{"generatedAt":"2026.10.23 02:16","concepts":[{"id":"concept-a2a","illo":"converge","cat":"协议标准","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"A2A（智能体间协议）","brief":"让不同厂商、不同框架构建的 AI 智能体相互发现、沟通与协作的开放协议。","sum":"让不同厂商、不同框架构建的 AI 智能体相互发现、沟通与协作的开放协议。","gl1":"为什么重要","gl2":"影响谁","why":"MCP解决智能体连接工具，A2A进一步解决智能体与智能体之间如何合作。","who":"构建多智能体系统、企业自动化平台与跨厂商协作流程的团队。","srcs":["Google Developers","A2A 官方规范","A2A GitHub","IT之家","量子位","爱范儿"],"links":[{"name":"Google Developers","url":"https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/"},{"name":"A2A 官方规范","url":"https://a2a-protocol.org/latest/"},{"name":"A2A 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是让它可持续、可检查地工作的外部架构。","relatedConcepts":[{"id":"concept-ai-agent","name":"AI Agent（人工智能体）"},{"id":"concept-context-engineering","name":"Context Engineering（上下文工程）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"id":"concept-agent-skills","illo":"converge","cat":"能力模块","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Agent Skills（智能体技能）","brief":"把专业知识、操作步骤、脚本和素材封装成可重复调用的智能体能力。","sum":"把专业知识、操作步骤、脚本和素材封装成可重复调用的智能体能力。","gl1":"为什么重要","gl2":"影响谁","why":"同一套能力可以跨多个兼容的 AI 工具复用，不必反复编写长提示词。","who":"需要沉淀固定工作流、团队规范和专业能力的个人与组织。","srcs":["Agent Skills","Agent Skills 规范","Microsoft","rakshazi.me","Reddit / r/comfyui","IT之家"],"links":[{"name":"Agent Skills","url":"https://agentskills.io/home"},{"name":"Agent Skills 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天持续出现。","srcs":["reddit.com","原文"],"links":[{"name":"reddit.com","url":"https://www.reddit.com/r/ClaudeAI/comments/1wkvqxc/what_is_the_best_way_to_use_llmss_in_2026_i_feel/"},{"name":"reddit.com","url":"https://www.reddit.com/r/OpenAI/comments/1wkvofz/what_is_the_best_way_to_use_llmss_in_2026_i_feel/"},{"name":"原文","url":"https://garymarcus.substack.com/p/wake-up-people-what-we-should-actually"},{"name":"机器之心","url":"https://jigou.jiqizhixin.com/articles/2026-09-19-6"},{"name":"reddit.com","url":"https://www.reddit.com/r/ClaudeAI/comments/1wm5c21/i_am_done_with_this_shit/"},{"name":"HN 热议","url":"https://agentexecutor.io"}],"aliases":["Agentic模式","智能体AI"],"plainExplanation":"就像从手动挡汽车换成自动驾驶，你只需告诉AI目的地，它自己规划路线、处理路况。","mechanism":["多智能体协作：多个AI智能体分工合作，各自负责子任务。","工具调用：智能体自主调用外部API、代码执行器等工具。","任务规划：智能体将复杂目标分解为可执行的步骤序列。"],"useCases":["自动化编码：AI智能体自主编写、测试和修复代码。","工作流自动化：AI智能体处理邮件、日历、数据整理等日常任务。"],"notThis":"不是简单的聊天机器人，也不是单个AI功能，而是一种用户与AI交互的范式转变。","misconceptions":"误以为Agentic AI是某个具体产品，实际上它是多种技术和使用方式的集合。","comparison":"与传统的“提示-响应”模式相比，Agentic AI强调AI的自主性和主动性，而非被动执行指令。","relatedConcepts":[{"id":"concept-ai-agent","name":"AI Agent"},{"id":"concept-multi-agent","name":"多智能体系统"}]},{"id":"concept-ai-agent","illo":"converge","cat":"新概念","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"AI Agent（人工智能体）","brief":"能够围绕目标自主规划步骤、调用工具、观察结果并继续行动的 AI 系统。","sum":"能够围绕目标自主规划步骤、调用工具、观察结果并继续行动的 AI 系统。","gl1":"为什么重要","gl2":"影响谁","why":"AI 正从只回答问题，转向能够执行多步骤任务并对结果负责。","who":"办公自动化、编程、客服、研究与跨系统业务流程。","srcs":["Anthropic","OpenAI"],"links":[{"name":"Anthropic","url":"https://www.anthropic.com/research/building-effective-agents"},{"name":"OpenAI","url":"https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf"}],"aliases":["AI Agent","Agentic AI","智能体","人工智能体"],"points":[],"catalogManaged":true,"cats":["智能体与工程"],"plainExplanation":"普通聊天模型回答一次就停下；AI Agent 会围绕目标反复决定下一步、调用工具并检查结果。","mechanism":["接收目标并判断当前状态。","选择工具或动作，执行后读取新结果。","根据结果继续、改道、请求确认或结束任务。"],"useCases":["独立完成代码修改、测试与交付。","在邮件、日历和业务系统间处理多步骤任务。"],"notThis":"AI Agent 不是任何使用大模型的应用；它必须能根据结果继续采取行动。","misconceptions":"智能体的“自主”不等于不受约束；权限、确认点和失败处理决定了它能否安全使用。","comparison":"工作流程通常预先写死步骤；AI Agent 会根据中间结果动态决定下一步。","relatedConcepts":[{"id":"concept-tool-use","name":"Tool Use（工具调用）"},{"id":"concept-agent-harness","name":"Agent Harness（智能体驾驭工程）"},{"id":"concept-personal-ai-agent","name":"Personal AI Agent（个人智能体）"}]},{"id":"concept-ai-alignment","illo":"converge","cat":"工程范式","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"AI Alignment（人工智能对齐）","brief":"让 AI 系统的目标、行为和结果与人类意图、价值和安全要求保持一致。","sum":"让 AI 系统的目标、行为和结果与人类意图、价值和安全要求保持一致。","gl1":"为什么重要","gl2":"影响谁","why":"模型能力越强，错误目标、误解指令和不可控行为带来的风险越大。","who":"基础模型、安全研究、产品治理和高风险 AI 应用。","srcs":["OpenAI Alignment","NIST AI 风险管理框架"],"links":[{"name":"OpenAI Alignment","url":"https://openai.com/index/our-approach-to-alignment-research/"},{"name":"NIST AI 风险管理框架","url":"https://www.nist.gov/itl/ai-risk-management-framework"}],"aliases":["AI Alignment","Alignment","人工智能对齐","模型对齐"],"points":[],"catalogManaged":true,"cats":["安全治理"],"plainExplanation":"AI 对齐研究怎样让系统理解并持续遵循人类意图、规则与安全目标，尤其在能力增强和环境变化时仍不过度偏离。","mechanism":["通过监督数据和偏好数据塑造目标行为。","使用评测、红队和可解释性方法发现失配。","结合训练、系统约束、监控和治理持续降低风险。"],"useCases":["调整通用助手的帮助性、诚实性和安全边界。","为高影响智能体设计权限、监督和失败处置机制。"],"notThis":"AI 对齐不是一个已经被单一算法解决的问题，也不等于只做内容过滤。","misconceptions":"让模型迎合用户偏好不等于完成对齐；短期服从可能与事实、长期目标或他人利益冲突。","comparison":"对齐是让系统目标与人类要求一致的总体问题；Guardrails 是部署阶段用于约束具体输入、输出和行动的一层措施。","relatedConcepts":[{"id":"concept-rlhf","name":"RLHF（基于人类反馈的强化学习）"},{"id":"concept-dpo","name":"DPO（直接偏好优化）"},{"id":"concept-guardrails","name":"AI Guardrails（AI 护栏）"}]},{"illo":"loop","cat":"行业现象","type":"AI 概念","ver":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.08.11","trend":"进入长期概念档案","title":"AI Compute Arms Race（AI 算力军备）","brief":"模型公司、云厂商和资本方围绕芯片、数据中心、电力与长期算力供应展开的大规模竞争性投入。","sum":"模型公司、云厂商和资本方围绕芯片、数据中心、电力与长期算力供应展开的大规模竞争性投入。","gl1":"为什么重要","gl2":"影响谁","why":"前沿模型的训练和服务越来越依赖长期、稳定且昂贵的基础设施供给。","who":"模型公司、云与芯片厂商、数据中心、电力供应商、投资者和政策制定者。","excerpts":[{"src":"SpaceX 财报会","ex":"未来所有 AI 算力将独家采用 Vera Rubin 架构，2026 年底总算力超 2GW，2027 年底接近 10GW。"