and-its-i-017","type":"interview","title":"什么是置信区间？在统计学中为何重要？","href":"/interview/math-define-confidence-interval-and-its-i-017","subtitle":"置信区间是在重复抽样下，有指定比例（如 95%）会覆盖真实参数值的区间范围；比单点估计更能表达不确定性，是推断统计的核心工具。","tags":["math","Define"],"extraHaystack":"高级 概念"},{"id":"interview:math-describe-the-difference-between-disc-054","type":"interview","title":"离散概率分布与连续概率分布有何区别？","href":"/interview/math-describe-the-difference-between-disc-054","subtitle":"离散分布取可数点值（PMF）；连续分布取区间值（PDF，概率用密度积分）；二者都可用 CDF 描述 P(X≤x)。","tags":["math","Describe"],"extraHaystack":"高级 概念"},{"id":"interview:math-describe-what-a-p-value-is-and-what--089","type":"interview","title":"什么是 p 值？它如何反映统计显著性？","href":"/interview/math-describe-what-a-p-value-is-and-what--089","subtitle":"p 值是在 H0 为真时，观察到当前或更极端数据的概率；p<α 时称结果「统计显著」，但不等于效应重要或 H0 为假的概率。","tags":["math","Describe"],"extraHaystack":"中级 概念"},{"id":"interview:math-entropy-kl-001","type":"interview","title":"信息熵、交叉熵与 KL 散度有什么关系？","href":"/interview/math-entropy-kl-001","subtitle":"熵是分布自身不确定性，交叉熵=熵+KL散度，KL 衡量两分布差异且非对称、非负。","tags":["信息熵","交叉熵","KL散度"],"extraHaystack":"中级 概念"},{"id":"interview:math-estimator-properties-001","type":"interview","title":"估计量的无偏性、有效性与一致性是什么？","href":"/interview/math-estimator-properties-001","subtitle":"无偏=期望等于真值，有效=方差最小，一致=样本量趋于无穷时收敛到真值。","tags":["估计量","无偏性","一致性"],"extraHaystack":"中级 概念"},{"id":"interview:math-explain-the-concepts-of-type-i-and-t-129","type":"interview","title":"假设检验中的 I 类错误与 II 类错误是什么？","href":"/interview/math-explain-the-concepts-of-type-i-and-t-129","subtitle":"I 类错误是 H0 为真却拒绝（α）；II 类错误是 H0 为假却不拒绝（β）；功效 = 1−β 是正确检出真实效应的概率。","tags":["math","Explain","Type","II"],"extraHaystack":"中级 概念"},{"id":"interview:math-explain-the-properties-of-a-normal-d-025","type":"interview","title":"正态分布有哪些性质？","href":"/interview/math-explain-the-properties-of-a-normal-d-025","subtitle":"正态分布 N(μ, σ²) 呈钟形、关于 μ 对称；68% 数据在 μ±σ 内；许多统计方法依赖或近似正态。","tags":["math","Explain","Normal"],"extraHaystack":"高级 概念"},{"id":"interview:math-explain-what-a-distribution-is-in-st-097","type":"interview","title":"统计学中的「分布」是什么？常见分布有哪些？","href":"/interview/math-explain-what-a-distribution-is-in-st-097","subtitle":"概率分布规定随机变量各取值（或区间）的概率/密度；常见有正态、二项、泊松、指数、均匀等，分别建模不同随机现象。","tags":["math","Explain"],"extraHaystack":"中级 概念"},{"id":"interview:math-gradient-hessian-001","type":"interview","title":"梯度、Jacobian 与 Hessian 在优化中分别是什么？","href":"/interview/math-gradient-hessian-001","subtitle":"梯度是一阶方向，Jacobian 是向量值函数的一阶导矩阵，Hessian 是二阶曲率、判定凸性。","tags":["梯度","Hessian","Jacobian"],"extraHaystack":"中级 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的概率（1−β）；功效不足易漏检真实效应。","tags":["math","What"],"extraHaystack":"中级 概念"},{"id":"interview:math-what-is-a-null-hypothesis-and-an-alt-117","type":"interview","title":"什么是零假设与备择假设？","href":"/interview/math-what-is-a-null-hypothesis-and-an-alt-117","subtitle":"零假设 H0 是默认/无效应陈述；备择假设 H1 是研究者想寻找证据支持的陈述；检验用数据判断是否有足够证据拒绝 H0。","tags":["math","What"],"extraHaystack":"中级 概念"},{"id":"interview:math-what-is-bayes-theorem-and-how-is-it--123","type":"interview","title":"贝叶斯定理是什么？