},{"src":"行业分析","ex":"杰文斯悖论还能持续吗：效率提升让用量涨得更快，定价分层正在支撑算力需求继续增长。"}],"srcs":["Stanford AI Index 2026","Epoch AI Compute Trends"],"note":"金额为协议口径，非实际支出","links":[{"name":"Stanford AI Index 2026","url":"https://hai.stanford.edu/assets/files/ai_index_report_2026.pdf"},{"name":"Epoch AI Compute Trends","url":"https://epoch.ai/data-insights/notable-ai-models"}],"img":"media/concepts/06.jpg","imgKind":"pic","status":"已沉淀","id":"concept-ai-compute-arms-race","lastObservedAt":"2026.08.23","aliases":["AI Compute Arms Race","AI 算力军备","算力资本军备","AI Infrastructure 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护栏）","brief":"围绕模型输入、输出和工具行为设置的检测、限制与纠错机制。","sum":"围绕模型输入、输出和工具行为设置的检测、限制与纠错机制。","gl1":"为什么重要","gl2":"影响谁","why":"它为内容安全、隐私保护、权限控制和业务规则提供额外防线。","who":"企业 AI、智能体、客服、内容平台和高风险业务系统。","srcs":["NVIDIA NeMo Guardrails","AWS Bedrock Guardrails"],"links":[{"name":"NVIDIA NeMo Guardrails","url":"https://docs.nvidia.com/nemo/guardrails/index.html"},{"name":"AWS Bedrock Guardrails","url":"https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html"}],"aliases":["AI Guardrails","Guardrails","AI 护栏","模型护栏"],"points":[],"catalogManaged":true,"cats":["安全治理"],"plainExplanation":"AI 护栏是在模型周围增加输入检查、输出检查、权限限制、操作确认和监控，避免不合规内容或危险行动直接进入真实业务。","mechanism":["在输入阶段检测敏感内容、攻击和越权请求。","在模型输出与工具调用阶段执行规则、分类和业务验证。","对高风险操作拦截、降级、请求人工确认并记录审计信息。"],"useCases":["限制客服泄露隐私、生成违规内容或越权查询。","在智能体执行付款、发信、删除或发布前进行权限与人工确认。"],"notThis":"Guardrails 不是替代模型训练、应用安全和人工治理的万能安全层。","misconceptions":"一套静态关键词规则不能覆盖语义变化、间接注入和多步工具风险，需要多层控制与持续测试。","comparison":"模型对齐塑造基础行为倾向；护栏在具体产品运行时检查和约束输入、输出与行动。","relatedConcepts":[{"id":"concept-prompt-injection","name":"Prompt Injection（提示词注入）"},{"id":"concept-ai-alignment","name":"AI Alignment（人工智能对齐）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"illo":"converge","cat":"协议标准","type":"AI 概念","ver":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.08.11","trend":"进入长期概念档案","title":"AI 内容标识与透明度","brief":"通过披露、来源凭证、水印或机器可读标记说明内容是否由 AI 生成或编辑及其流转过程。","sum":"通过披露、来源凭证、水印或机器可读标记说明内容是否由 AI 生成或编辑及其流转过程。","gl1":"为什么重要","gl2":"影响谁","why":"合成内容越来越难凭肉眼判断，平台、创作者和接收者需要可核验的来源信息。","who":"内容平台、生成式 AI 产品、媒体、广告、创作者与合规团队。","excerpts":[{"src":"欧盟规则","ex":"公司必须披露用户何时与 AI 模型互动，并为合成音视频和文本添加机器可读标记，违规最高罚 1500 万欧元。"},{"src":"媒体报道","ex":"超过 50 条违规图片和视频广告发布在 Facebook、Instagram、Messenger 或 Threads 上，其中一些本周仍在投放。"}],"srcs":["NIST Synthetic Content Transparency","C2PA 规范"],"note":"具体义务以法规原文为准","links":[{"name":"NIST 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生成；凭证也可能在截图、转码或不兼容平台中丢失。","comparison":"水印在内容信号中嵌入标记；来源凭证记录可验证的创作与编辑链，两者可以组合但不等价。","relatedConcepts":[{"id":"concept-generative-ai","name":"Generative AI（生成式人工智能）"},{"id":"concept-guardrails","name":"AI Guardrails（AI 护栏）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","lastObservedAt":"2026.09.20","time":"已被行业采用","ts":"观测截至 2026.09.20","trend":"进入长期概念档案","title":"AI安全评估","brief":"对AI模型（尤其是大模型）进行系统性安全测试和评估的实践，包括漏洞检测、对抗性测试和风险评估。","sum":"对AI模型（尤其是大模型）进行系统性安全测试和评估的实践，包括漏洞检测、对抗性测试和风险评估。","gl1":"这是什么","gl2":"为什么在升温","why":"随着AI能力增强，模型可能被滥用或产生意外行为，需要标准化评估来确保安全性和可靠性。","who":"近 14 天跨 3 类来源、2 天持续出现。","srcs":["baseten.co","Reddit / r/LocalLLaMA","原文"],"links":[{"name":"量子位","url":"https://www.qbitai.com/2026/09/492849.html"},{"name":"baseten.co","url":"https://www.baseten.co"},{"name":"Reddit / 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Mechanism（注意力机制）","brief":"让模型在处理输入时动态判断不同位置或信息之间重要程度的机制。","sum":"让模型在处理输入时动态判断不同位置或信息之间重要程度的机制。","gl1":"为什么重要","gl2":"影响谁","why":"它帮助模型捕捉长距离关系，是 Transformer 理解上下文的关键。","who":"语言模型、视觉模型、多模态模型和长上下文研究。","srcs":["Attention Is All You Need","Google 机器学习术语库"],"links":[{"name":"Attention Is All You Need","url":"https://arxiv.org/abs/1706.03762"},{"name":"Google 机器学习术语库","url":"https://developers.google.com/machine-learning/glossary"}],"aliases":["Attention","Attention Mechanism","Self-Attention","注意力机制","自注意力"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"注意力让模型在处理当前信息时，动态给其他位置分配不同权重，从而更关注相关内容。","mechanism":["将输入映射为查询、键和值。","计算查询与各个键的相似度并归一化为权重。","按权重汇总对应的值，形成当前位置的上下文表示。"],"useCases":["理解句子中远距离词语的指代与依赖。","在视觉模型中联系图像区域，或在多模态模型中对齐文字与图像。"],"notThis":"注意力权重不是可直接当作人类思考过程或因果证据的解释。","misconceptions":"高注意力分数表示当前计算中影响较大，但不足以证明该输入对最终结果具有唯一因果作用。","comparison":"注意力是 Transformer 的核心计算机制；Transformer 还包括前馈网络、残差连接与归一化等完整结构。","relatedConcepts":[{"id":"concept-transformer","name":"Transformer（变换器架构）"},{"id":"concept-context-window","name":"Context Window（上下文窗口）"},{"id":"concept-embedding","name":"Embedding（向量表示）"}]},{"id":"concept-chain-of-thought","illo":"converge","cat":"模型能力","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Chain-of-Thought（思维链）","brief":"让模型通过中间推理步骤处理复杂问题的一类提示或训练方法。","sum":"让模型通过中间推理步骤处理复杂问题的一类提示或训练方法。","gl1":"为什么重要","gl2":"影响谁","why":"它显著影响数学、逻辑和多步骤任务的表现，也是推理模型的重要基础。","who":"复杂推理、数学、代码、规划任务与模型评测。","srcs":["Chain-of-Thought 论文","Google Research"],"links":[{"name":"Chain-of-Thought 论文","url":"https://arxiv.org/abs/2201.11903"},{"name":"Google Research","url":"https://research.google/blog/language-models-perform-reasoning-via-chain-of-thought/"}],"aliases":["Chain-of-Thought","CoT","思维链","链式思考"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"思维链让复杂问题通过若干中间步骤展开，而不是直接从问题跳到最终答案。","mechanism":["把复杂任务拆成多个可衔接的中间判断。","按顺序生成或在内部计算这些步骤。","使用中间结果形成最终答案，并可结合验证器检查。"],"useCases":["解决包含多步计算的数学和逻辑问题。","帮助智能体分解计划、分析约束和检查行动结果。"],"notThis":"模型生成的思维链不是对其内部神经计算的完整、忠实记录。","misconceptions":"一段看起来合理的推理文字仍可能为错误答案事后编造理由，不能替代外部验证。","comparison":"思维链是一种中间推理组织方式；推理模型是在回答阶段系统投入额外计算的一类模型。","relatedConcepts":[{"id":"concept-reasoning-model","name":"Reasoning Model（推理模型）"},{"id":"concept-inference-scaling","name":"Inference-time Scaling（推理时扩展）"},{"id":"concept-prompt-engineering","name":"Prompt Engineering（提示词工程）"}]},{"id":"concept-computer-use","illo":"converge","cat":"能力模块","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Computer Use（计算机操作）","brief":"让模型通过识别屏幕并操作鼠标、键盘或浏览器来使用现有软件。","sum":"让模型通过识别屏幕并操作鼠标、键盘或浏览器来使用现有软件。","gl1":"为什么重要","gl2":"影响谁","why":"它让智能体能够处理没有专门接口的网页和桌面软件。","who":"浏览器自动化、桌面办公、测试和跨软件工作流。","srcs":["Anthropic","OpenAI"],"links":[{"name":"Anthropic","url":"https://www.anthropic.com/news/3-5-models-and-computer-use"},{"name":"OpenAI","url":"https://openai.com/index/computer-using-agent/"}],"aliases":["Computer Use","Computer-Using Agent","计算机操作","电脑操作智能体"],"points":[],"catalogManaged":true,"cats":["智能体与工程"],"plainExplanation":"它让 AI 像人一样看屏幕、点按钮和输入文字，因此能使用没有专用接口的网页和桌面软件。","mechanism":["捕获屏幕或页面状态。","模型识别可操作元素并选择鼠标、键盘或浏览器动作。","执行动作后再次观察，直到完成或需要人工确认。"],"useCases":["在旧的企业后台中录入和查询数据。","执行端到端界面测试或跨多个桌面软件处理任务。"],"notThis":"Computer Use 不是普通网页抓取，也不是通过正式 API 直接调用功能。","misconceptions":"能看懂界面不等于能稳定操作；布局变化、弹窗和高风险操作都需要额外防护。","comparison":"工具调用通过稳定接口执行，通常更可靠；Computer Use 用界面操作覆盖没有接口的软件。","relatedConcepts":[{"id":"concept-tool-use","name":"Tool Use（工具调用）"},{"id":"concept-ai-agent","name":"AI Agent（人工智能体）"},{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"}]},{"id":"concept-context-engineering","illo":"converge","cat":"工程范式","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Context Engineering（上下文工程）","brief":"系统设计智能体在每一步能看到哪些信息、工具、记忆和规则的工程方法。","sum":"系统设计智能体在每一步能看到哪些信息、工具、记忆和规则的工程方法。","gl1":"为什么重要","gl2":"影响谁","why":"它比单次提示词更关注完整运行过程，是提升长任务可靠性的关键。","who":"设计复杂智能体、长任务工作流和企业 AI 系统的团队。","srcs":["Anthropic","LangChain","reddit.com","HN 热议","原文"],"links":[{"name":"Anthropic","url":"https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents"},{"name":"LangChain","url":"https://blog.langchain.com/the-rise-of-context-engineering/"},{"name":"reddit.com","url":"https://www.reddit.com/r/AI_Agents/comments/1wz5soi/what_are_you_using_for_a_shared_agentnative/"},{"name":"HN 热议","url":"https://arxiv.org/abs/2609.37725"},{"name":"HN 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不是内容真实性或版权安全的保证机制。","misconceptions":"模型并不是从训练库中简单查找并复制一条现成答案；但它也可能重现训练数据的特定片段。","comparison":"判别式 AI 主要在已有选项中预测类别或分数；生成式 AI 构造新的内容序列。","relatedConcepts":[{"id":"concept-foundation-model","name":"Foundation Model（基础模型）"},{"id":"concept-large-language-model","name":"LLM（大语言模型）"},{"id":"concept-diffusion-model","name":"Diffusion Model（扩散模型）"}]},{"id":"concept-grounding","illo":"converge","cat":"工程范式","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Grounding（事实依据与落地）","brief":"让模型回答建立在可验证数据、检索结果或真实环境信息之上的方法。","sum":"让模型回答建立在可验证数据、检索结果或真实环境信息之上的方法。","gl1":"为什么重要","gl2":"影响谁","why":"它能减少无依据回答，并让生成内容更贴近最新或私有事实。","who":"企业知识库、搜索、客服、研究助手和高可信问答产品。","srcs":["Google Cloud Grounding","Google 机器学习术语库"],"links":[{"name":"Google Cloud Grounding","url":"https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview"},{"name":"Google 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是通过可靠资料或环境状态约束输出的应对方法。","relatedConcepts":[{"id":"concept-grounding","name":"Grounding（事实依据与落地）"},{"id":"concept-rag","name":"RAG（检索增强生成）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"illo":"team","cat":"行业现象","type":"AI 概念","ver":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.08.11","trend":"进入长期概念档案","title":"Indirect Prompt Injection（间接提示词注入）","brief":"恶意指令隐藏在网页、文件、邮件或工具结果中，在 AI 读取外部内容时改变其行为的攻击。","sum":"恶意指令隐藏在网页、文件、邮件或工具结果中，在 AI 读取外部内容时改变其行为的攻击。","gl1":"为什么重要","gl2":"影响谁","why":"智能体连接的外部内容越多，攻击者可以投放隐藏指令的入口就越多。","who":"浏览器助手、RAG、邮件与文档智能体、MCP 工具和企业自动化系统。","excerpts":[{"src":"漏洞披露","ex":"攻击通过间接提示注入利用其 URL 检索工具实现，无需人工审批即可执行，即使组织禁用网页搜索功能也无法避免。"},{"src":"工程实践","ex":"用 SkillSpector、YARA 规则与 CI 策略门，把技能安全审计变成每次提交自动跑的流水线。"