在统计学中如何应用？","href":"/interview/math-what-is-bayes-theorem-and-how-is-it--123","subtitle":"贝叶斯定理通过似然与先验更新为后验：P(θ|data) ∝ P(data|θ)P(θ)；是贝叶斯统计与许多 ML 分类器的核心。","tags":["math","What","Bayes","Theorem"],"extraHaystack":"中级 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API？","href":"/interview/methodology-api-design-001","subtitle":"一致命名、清晰资源与动词、版本化、幂等、合理状态码与错误结构、分页限流、向后兼容、文档示例。","tags":["API 设计","RESTful","版本化","向后兼容"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-bdd-001","type":"interview","title":"行为驱动开发（BDD）与 TDD 有什么区别？","href":"/interview/methodology-bdd-001","subtitle":"BDD 从用户行为出发用 Given-When-Then 对齐业务，与 TDD 互补。","tags":["BDD","TDD","Gherkin"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-clean-code-001","type":"interview","title":"整洁代码（Clean Code）有哪些核心原则？","href":"/interview/methodology-clean-code-001","subtitle":"命名表意、小函数单一职责、DRY、清晰胜过聪明、显式错误处理。","tags":["整洁代码","可读性","代码质量"],"extraHaystack":"初级 概念"},{"id":"interview:methodology-code-review-001","type":"interview","title":"高质量的代码评审（Code Review）应该关注什么？","href":"/interview/methodology-code-review-001","subtitle":"从正确性/可读性/设计/测试/安全性能切入，小批量、对事不对人、自动化先行。","tags":["CodeReview","代码质量","协作"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-data-validation-001","type":"interview","title":"如何对数据做校验与测试（数据契约）？","href":"/interview/methodology-data-validation-001","subtitle":"在管线入口用 schema/类型/范围/空值/唯一性/分布检查拦截脏数据，定义数据契约，保证训练-服务一致。","tags":["数据校验","数据契约","数据漂移","Great Expectations"],"extraHaystack":"中级 场景"},{"id":"interview:methodology-design-patterns-001","type":"interview","title":"常用设计模式有哪些？分别解决什么问题？","href":"/interview/methodology-design-patterns-001","subtitle":"按创建型/结构型/行为型三类掌握典型模式及其解决的耦合与复用问题。","tags":["设计模式","面向对象","解耦"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-documentation-001","type":"interview","title":"工程文档应该怎么写才有用？","href":"/interview/methodology-documentation-001","subtitle":"面向读者写，分清 README/ADR/API 文档/runbook，讲清为什么与怎么用，靠近代码、示例优先、可维护。","tags":["工程文档","README","ADR","Runbook"],"extraHaystack":"初级 概念"},{"id":"interview:methodology-eval-driven-dev-001","type":"interview","title":"评测驱动开发（Eval-Driven Development）LLM 应用怎么做？","href":"/interview/methodology-eval-driven-dev-001","subtitle":"先建评测集与指标再迭代提示/模型；用离线评测（规则/LLM-as-judge/人工）驱动开发，并在 CI 中跑评测防回归。","tags":["评测驱动开发","LLM 评测","LLM-as-judge","CI 回归"],"extraHaystack":"中级 场景"},{"id":"interview:methodology-feature-flags-001","type":"interview","title":"特性开关（Feature Flags）有什么用？