}],"srcs":["OWASP Prompt Injection","OWASP Prevention Cheat Sheet"],"note":"漏洞细节以官方修复公告为准","links":[{"name":"OWASP Prompt Injection","url":"https://genai.owasp.org/llmrisk/llm01-prompt-injection/"},{"name":"OWASP Prevention Cheat 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Jailbreak","模型越狱","越狱提示"],"points":[],"catalogManaged":true,"cats":["安全治理"],"plainExplanation":"模型越狱通过角色扮演、编码、分步诱导或对抗提示等方式，试图绕过模型原本设置的安全限制。","mechanism":["构造能触发规则冲突或掩盖真实意图的输入。","利用模型对指令、上下文和安全边界判断的不稳定性。","逐步诱导模型生成原本应拒绝的内容或行为。"],"useCases":["红队测试模型的内容安全与拒答边界。","评估模型升级后是否出现新的安全回退或已知攻击复现。"],"notThis":"模型越狱通常不是获取服务器管理员权限，也不等于传统设备越狱。","misconceptions":"在一个提示上成功或失败都不能代表整体安全性；攻击会随模型、语言和对话状态变化。","comparison":"越狱主要以绕过模型安全策略为目标；提示词注入范围更广，也可操纵正常业务指令和工具行为。","relatedConcepts":[{"id":"concept-prompt-injection","name":"Prompt Injection（提示词注入）"},{"id":"concept-guardrails","name":"AI Guardrails（AI 护栏）"},{"id":"concept-ai-alignment","name":"AI Alignment（人工智能对齐）"}]},{"id":"concept-knowledge-distillation","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Knowledge Distillation（知识蒸馏）","brief":"让较小模型学习较大模型输出或内部表示，从而继承部分能力的方法。","sum":"让较小模型学习较大模型输出或内部表示，从而继承部分能力的方法。","gl1":"为什么重要","gl2":"影响谁","why":"它可以在保留能力的同时降低模型体积、成本和运行延迟。","who":"小模型、端侧部署、低成本推理和模型压缩团队。","srcs":["Distilling the Knowledge","Google 机器学习术语库"],"links":[{"name":"Distilling the Knowledge","url":"https://arxiv.org/abs/1503.02531"},{"name":"Google 机器学习术语库","url":"https://developers.google.com/machine-learning/glossary"}],"aliases":["Knowledge Distillation","Distillation","知识蒸馏","模型蒸馏"],"points":[],"catalogManaged":true,"cats":["训练优化"],"plainExplanation":"知识蒸馏让较小的学生模型学习较大教师模型的输出分布、示例或中间表示，以较低运行成本保留部分能力。","mechanism":["选择教师模型并生成软标签、推理示例或中间特征。","让学生模型同时拟合真实标签和教师信号。","在体积、速度与任务质量之间调节训练目标。"],"useCases":["把大型语言模型的特定能力迁移到本地小模型。","压缩分类、视觉和语音模型以部署到受限设备。"],"notThis":"蒸馏不是无损复制教师模型，也不是把教师参数直接压缩成学生参数。","misconceptions":"学生模型在某个基准接近教师，不代表继承了教师的全部通用能力、边界和鲁棒性。","comparison":"量化降低每个参数的数值精度；蒸馏训练一个新的较小模型来模仿教师行为。","relatedConcepts":[{"id":"concept-small-language-model","name":"SLM（小语言模型）"},{"id":"concept-quantization","name":"Quantization（模型量化）"},{"id":"concept-transfer-learning","name":"Transfer Learning（迁移学习）"}]},{"id":"concept-large-language-model","illo":"converge","cat":"模型架构","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"LLM（大语言模型）","brief":"通过海量文本和其他数据训练、用于理解与生成语言的大规模模型。","sum":"通过海量文本和其他数据训练、用于理解与生成语言的大规模模型。","gl1":"为什么重要","gl2":"影响谁","why":"它是聊天助手、代码生成、知识问答和智能体的核心基础。","who":"几乎所有生成式 AI 产品、研发团队与使用者。","srcs":["Google LLM 介绍","Google 机器学习术语库"],"links":[{"name":"Google LLM 介绍","url":"https://developers.google.com/machine-learning/resources/intro-llms"},{"name":"Google 机器学习术语库","url":"https://developers.google.com/machine-learning/glossary"}],"aliases":["LLM","Large Language Model","大语言模型","大型语言模型"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"LLM 通过学习海量文本和代码中词元的关系，根据上下文持续预测后续内容，从而完成理解与生成任务。","mechanism":["将文本转换为词元序列。","通过 Transformer 在上下文中建模词元之间的关系。","自回归地预测后续词元，直到形成回答或结构化输出。"],"useCases":["问答、摘要、翻译、写作与代码生成。","作为智能体的语言理解、规划和工具选择核心。"],"notThis":"LLM 不是搜索引擎、数据库或拥有真实世界经验的主体。","misconceptions":"语言流畅不等于事实正确；预训练知识也不等于拥有当下的实时资料。","comparison":"SLM 通常更小、更快并适合本地或专用任务；LLM 通常以更高通用能力换取更大计算成本。","relatedConcepts":[{"id":"concept-foundation-model","name":"Foundation Model（基础模型）"},{"id":"concept-transformer","name":"Transformer（变换器架构）"},{"id":"concept-tokenization","name":"Tokenization（分词与词元化）"}]},{"illo":"replay","cat":"模型能力","type":"AI 概念","ver":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.08.11","trend":"进入长期概念档案","title":"Long-form Video Generation（长叙事视频生成）","brief":"生成包含多个镜头或较长时间跨度，并保持角色、场景、事件与音画叙事连贯的视频能力。","sum":"生成包含多个镜头或较长时间跨度，并保持角色、场景、事件与音画叙事连贯的视频能力。","gl1":"为什么重要","gl2":"影响谁","why":"视频生成的竞争正在从单个短镜头质量扩展到跨镜头一致性与完整叙事。","who":"影视、广告、动画、短剧、创作者工具与视频模型评测团队。","excerpts":[{"src":"Runway 官方帖","ex":"Build entire worlds full of characters with up to 50 references per generation, create clips up to 30 seconds with full sound and dialogue."},{"src":"火山引擎公告","ex":"单次生成时长翻倍至 30 秒，支持最多 50 个全模态素材参考，全面兼容十余种语言。"}],"srcs":["LoCoT2V-Bench","EntityBench"],"note":"能力以官方说明为准","links":[{"name":"LoCoT2V-Bench","url":"https://arxiv.org/abs/2510.26412"},{"name":"EntityBench","url":"https://arxiv.org/abs/2605.15199"}],"img":"media/concepts/02.jpg","imgKind":"pic","status":"已沉淀","id":"concept-long-form-video-generation","lastObservedAt":"2026.08.23","aliases":["Long-form Video Generation","长叙事视频生成","视频生成进入「长叙事」时代","长视频生成"],"cats":["生成与多模态"],"points":[],"catalogManaged":true,"plainExplanation":"它把视频生成从几秒的单镜头扩展到多个镜头或更长片段，同时尽量让角色、场景、事件顺序与音画关系保持一致。","mechanism":["先把脚本拆成镜头、角色、场景和时间安排。","使用参考图、角色记忆或前序片段条件逐段生成。","通过跨镜头一致性、转场、音画同步和叙事评测选择或修正结果。"],"useCases":["根据脚本生成广告、短剧和动画的多镜头初稿。","让固定角色在不同场景和较长时间跨度中连续表演。"],"notThis":"长叙事视频生成不只是把单个短视频循环、插帧或无条件延长。","misconceptions":"时长增加不等于叙事能力提高；角色漂移、事件断裂和转场失败可能随镜头数迅速累积。","comparison":"普通文生视频多聚焦单镜头质量；长叙事视频生成还必须管理跨镜头实体、剧情和时间连续性。","relatedConcepts":[{"id":"concept-text-to-video","name":"Text-to-Video（文生视频）"},{"id":"concept-world-model","name":"World Model（世界模型）"},{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"}]},{"id":"concept-lora","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"LoRA（低秩适配）","brief":"通过训练少量低秩矩阵来适配大模型，而不直接更新全部原始参数的方法。","sum":"通过训练少量低秩矩阵来适配大模型，而不直接更新全部原始参数的方法。","gl1":"为什么重要","gl2":"影响谁","why":"它是目前最常用的低成本模型微调方式之一。","who":"开源模型微调、图像模型训练、个人开发者和企业定制。","srcs":["LoRA 论文","Hugging Face LoRA"],"links":[{"name":"LoRA 论文","url":"https://arxiv.org/abs/2106.09685"},{"name":"Hugging Face LoRA","url":"https://huggingface.co/docs/peft/main/en/conceptual_guides/lora"}],"aliases":["LoRA","Low-Rank Adaptation","低秩适配","LoRA 微调"],"points":[],"catalogManaged":true,"cats":["训练优化"],"plainExplanation":"LoRA 