如何落地？","href":"/interview/methodology-feature-flags-001","subtitle":"用开关控制功能上线，支持灰度/AB/快速回滚、解耦发布与部署；注意开关债务与配置一致性。","tags":["特性开关","灰度发布","AB 测试","快速回滚"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-git-workflow-001","type":"interview","title":"Git 分支策略（Git Flow 与 Trunk-Based）如何选择？","href":"/interview/methodology-git-workflow-001","subtitle":"Git Flow 多分支适合长发布周期，Trunk-Based 主干开发适合持续交付。","tags":["Git","分支策略","持续交付"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-mvp-001","type":"interview","title":"MVP（最小可行产品）思维在 AI 项目中如何应用？","href":"/interview/methodology-mvp-001","subtitle":"先用最简方案（规则/小模型/现成 API）验证价值与可行，快速拿反馈再迭代，避免一上来追大模型、过度工程。","tags":["MVP","快速验证","AI 工程实践"],"extraHaystack":"初级 开放"},{"id":"interview:methodology-pair-programming-001","type":"interview","title":"结对编程（Pair Programming）的价值与实践是什么？","href":"/interview/methodology-pair-programming-001","subtitle":"驾驶员+领航员实时协作，边写边审、共享知识、减少缺陷，适合复杂关键代码，成本是两人同时投入。","tags":["结对编程","代码评审","工程协作"],"extraHaystack":"初级 概念"},{"id":"interview:methodology-refactoring-001","type":"interview","title":"何时以及如何安全地重构代码？","href":"/interview/methodology-refactoring-001","subtitle":"重构是不改外部行为地改进内部结构，前提是测试保护，方法是小步频繁。","tags":["重构","代码质量","测试"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-sdd-001","type":"interview","title":"规范驱动开发（Spec-Driven Development）是什么？在 AI 时代有何意义？","href":"/interview/methodology-sdd-001","subtitle":"先写清晰规范/契约再实现，规范成为驱动 AI 生成与验证的单一事实源。","tags":["SDD","规范驱动","AI辅助编程"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-solid-001","type":"interview","title":"SOLID 设计原则分别是什么？","href":"/interview/methodology-solid-001","subtitle":"SOLID 五原则的含义及其共同目标：高内聚低耦合、易扩展易维护。","tags":["SOLID","面向对象","设计原则"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-tdd-001","type":"interview","title":"测试驱动开发（TDD）是什么？红-绿-重构循环如何运作？","href":"/interview/methodology-tdd-001","subtitle":"先写失败测试驱动实现，按红-绿-重构小步快跑迭代。","tags":["TDD","单元测试","重构"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-tech-debt-001","type":"interview","title":"技术债务如何识别、量化与管理？","href":"/interview/methodology-tech-debt-001","subtitle":"技术债是为速度做的妥协，需识别、量化影响并按 ROI 在迭代中偿还。","tags":["技术债务","工程治理","代码质量"],"extraHaystack":"中级 开放"},{"id":"interview:methodology-test-pyramid-001","type":"interview","title":"测试金字塔（单元 / 集成 / E2E）如何分层？","href":"/interview/methodology-test-pyramid-001","subtitle":"底层大量快的单元测试，向上递减到极少端到端，避免倒金字塔/冰淇淋。","tags":["测试金字塔","单元测试","E2E"],"extraHaystack":"中级 概念"},{"id":"interview:methodology-unit-test-ml-001","type":"interview","title":"机器学习代码如何写单元测试？","href":"/interview/methodology-unit-test-ml-001","subtitle":"测确定性部分：用小固定数据 + 固定随机种子，断言形状/范围/不变量与梯度，而非精度数值；过拟合一小批做 sanity check。","tags":["单元测试","ML 工程","测试方法","可复现性"],"extraHaystack":"中级 场景"},{"id":"interview:ml-ab-test-stats-001","type":"interview","title":"A/B 测试如何判断结果是否统计显著？","href":"/interview/ml-ab-test-stats-001","subtitle":"先定指标与假设，做双样本检验，看 p 值与置信区间，并防功效不足、多重比较与提前看数。","tags":["A/B测试","假设检验","统计显著性"],"extraHaystack":"中级 场景"},{"id":"interview:ml-anomaly-detection-001","type":"interview","title":"异常检测有哪些常用方法？","href":"/interview/ml-anomaly-detection-001","subtitle":"从统计法、孤立森林、One-Class SVM 到自编码器重构误差与密度 LOF，按数据维度与标签情况选型。","tags":["异常检测","孤立森林","无监督"],"extraHaystack":"中级 概念"},{"id":"interview:ml-bias-variance-001","type":"interview","title":"什么是偏差-方差权衡（Bias-Variance Tradeoff）？","href":"/interview/ml-bias-variance-001","subtitle":"总误差 = 偏差² + 方差 + 噪声；模型太简单高偏差，太复杂高方差，需在两者间找平衡。","tags":["机器学习","过拟合","欠拟合","泛化"],"extraHaystack":"初级 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P(y|x)；判别式分类通常更准。","tags":["生成式模型","判别式模型","概率建模"],"extraHaystack":"中级 概念"},{"id":"interview:ml-gradient-001","type":"interview","title":"梯度下降的原理是什么？SGD 和 Adam 有什么区别？","href":"/interview/ml-gradient-001","subtitle":"沿损失梯度反方向更新参数；SGD 用 mini-batch 估计梯度，Adam 自适应学习率，收敛更快更稳。","tags":["梯度下降","优化","Adam","SGD"],"extraHaystack":"初级 概念"},{"id":"interview:ml-gradient-boosting-001","type":"interview","title":"梯度提升（GBDT / XGBoost）的原理是什么？","href":"/interview/ml-gradient-boosting-001","subtitle":"串行加法模型，每棵新树拟合当前残差/负梯度；XGBoost 加正则并用二阶泰勒展开。","tags":["GBDT","XGBoost","梯度提升"],"extraHaystack":"中级 概念"},{"id":"interview:ml-hyperparameter-tuning-001","type":"interview","title":"网格搜索、随机搜索与贝叶斯优化如何选择？","href":"/interview/ml-hyperparameter-tuning-001","subtitle":"网格穷举适合少量参数，随机搜索高维更高效，贝叶斯用代理模型指导采样省评估次数。","tags":["超参数","网格搜索","贝叶斯优化"],"extraHaystack":"中级 场景"},{"id":"interview:ml-kmeans-001","type":"interview","title":"K-means 聚类如何工作？有哪些局限？","href":"/interview/ml-kmeans-001","subtitle":"迭代「分配到最近质心 + 更新均值」，需预设 k，对初始化与量纲敏感，只擅长球形簇。","tags":["K-means","聚类","无监督学习"],"extraHaystack":"初级 概念"},{"id":"interview:ml-knn-001","type":"interview","title":"KNN 算法的原理与优缺点是什么？","href":"/interview/ml-knn-001","subtitle":"惰性学习，预测时找最近 K 个邻居投票/平均；无训练成本但推理慢、受维度灾难影响。","tags":["KNN","惰性学习","距离度量"],"extraHaystack":"初级 概念"},{"id":"interview:ml-label-noise-001","type":"interview","title":"标签噪声如何影响模型？有哪些缓解方法？","href":"/interview/ml-label-noise-001","subtitle":"错误标签抬高误差上界、易被模型记忆；用鲁棒损失、置信学习、清洗与早停缓解。","tags":["标签噪声","鲁棒学习","数据质量"],"extraHaystack":"中级 场景"},{"id":"interview:ml-learning-curve-001","type":"interview","title":"如何用学习曲线诊断欠拟合与过拟合？","href":"/interview/ml-learning-curve-001","subtitle":"看训练/验证误差随样本量变化：两者都高且接近为欠拟合，gap 大为过拟合。","tags":["学习曲线","过拟合","欠拟合"],"extraHaystack":"初级 场景"},{"id":"interview:ml-let-a-and-b-be-events-on-the-same-sa-132","type":"interview","title":"概率题：P(A)=0.6、P(B)=0.7 时，A 与 B 能否互斥？","href":"/interview/ml-let-a-and-b-be-events-on-the-same-sa-132","subtitle":"互斥要求 P(A∩B)=0；由加法公式 P(A∪B)=1.3−P(A∩B)≤1 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个主成分投影实现降维。","tags":["PCA","降维","协方差矩阵","SVD"],"extraHaystack":"中级 概念"},{"id":"interview:ml-precision-recall-001","type":"interview","title":"精确率、召回率与 F1 分别衡量什么？如何取舍？","href":"/interview/ml-precision-recall-001","subtitle":"精确率看预测为正里有多少真对，召回率看真正例里抓回多少，F1 是两者调和平均，靠阈值取舍。","tags":["精确率","召回率","F1","混淆矩阵"],"extraHaystack":"初级 概念"},{"id":"interview:ml-recsys-metrics-001","type":"interview","title":"推荐 / 排序系统常用哪些评估指标（NDCG、Recall@K）？","href":"/interview/ml-recsys-metrics-001","subtitle":"NDCG 带位置折扣衡量排序质量，Recall@K/Precision@K、MAP、命中率看召回与精度，覆盖率/多样性补充体验。","tags":["推荐系统","排序","评估指标"],"extraHaystack":"中级 概念"},{"id":"interview:ml-regularization-001","type":"interview","title":"L1 与 L2 正则化有什么区别？