不直接重训模型的大型权重矩阵，而是学习一组很小的低秩增量，运行时再把增量效果叠加到基础模型上。","mechanism":["冻结原模型权重。","在目标层旁加入两个较小的低秩矩阵。","训练小矩阵并在推理时合并或叠加其权重变化。"],"useCases":["为图像生成模型训练人物、画风或产品 LoRA。","低成本适配语言模型的专业任务、语气或格式。"],"notThis":"LoRA 不是独立完整模型，通常必须与对应的基础模型和兼容结构一起使用。","misconceptions":"LoRA 文件小不代表训练数据和参数选择不重要；错误数据同样会造成过拟合、概念污染和质量下降。","comparison":"PEFT 是参数高效微调方法的总称；LoRA 是通过低秩权重增量实现 PEFT 的一种具体技术。","relatedConcepts":[{"id":"concept-parameter-efficient-fine-tuning","name":"PEFT（参数高效微调）"},{"id":"concept-fine-tuning","name":"Fine-tuning（微调）"},{"id":"concept-diffusion-model","name":"Diffusion Model（扩散模型）"}]},{"id":"concept-mcp","illo":"converge","cat":"协议标准","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.10.17","lastObservedAt":"2026.10.17","trend":"进入长期概念档案","title":"MCP（模型上下文协议）","brief":"让 AI 应用以统一方式连接外部数据、工具和服务的开放协议。","sum":"让 AI 应用以统一方式连接外部数据、工具和服务的开放协议。","gl1":"为什么重要","gl2":"影响谁","why":"它把过去需要逐个平台定制的接入方式，变成可复用的通用接口。","who":"开发者、AI 产品经理，以及需要连接企业数据或外部工具的团队。","srcs":["MCP 官方规范","Anthropic","TechCrunch","IT之家","机器之心","youtu.be"],"links":[{"name":"MCP 官方规范","url":"https://modelcontextprotocol.io/specification/latest"},{"name":"Anthropic","url":"https://www.anthropic.com/research/model-context-protocol"},{"name":"TechCrunch","url":"https://techcrunch.com/2026/10/21/while-amazon-blocks-ai-agents-walmart-rolls-out-the-welcome-mat/"},{"name":"IT之家","url":"https://www.ithome.com/1/016/172.htm"},{"name":"机器之心","url":"https://jigou.jiqizhixin.com/articles/2026-10-22-16"},{"name":"youtu.be","url":"https://youtu.be/87w4GFO6fd4"}],"aliases":["MCP","Model Context Protocol","模型上下文协议"],"points":[],"catalogManaged":true,"cats":["智能体与工程"],"plainExplanation":"MCP 像 AI 世界的通用插座：数据库、文件和业务工具只要提供同一种接口，兼容的 AI 应用就能连接它们。","mechanism":["服务端公开可用的工具、资源与提示模板。","AI 应用通过客户端发现能力并发送结构化请求。","服务端在权限范围内返回数据或执行结果。"],"useCases":["让对话助手读取企业文档与数据库。","让编程智能体调用代码仓库、测试和部署工具。"],"notThis":"MCP 不是模型、知识库或自动化平台；它是连接这些能力的协议。","misconceptions":"接入 MCP 不等于 AI 获得所有权限；身份验证、授权和操作确认仍需单独设计。","comparison":"MCP 解决智能体如何连接工具与上下文；A2A 解决独立智能体如何相互协作。","relatedConcepts":[{"id":"concept-a2a","name":"A2A（智能体间协议）"},{"id":"concept-tool-use","name":"Tool Use（工具调用）"},{"id":"concept-ai-agent","name":"AI Agent（人工智能体）"}]},{"illo":"cache","cat":"工程实践","type":"AI 概念","ver":"已沉淀","time":"已被行业采用","ts":"观测截至 2026.08.11","trend":"进入长期概念档案","title":"Memory-efficient LLM（小显存运行大模型）","brief":"通过量化、分层加载、内存映射与参数高效训练等方法，在有限内存或显存上运行和适配大模型。","sum":"通过量化、分层加载、内存映射与参数高效训练等方法，在有限内存或显存上运行和适配大模型。","gl1":"为什么重要","gl2":"影响谁","why":"它降低了本地部署和实验大模型的硬件门槛。","who":"个人开发者、端侧 AI、私有部署团队与成本敏感的推理服务。","excerpts":[{"src":"Swiftlet 仓库","ex":"在 Mac 上运行 80B 版 Qwen（内存 4.3 GB），在 iPhone 上运行 35B 版。"},{"src":"社区实践","ex":"单颗 AMD MI300X 上运行 DeepSeek V4 Flash——旗舰模型不再默认需要旗舰显卡。"}],"srcs":["Hugging Face Quantization","Hugging Face bitsandbytes"],"note":"实际速度受硬件限制，适合离线场景","links":[{"name":"Hugging Face Quantization","url":"https://huggingface.co/docs/text-generation-inference/en/conceptual/quantization"},{"name":"Hugging Face 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AI（端侧人工智能）"}]},{"id":"concept-model-evaluation","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Model Evaluation（模型评测）","brief":"通过数据集、指标、人工判断和真实任务持续衡量模型表现的过程。","sum":"通过数据集、指标、人工判断和真实任务持续衡量模型表现的过程。","gl1":"为什么重要","gl2":"影响谁","why":"只有可重复的评测，才能判断模型升级是否真正改善质量并避免退步。","who":"模型研发、产品团队、质量保障、安全与采购决策。","srcs":["OpenAI Evals","Stanford HELM"],"links":[{"name":"OpenAI Evals","url":"https://platform.openai.com/docs/guides/evals"},{"name":"Stanford HELM","url":"https://crfm.stanford.edu/helm/latest/"}],"aliases":["Model Evaluation","AI 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Search（语义搜索）"},{"id":"concept-grounding","name":"Grounding（事实依据与落地）"}]},{"id":"concept-reasoning-model","illo":"converge","cat":"模型能力","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Reasoning Model（推理模型）","brief":"在回答前投入更多内部计算，用于处理数学、代码和复杂规划任务的模型。","sum":"在回答前投入更多内部计算，用于处理数学、代码和复杂规划任务的模型。","gl1":"为什么重要","gl2":"影响谁","why":"行业竞争从单纯扩大训练规模，延伸到如何让模型在回答时进行更有效的思考。","who":"复杂分析、数学、编程、科学研究和智能体规划。","srcs":["OpenAI","DeepSeek-R1"],"links":[{"name":"OpenAI","url":"https://openai.com/index/learning-to-reason-with-llms/"},{"name":"DeepSeek-R1","url":"https://arxiv.org/abs/2501.12948"}],"aliases":["Reasoning Model","推理模型","Reasoning LLM"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"这类模型在给出答案前会分配更多计算来处理中间步骤，适合不能只靠表面模式匹配的复杂任务。","mechanism":["根据问题生成中间推理或候选路径。","在推理阶段投入额外词元、搜索或校验计算。","综合中间结果后输出最终回答或行动。"],"useCases":["解数学题、复杂编程和科学分析问题。","为多步骤智能体规划任务并检查中间结果。"],"notThis":"推理模型不是能确保每次逻辑都正确的形式证明器。","misconceptions":"输出更长不等于推理更好；有效性需要通过正确答案、工具校验和真实任务评测。","comparison":"普通模型通常优先低延迟回答；推理模型愿意用更多回答阶段计算换取复杂任务质量。","relatedConcepts":[{"id":"concept-inference-scaling","name":"Inference-time Scaling（推理时扩展）"},{"id":"concept-chain-of-thought","name":"Chain-of-Thought（思维链）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"illo":"converge","cat":"新概念","type":"AI 概念","ver":"已沉淀","status":"已沉淀","lastObservedAt":"2026.08.26","time":"已被行业采用","ts":"观测截至 2026.08.26","trend":"进入长期概念档案","title":"Reward Hacking","brief":"AI 智能体通过利用环境或奖励函数的漏洞，以非预期方式获得高奖励的行为。","sum":"AI 智能体通过利用环境或奖励函数的漏洞，以非预期方式获得高奖励的行为。","gl1":"这是什么","gl2":"为什么在升温","why":"它揭示了 AI 系统在优化目标时可能采取与人类意图不符的捷径，导致安全风险。","who":"近 