为什么 L1 能产生稀疏？","href":"/interview/ml-regularization-001","subtitle":"L1 加权重绝对值之和、约束区为菱形顶点在轴上故产生稀疏；L2 加平方和、平滑收缩权重不归零。","tags":["L1正则化","L2正则化","稀疏","过拟合"],"extraHaystack":"中级 概念"},{"id":"interview:ml-roc-auc-001","type":"interview","title":"ROC 曲线与 AUC 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异源做编解码交互，因果注意力掩盖未来位置。","tags":["注意力机制","Transformer","自注意力"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-bert-finetune-001","type":"interview","title":"如何有效优化和微调 BERT 以应对特定 NLP 任务？","href":"/interview/nlp-bert-finetune-001","subtitle":"微调 BERT 要按任务接对应任务头，用小学习率（2e-5~5e-5）、少 epoch、warmup 加线性衰减，并通过 dropout、早停、分层学习率、数…","tags":["BERT","微调","迁移学习","PEFT","过拟合"],"extraHaystack":"中级 场景"},{"id":"interview:nlp-bert-gpt-001","type":"interview","title":"BERT 和 GPT 的架构与适用场景有何不同？","href":"/interview/nlp-bert-gpt-001","subtitle":"BERT 是 Encoder-only 双向理解模型，适合分类/NER；GPT 是 Decoder-only 自回归生成模型，适合文本生成与对话。","tags":["BERT","GPT","Transformer","NLP"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-bert-input-length-001","type":"interview","title":"BERT 输入长度的 512 限制如何解决？","href":"/interview/nlp-bert-input-length-001","subtitle":"BERT 因位置编码与 O(L²) 注意力被限制在 512 token；长文本可用截断、滑动窗口分段聚合、层次编码、长上下文模型（Longformer/Big…","tags":["BERT","长文本","512限制","Longformer","稀疏注意力"],"extraHaystack":"中级 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如何评估生成文本质量？","href":"/interview/nlp-bleu-rouge-001","subtitle":"BLEU 看 n-gram 精确率加简短惩罚偏翻译，ROUGE 看 n-gram/LCS 召回偏摘要。","tags":["BLEU","ROUGE","文本生成评估","机器翻译"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-cbow-vs-skipgram-001","type":"interview","title":"CBOW 和 Skip-gram 分别更适合哪些应用场景？","href":"/interview/nlp-cbow-vs-skipgram-001","subtitle":"CBOW 用上下文预测中心词，训练快、对高频词效果好；Skip-gram 用中心词预测每个上下文词，训练慢但对低频/罕见词表示更细更好，且按 Mikolov …","tags":["Word2Vec","CBOW","Skip-gram","词向量"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-define-sentiment-analysis-and-discus-028","type":"interview","title":"什么是情感分析？有哪些应用？","href":"/interview/nlp-define-sentiment-analysis-and-discus-028","subtitle":"情感分析（Sentiment Analysis）自动判断文本主观态度（正/负/中性或更细粒度），广泛用于商品评论挖掘、品牌舆情监控、金融市场情绪指标与客服质检。","tags":["nlp","Define"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-describe-lemmatization-and-stemming--095","type":"interview","title":"词形还原与词干提取有何区别？何时选用？","href":"/interview/nlp-describe-lemmatization-and-stemming--095","subtitle":"Stemming 用规则快速砍掉词尾（Porter），不求合法词形；Lemmatization 结合词典与 [词性](/glossary/part-of-sp…","tags":["nlp","Describe","When"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-describe-what-a-bag-of-words-model-i-092","type":"interview","title":"什么是词袋模型？有哪些局限？","href":"/interview/nlp-describe-what-a-bag-of-words-model-i-092","subtitle":"词袋模型（BoW）把文档表示为词频向量，完全忽略词序与语法。简单高效，适合基线分类，但无法区分「狗咬人」与「人咬狗」，且高维稀疏、难处理多义词。","tags":["nlp","Describe"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-elmo-001","type":"interview","title":"ELMo 有哪些优缺点？它能否做到一词多义？为什么？","href":"/interview/nlp-elmo-001","subtitle":"ELMo 用双向 LSTM 语言模型按整句上下文动态生成词向量，同一个词在不同句子里向量不同，因而能表达一词多义；但其双向是两个单向 LSTM 拼接、难并行，…","tags":["ELMo","词向量","上下文表示","双向LSTM","一词多义"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-embedding-001","type":"interview","title":"Word2Vec 和 Transformer Embedding 有何区别？","href":"/interview/nlp-embedding-001","subtitle":"Word2Vec 是静态词向量，一词一向量；Transformer 是上下文相关动态表示，同一词在不同句子向量不同。","tags":["Word2Vec","Embedding","NLP"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-embedding-algorithms-001","type":"interview","title":"文本 Embedding 有哪几种算法？业界有哪些可选的 Embedding 模型？","href":"/interview/nlp-embedding-algorithms-001","subtitle":"Embedding 算法从静态词向量（Word2Vec/GloVe/FastText）演进到上下文表示（ELMo/BERT），再到专门优化检索的句/文档级嵌入…","tags":["Embedding","词向量","BERT","向量检索","MTEB"],"extraHaystack":"初级 概念"},{"id":"interview:nlp-embedding-selection-001","type":"interview","title":"如何选择与评估一个 Embedding 模型？需要考虑哪些因素？","href":"/interview/nlp-embedding-selection-001","subtitle":"从任务/领域匹配、语言、向量维度、最大长度、是否支持非对称与指令、成本延迟、隐私等维度选型，并用自己的标注数据测 Recall@k/MRR/NDCG 做端到端…","tags":["Embedding","向量检索","RAG","MTEB"],"extraHaystack":"中级 场景"},{"id":"interview:nlp-explain-how-decision-trees-are-utili-005","type":"interview","title":"决策树如何用于自然语言处理任务？","href":"/interview/nlp-explain-how-decision-trees-are-utili-005","subtitle":"NLP 中常先把文本转为 [TF-IDF](/glossary/tf-idf) 或 n-gram 稀疏向量，再用决策树做情感分类、主题分类、意图识别等。树模型…","tags":["nlp","Explain","Decision","Trees","NLP"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-explain-how-the-naive-bayes-classifi-049","type":"interview","title":"朴素贝叶斯分类器如何用于自然语言处理？","href":"/interview/nlp-explain-how-the-naive-bayes-classifi-049","subtitle":"朴素贝叶斯用贝叶斯定理 + 特征条件独立假设做文本分类：用训练集估计 $P(c|w_1...w_n) \\propto P(c)\\prod P(w_i|c)$。…","tags":["nlp","Explain","Naive","Bayes","NLP"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-explain-the-significance-of-part-of--059","type":"interview","title":"词性标注（POS）在 NLP 中有何意义？","href":"/interview/nlp-explain-the-significance-of-part-of--059","subtitle":"[词性标注（POS）](/glossary/part-of-speech-tagging) 为每个词分配语法类别（名词、动词等），是句法分析、信息抽取和机器翻…","tags":["nlp","Explain","Part-of-Speech","POS","NLP"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-fasttext-001","type":"interview","title":"FastText 相比 Word2Vec 有何改进？哪些情况更适合用 FastText？","href":"/interview/nlp-fasttext-001","subtitle":"FastText 在 Word2Vec 基础上把词拆成字符 n-gram，词向量等于其子词向量之和，从而能为未登录词生成向量，对形态丰富语言和拼写变体更鲁棒，…","tags":["FastText","Word2Vec","词向量","subword","OOV"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-glove-001","type":"interview","title":"GloVe 怎么训练？有哪些应用场景？