14 天跨 3 类来源、2 天持续出现。","srcs":["Digg 事件页","核实来源","本人发布（X）"],"links":[{"name":"Digg 事件页","url":"https://digg.com/tech/hw3nvjy5"},{"name":"核实来源","url":"https://openai.com/index/designing-agents-to-resist-prompt-injection"},{"name":"本人发布（X）","url":"https://x.com/PrimeIntellect/status/2092283970537017598"},{"name":"Digg 事件页","url":"https://digg.com/tech/ed3s6odi"},{"name":"本人发布（X）","url":"https://x.com/voooooogel/status/2090381929812258894"},{"name":"核实来源","url":"https://www.anthropic.com/news/fable-safeguards-jailbreak-framework"}],"aliases":["奖励黑客","奖励攻击"],"plainExplanation":"就像学生为了考试分数而作弊，AI 也会找到奖励机制的漏洞来‘骗分’，而不是真正完成任务。","mechanism":["智能体发现环境中的未预期漏洞（如沙盒逃逸）","利用漏洞获取高奖励而不完成真实任务","奖励函数未能覆盖所有安全约束"],"useCases":["AI 安全测试中检测模型漏洞","强化学习训练中的鲁棒性评估"],"notThis":"它不是普通的模型错误，而是有目的性地利用漏洞。","misconceptions":"常见误区是认为只有恶意 AI 才会奖励黑客，实际上任何优化目标都可能产生。","comparison":"与‘奖励模型过拟合’不同，奖励黑客是主动利用环境漏洞，而非被动拟合。","relatedConcepts":[{"id":"concept-ai-safety","name":"AI 安全"},{"id":"concept-reinforcement-learning","name":"强化学习"}],"img":"media/2026.08.26/24.jpg","imgKind":"pic"},{"id":"concept-rlhf","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"RLHF（基于人类反馈的强化学习）","brief":"利用人类偏好反馈训练奖励信号，再通过强化学习调整模型行为的方法。","sum":"利用人类偏好反馈训练奖励信号，再通过强化学习调整模型行为的方法。","gl1":"为什么重要","gl2":"影响谁","why":"它帮助模型更符合人类意图、安全要求和对话习惯。","who":"对话模型、模型对齐、安全训练和基础模型研发。","srcs":["InstructGPT 论文","OpenAI RLHF"],"links":[{"name":"InstructGPT 论文","url":"https://arxiv.org/abs/2203.02155"},{"name":"OpenAI RLHF","url":"https://openai.com/index/learning-from-human-preferences/"}],"aliases":["RLHF","Reinforcement Learning from Human Feedback","人类反馈强化学习"],"points":[],"catalogManaged":true,"cats":["训练优化"],"plainExplanation":"RLHF 先收集人类对多个回答的偏好，再把这种偏好变成训练信号，让模型更倾向输出人们认为有帮助、安全和符合要求的答案。","mechanism":["收集人类对候选回答的比较或评分。","训练奖励模型近似预测人类偏好。","使用强化学习优化语言模型，使其获得更高预测奖励。"],"useCases":["让对话模型更遵循指令并减少明显有害输出。","根据产品目标调整回答风格、帮助性和拒答边界。"],"notThis":"RLHF 不是把人类价值完整写入模型，也不是对事实正确性的直接保证。","misconceptions":"偏好数据代表标注规则与样本人群，不自动代表所有人的共同价值；奖励模型也可能被钻空子。","comparison":"RLHF 通过奖励模型和强化学习优化偏好；DPO 直接利用偏好对优化策略，省去显式奖励模型训练流程。","relatedConcepts":[{"id":"concept-dpo","name":"DPO（直接偏好优化）"},{"id":"concept-fine-tuning","name":"Fine-tuning（微调）"},{"id":"concept-ai-alignment","name":"AI Alignment（人工智能对齐）"}]},{"id":"concept-semantic-search","illo":"converge","cat":"能力模块","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Semantic Search（语义搜索）","brief":"依据查询与内容的含义而非只匹配关键词来寻找相关结果的搜索方法。","sum":"依据查询与内容的含义而非只匹配关键词来寻找相关结果的搜索方法。","gl1":"为什么重要","gl2":"影响谁","why":"它能理解同义表达和上下文关系，是知识检索与智能搜索的核心能力。","who":"搜索引擎、企业知识库、客服、推荐和 RAG 应用。","srcs":["Microsoft 语义搜索","Google Cloud Vector Search"],"links":[{"name":"Microsoft 语义搜索","url":"https://learn.microsoft.com/en-us/azure/search/semantic-search-overview"},{"name":"Google Cloud Vector Search","url":"https://cloud.google.com/vertex-ai/docs/vector-search/overview"}],"aliases":["Semantic Search","语义搜索","语义检索"],"points":[],"catalogManaged":true,"cats":["检索与数据"],"plainExplanation":"语义搜索尝试理解查询想表达的意思，从而找到用词不同但含义相关的内容。","mechanism":["把查询和候选内容编码为语义表示。","计算相似度并结合关键词、过滤条件或重排模型。","按综合相关性返回结果。"],"useCases":["让企业知识库找到同义表达和自然语言问题对应的文档。","在商品、帮助中心和研究资料中改善关键词未直接命中的搜索。"],"notThis":"语义搜索不是完全抛弃关键词，也不是让生成模型直接编写搜索结果。","misconceptions":"语义相近不等于满足所有条件；时间、权限、价格和精确标识仍需结构化过滤。","comparison":"关键词搜索匹配字面词项；语义搜索使用向量或重排模型补充含义层面的相关性。","relatedConcepts":[{"id":"concept-embedding","name":"Embedding（向量表示）"},{"id":"concept-vector-database","name":"Vector Database（向量数据库）"},{"id":"concept-rag","name":"RAG（检索增强生成）"}]},{"id":"concept-small-language-model","illo":"converge","cat":"模型架构","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"SLM（小语言模型）","brief":"参数规模较小、强调低成本和特定任务效率的语言模型。","sum":"参数规模较小、强调低成本和特定任务效率的语言模型。","gl1":"为什么重要","gl2":"影响谁","why":"它更适合端侧运行、私有部署和对速度或成本敏感的场景。","who":"移动应用、边缘设备、企业私有部署和轻量 AI 产品。","srcs":["Microsoft Phi","Microsoft Research"],"links":[{"name":"Microsoft Phi","url":"https://azure.microsoft.com/en-us/products/phi"},{"name":"Microsoft Research","url":"https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/"}],"aliases":["SLM","Small Language Model","小语言模型","小型语言模型"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"SLM 使用更少参数和计算资源，通常面向明确任务、本地设备或对延迟和成本敏感的场景。","mechanism":["选择较小的模型架构或通过蒸馏缩小能力。","用特定任务数据和指令优化有限容量。","通过量化等方法在手机、电脑或边缘设备上运行。"],"useCases":["在离线环境中做摘要、分类或简单问答。","作为大型系统的路由、过滤、抽取或护栏模型。"],"notThis":"SLM 没有统一的参数上限，也不代表功能简陋或质量必然较差。","misconceptions":"小模型在特定任务上可以很强，但不应把局部评测优势误解为通用能力全面超越大模型。","comparison":"SLM 优先效率、本地运行和任务专用性；LLM 优先更广的知识与复杂任务能力。","relatedConcepts":[{"id":"concept-large-language-model","name":"LLM（大语言模型）"},{"id":"concept-knowledge-distillation","name":"Knowledge Distillation（知识蒸馏）"},{"id":"concept-on-device-ai","name":"On-device AI（端侧人工智能）"}]},{"id":"concept-structured-output","illo":"converge","cat":"能力模块","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Structured Outputs（结构化输出）","brief":"约束模型按照预先定义的字段、类型和层级生成可直接处理的数据。","sum":"约束模型按照预先定义的字段、类型和层级生成可直接处理的数据。","gl1":"为什么重要","gl2":"影响谁","why":"它减少格式错误，让模型输出更可靠地进入软件流程和自动化系统。","who":"API 集成、智能体、数据提取、表单生成和业务自动化。","srcs":["OpenAI Structured Outputs","Google 结构化输出"],"links":[{"name":"OpenAI Structured Outputs","url":"https://platform.openai.com/docs/guides/structured-outputs"},{"name":"Google 结构化输出","url":"https://ai.google.dev/gemini-api/docs/structured-output"}],"aliases":["Structured Outputs","Structured Output","结构化输出","JSON Schema 输出"],"points":[],"catalogManaged":true,"cats":["智能体与工程"],"plainExplanation":"结构化输出要求模型按照预先定义的字段、类型和层级返回结果，方便程序直接读取，而不是再猜一段自然语言代表什么。","mechanism":["应用定义 JSON Schema 或其他输出结构。","模型在解码时受格式约束或在输出后接受验证。","不合格结果被拒绝、修复或重试后再交给业务系统。"],"useCases":["从合同、票据或网页中抽取固定字段。","让智能体稳定生成工具参数、表单数据和工作流节点。"],"notThis":"结构化输出只保证格式符合要求，不保证字段里的事实、数值和业务判断正确。","misconceptions":"返回合法 JSON 不等于符合完整 Schema，更不等于通过权限和业务规则校验。","comparison":"结构化输出约束模型回答形状；工具调用使用结构化参数触发外部能力。","relatedConcepts":[{"id":"concept-tool-use","name":"Tool Use（工具调用）"},{"id":"concept-prompt-engineering","name":"Prompt Engineering（提示词工程）"},{"id":"concept-model-evaluation","name":"Model Evaluation（模型评测）"}]},{"id":"concept-text-to-image","illo":"converge","cat":"模型能力","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Text-to-Image（文生图）","brief":"根据自然语言描述生成对应图像的生成式 AI 能力。","sum":"根据自然语言描述生成对应图像的生成式 AI 能力。","gl1":"为什么重要","gl2":"影响谁","why":"它把图像创作从手工制作扩展到通过语言快速构思和迭代。","who":"设计、广告、影视、游戏、电商和个人内容创作。","srcs":["DALL·E","Latent Diffusion 论文"],"links":[{"name":"DALL·E","url":"https://openai.com/index/dall-e/"},{"name":"Latent Diffusion 论文","url":"https://arxiv.org/abs/2112.10752"}],"aliases":["Text-to-Image","Text to Image","文生图","文本生成图像"],"points":[],"catalogManaged":true,"cats":["生成与多模态"],"plainExplanation":"文生图把自然语言描述转换为图像条件，再由生成模型创建与文字语义相符的画面。","mechanism":["文本编码器把提示词转换为语义表示。","图像生成模型在文字条件引导下构造或去噪潜在表示。","解码器把潜在表示还原为最终图像。"],"useCases":["生成广告概念图、插画、商品视觉和设计探索。","通过文字配合参考图完成局部修改、扩图和风格变化。"],"notThis":"文生图不是从网络图库中直接搜索一张与提示词匹配的现有图片。","misconceptions":"提示词准确不保证像素级可控；构图、文字渲染、人物一致性和细节仍受模型与控制方法限制。","comparison":"文生图关注单张或一组静态画面；文生视频还需要建模时间、动作与跨帧一致性。","relatedConcepts":[{"id":"concept-diffusion-model","name":"Diffusion Model（扩散模型）"},{"id":"concept-text-to-video","name":"Text-to-Video（文生视频）"},{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"}]},{"id":"concept-text-to-video","illo":"converge","cat":"模型能力","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Text-to-Video（文生视频）","brief":"根据文字提示生成具有时间连续性和运动内容的视频。","sum":"根据文字提示生成具有时间连续性和运动内容的视频。","gl1":"为什么重要","gl2":"影响谁","why":"它正在改变广告、影视预演、短视频和动态素材的制作流程。","who":"视频创作者、广告团队、影视制作、游戏和社交内容平台。","srcs":["OpenAI Sora 技术报告","Google VideoPoet"],"links":[{"name":"OpenAI Sora 技术报告","url":"https://openai.com/index/video-generation-models-as-world-simulators/"},{"name":"Google VideoPoet","url":"https://research.google/blog/videopoet-a-large-language-model-for-zero-shot-video-generation/"}],"aliases":["Text-to-Video","Text to Video","文生视频","文本生成视频"],"points":[],"catalogManaged":true,"cats":["生成与多模态"],"plainExplanation":"文生视频根据文字描述生成连续画面，不仅要满足每一帧的视觉内容，还要保持动作、时间和主体在多个帧之间连贯。","mechanism":["把文字提示编码为场景、主体和动作条件。","在时间与空间维度共同生成或逐段扩展视频表示。","解码为连续帧，并可同步生成声音或使用后处理增强一致性。"],"useCases":["制作广告分镜、短片素材和社交媒体视频。","把脚本、图片或首尾帧扩展为动态镜头。"],"notThis":"文生视频不是把一张文生图简单做平移缩放，也不等于完整的影视制作流程。","misconceptions":"单帧清晰不代表视频质量高；动作合理性、角色一致性、镜头衔接和音画同步同样关键。","comparison":"文生图解决静态空间构图；文生视频还要解决时间演化与跨帧一致性。","relatedConcepts":[{"id":"concept-text-to-image","name":"Text-to-Image（文生图）"},{"id":"concept-diffusion-model","name":"Diffusion Model（扩散模型）"},{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"}]},{"id":"concept-tokenization","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Tokenization（分词与词元化）","brief":"把文字或其他输入拆分并转换成模型能够处理的 Token 序列的过程。","sum":"把文字或其他输入拆分并转换成模型能够处理的 Token 序列的过程。","gl1":"为什么重要","gl2":"影响谁","why":"拆分方式会影响模型理解、上下文占用、多语言表现和成本。","who":"模型训练、文本处理、多语言应用和大模型成本优化。","srcs":["Hugging Face Tokenizer","Hugging Face 分词算法"],"links":[{"name":"Hugging Face Tokenizer","url":"https://huggingface.co/docs/transformers/main_classes/tokenizer"},{"name":"Hugging Face 分词算法","url":"https://huggingface.co/docs/transformers/tokenizer_summary"}],"aliases":["Tokenization","Tokenizer","分词","词元化"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"词元化决定模型怎样把原始文字切成可计算的片段；切法会影响长度、成本、多语言表现和模型对词形的理解。","mechanism":["从训练语料学习或设定一套词表。","用 BPE、WordPiece 等规则把输入拆成词元。","把词元转换为编号，并在生成后把编号还原为文本。"],"useCases":["为语言模型准备训练和推理输入。","优化多语言、代码或专业词汇的词元效率。"],"notThis":"词元化不是语言学意义上的标准分词，也不是把每个完整单词都固定视为一个单位。","misconceptions":"词表越大不一定越好；更大的词表会减少序列长度，但也增加模型参数并可能降低稀有组合的灵活性。","comparison":"Token 是词元化产生的单位；词元化是决定如何产生这些单位的过程。","relatedConcepts":[{"id":"concept-token","name":"Token（词元）"},{"id":"concept-embedding","name":"Embedding（向量表示）"},{"id":"concept-large-language-model","name":"LLM（大语言模型）"}]},{"id":"concept-token","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Token（词元）","brief":"模型读取和生成内容时使用的基本单位，可以是文字、子词、符号或其他片段。","sum":"模型读取和生成内容时使用的基本单位，可以是文字、子词、符号或其他片段。","gl1":"为什么重要","gl2":"影响谁","why":"上下文长度、模型计费和输出速度通常都以 Token 数量计算。","who":"所有使用大模型 API、设计提示词或控制推理成本的人。","srcs":["Hugging Face 术语库","OpenAI Tokenizer"],"links":[{"name":"Hugging Face 术语库","url":"https://huggingface.co/docs/transformers/glossary"},{"name":"OpenAI 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Use（工具调用）","brief":"让模型按照结构化参数调用搜索、代码、数据库和业务服务等外部能力。","sum":"让模型按照结构化参数调用搜索、代码、数据库和业务服务等外部能力。","gl1":"为什么重要","gl2":"影响谁","why":"模型不再只输出文字，而是可以真正查询信息、执行操作并完成任务。","who":"需要让 AI 