相比 Word2Vec 的优缺点？","href":"/interview/nlp-glove-001","subtitle":"GloVe 先统计全局词共现矩阵，再用加权最小二乘拟合「词向量点积约等于共现概率的对数」，兼顾全局统计与局部上下文，训练可并行、词类比稳定，但需存共现矩阵且仍…","tags":["GloVe","词向量","共现矩阵","Word2Vec"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-gpt-evolution-001","type":"interview","title":"GPT 系列从 GPT-1 到 GPT-4 关键演进了什么？","href":"/interview/nlp-gpt-evolution-001","subtitle":"GPT 沿自回归 Decoder 路线，从预训练+微调，到 zero-shot、in-context、175B 规模，再到 RLHF 对齐与多模态。","tags":["GPT","预训练","In-Context Learning","RLHF"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-hierarchical-softmax-001","type":"interview","title":"解释 Hierarchical Softmax（层次 Softmax）的流程及其优点","href":"/interview/nlp-hierarchical-softmax-001","subtitle":"层次 Softmax 用 Huffman 树组织词表（高频词路径短），预测一个词变成沿根到叶路径的若干次二分类，概率为路径上各节点 sigmoid 之积，把 …","tags":["Hierarchical Softmax","Word2Vec","Huffman树","词向量"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-how-are-hidden-markov-models-hmms-ap-043","type":"interview","title":"隐马尔可夫模型（HMM）如何应用于 NLP 任务？","href":"/interview/nlp-how-are-hidden-markov-models-hmms-ap-043","subtitle":"HMM 假设隐藏状态序列（如词性）按马尔可夫链演化，观测（词）由状态发射。经典用于 [词性标注](/glossary/part-of-speech-taggi…","tags":["nlp","How","Hidden","Markov","Models"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-how-does-a-dependency-parser-work-an-082","type":"interview","title":"依存句法分析器如何工作？提供哪些信息？","href":"/interview/nlp-how-does-a-dependency-parser-work-an-082","subtitle":"依存解析为句子中每个词找到语法头词及依存关系（如 nsubj、dobj），形成一棵依存树。它揭示「谁对谁做了什么」，支撑关系抽取、语义角色标注和机器翻译的重排…","tags":["nlp","How"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-negative-sampling-001","type":"interview","title":"负采样（Negative Sampling）在 Word2Vec 中如何运用？","href":"/interview/nlp-negative-sampling-001","subtitle":"负采样把「在全词表上预测」的昂贵多分类，转成「区分真实上下文词与少量噪声词」的逻辑回归二分类，按词频 3/4 次方采负样本，只更新少量参数，大幅加速训练。","tags":["Word2Vec","负采样","词向量","Skip-gram"],"extraHaystack":"高级 概念"},{"id":"interview:nlp-seq2seq-attention-001","type":"interview","title":"Seq2Seq 中的注意力机制解决了什么问题？","href":"/interview/nlp-seq2seq-attention-001","subtitle":"注意力让解码器动态加权关注全部编码状态，破解固定向量瓶颈，是 Transformer 之基。","tags":["Seq2Seq","注意力机制","编码器-解码器","机器翻译"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-subword-tokenization-001","type":"interview","title":"子词分词：BPE、WordPiece、Unigram 有什么区别？","href":"/interview/nlp-subword-tokenization-001","subtitle":"BPE 按频率自底向上合并、WordPiece 按似然增益合并（BERT）、Unigram 概率化自顶向下裁剪（SentencePiece）。","tags":["子词分词","BPE","WordPiece","Unigram"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-t5-001","type":"interview","title":"T5 的「Text-to-Text」统一范式是什么？","href":"/interview/nlp-t5-001","subtitle":"T5 把所有 NLP 任务统一成「文本进、文本出」，用 Encoder-Decoder 架构和 span corruption 预训练。","tags":["T5","Text-to-Text","Encoder-Decoder","预训练"],"extraHaystack":"中级 概念"},{"id":"interview:nlp-text-classification-imbalance-001","type":"interview","title":"文本分类任务中如何处理样本（类别）不平衡？","href":"/interview/nlp-text-classification-imbalance-001","subtitle":"类别不平衡会让模型偏向多数类、少数类召回崩塌；可从数据层重采样与增强、算法层类权重/Focal Loss、阈值调整、集成与合适指标（F1/宏平均/PR-AUC…","tags":["文本分类","类别不平衡","Focal Loss","数据增强"],"extraHaystack":"中级 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