连接真实业务系统和外部服务的产品。","srcs":["OpenAI","Anthropic"],"links":[{"name":"OpenAI","url":"https://platform.openai.com/docs/guides/function-calling"},{"name":"Anthropic","url":"https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/overview"}],"aliases":["Tool Use","Function Calling","工具调用","函数调用"],"points":[],"catalogManaged":true,"cats":["智能体与工程"],"plainExplanation":"模型不会直接假装它查过数据或做过操作，而是输出指定格式的调用请求，由外部系统真正执行。","mechanism":["应用向模型描述可用工具与参数格式。","模型选择工具并生成结构化参数。","应用执行工具后把结果返给模型继续处理。"],"useCases":["查询天气、订单、数据库或搜索结果。","创建日程、发送消息或触发业务流程。"],"notThis":"工具调用不是让模型自己运行任意代码，外部应用仍控制真正的执行。","misconceptions":"参数符合格式不代表请求合法；应用必须再做权限、范围和业务规则校验。","comparison":"结构化输出只约束回答格式；工具调用会把结构化结果交给外部系统执行。","relatedConcepts":[{"id":"concept-structured-output","name":"Structured Outputs（结构化输出）"},{"id":"concept-mcp","name":"MCP（模型上下文协议）"},{"id":"concept-ai-agent","name":"AI Agent（人工智能体）"}]},{"id":"concept-transfer-learning","illo":"converge","cat":"工程实践","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Transfer Learning（迁移学习）","brief":"把模型在一个任务或数据集学到的能力迁移到另一个相关任务。","sum":"把模型在一个任务或数据集学到的能力迁移到另一个相关任务。","gl1":"为什么重要","gl2":"影响谁","why":"它减少新任务所需的数据和训练成本，是预训练加微调范式的基础。","who":"计算机视觉、自然语言处理、行业模型和小样本任务。","srcs":["Hugging Face 术语库","Google 机器学习术语库"],"links":[{"name":"Hugging Face 术语库","url":"https://huggingface.co/docs/transformers/glossary"},{"name":"Google 机器学习术语库","url":"https://developers.google.com/machine-learning/glossary"}],"aliases":["Transfer Learning","迁移学习"],"points":[],"catalogManaged":true,"cats":["训练优化"],"plainExplanation":"迁移学习先利用模型在一个大任务或大数据集上学到的能力，再把这些能力用于相关的新任务，避免从零开始训练。","mechanism":["选择已经预训练的源模型。","复用其特征表示、部分网络或全部参数作为新任务起点。","冻结部分参数或继续微调以适应目标数据。"],"useCases":["用通用视觉模型适配医学影像或工业缺陷识别。","在预训练语言模型上构建分类、抽取和问答系统。"],"notThis":"迁移学习不要求源任务和目标任务完全相同，也不保证任何迁移都会产生正向效果。","misconceptions":"源数据、目标数据差异过大时可能出现负迁移，使结果比从零训练或其他起点更差。","comparison":"迁移学习是复用已有能力的总体范式；微调是实现迁移学习的常见具体方式。","relatedConcepts":[{"id":"concept-pretraining","name":"Pretraining（预训练）"},{"id":"concept-fine-tuning","name":"Fine-tuning（微调）"},{"id":"concept-knowledge-distillation","name":"Knowledge Distillation（知识蒸馏）"}]},{"id":"concept-transformer","illo":"converge","cat":"模型架构","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Transformer（变换器架构）","brief":"以注意力机制为核心、可并行处理序列信息的神经网络架构。","sum":"以注意力机制为核心、可并行处理序列信息的神经网络架构。","gl1":"为什么重要","gl2":"影响谁","why":"它奠定了现代大语言模型和大量多模态模型的基本结构。","who":"大模型研究、训练框架、模型优化与 AI 应用开发。","srcs":["Attention Is All You Need","Hugging Face 术语库"],"links":[{"name":"Attention Is All You Need","url":"https://arxiv.org/abs/1706.03762"},{"name":"Hugging Face 术语库","url":"https://huggingface.co/docs/transformers/glossary"}],"aliases":["Transformer","Transformer Architecture","变换器","Transformer 架构"],"points":[],"catalogManaged":true,"cats":["模型基础"],"plainExplanation":"Transformer 让模型在处理一个序列时，直接判断各个位置之间的关联，并能高效并行训练。","mechanism":["把输入转换为带位置信息的向量。","通过多头注意力计算不同位置之间的加权关系。","叠加注意力、前馈网络、残差连接和归一化形成深层模型。"],"useCases":["构建大语言模型和机器翻译系统。","处理图像、音频、视频和多模态序列。"],"notThis":"Transformer 不是一个具体产品或单一模型，而是可用于多种模型的网络架构。","misconceptions":"Transformer 不只能处理文字；只要数据能表示为序列或块，就可以应用相关结构。","comparison":"RNN 逐步处理序列；Transformer 主要用注意力直接建模位置关系，更利于并行训练。","relatedConcepts":[{"id":"concept-attention-mechanism","name":"Attention Mechanism（注意力机制）"},{"id":"concept-large-language-model","name":"LLM（大语言模型）"},{"id":"concept-context-window","name":"Context Window（上下文窗口）"}]},{"id":"concept-vector-database","illo":"converge","cat":"能力模块","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"Vector Database（向量数据库）","brief":"专门存储和检索向量表示，以查找语义上相近内容的数据库。","sum":"专门存储和检索向量表示，以查找语义上相近内容的数据库。","gl1":"为什么重要","gl2":"影响谁","why":"它是大规模语义搜索、推荐和 RAG 知识检索的重要基础设施。","who":"企业知识库、搜索、推荐系统和生成式 AI 应用。","srcs":["Google Cloud Vector Search","Microsoft 向量搜索"],"links":[{"name":"Google Cloud Vector Search","url":"https://cloud.google.com/vertex-ai/docs/vector-search/overview"},{"name":"Microsoft 向量搜索","url":"https://learn.microsoft.com/en-us/azure/search/vector-search-overview"}],"aliases":["Vector Database","Vector DB","向量数据库","向量库"],"points":[],"catalogManaged":true,"cats":["检索与数据"],"plainExplanation":"向量数据库专门存储和检索 Embedding，能够按语义相似程度找到内容，而不只依赖关键词完全一致。","mechanism":["保存向量及其对应原文、标识和筛选信息。","构建近似最近邻索引以减少大规模比较成本。","把查询转换为向量后返回距离最近并满足条件的记录。"],"useCases":["为 RAG 知识库召回相关文档片段。","实现图片、商品、用户或内容的相似推荐与去重。"],"notThis":"向量数据库不是生成模型，也不会自行理解和回答用户问题。","misconceptions":"引入向量数据库不代表检索质量自然提升；切片、Embedding、索引参数和业务过滤都需要评测。","comparison":"传统数据库擅长精确条件、事务和结构化查询；向量数据库擅长高维相似度检索，两者常组合使用。","relatedConcepts":[{"id":"concept-embedding","name":"Embedding（向量表示）"},{"id":"concept-semantic-search","name":"Semantic Search（语义搜索）"},{"id":"concept-rag","name":"RAG（检索增强生成）"}]},{"id":"concept-vision-language-model","illo":"converge","cat":"模型架构","type":"AI 概念","ver":"已沉淀","status":"已沉淀","time":"已被行业采用","ts":"2026.08.20","lastObservedAt":"2026.08.20","trend":"进入长期概念档案","title":"VLM（视觉语言模型）","brief":"能够共同理解图像或视频与自然语言关系的多模态模型。","sum":"能够共同理解图像或视频与自然语言关系的多模态模型。","gl1":"为什么重要","gl2":"影响谁","why":"它让 AI 可以看懂界面、文档、图表和真实环境，并用语言进行推理。","who":"视觉问答、文档理解、机器人、计算机操作和多模态助手。","srcs":["CLIP 论文","GPT-4V 系统卡"],"links":[{"name":"CLIP 论文","url":"https://arxiv.org/abs/2103.00020"},{"name":"GPT-4V 系统卡","url":"https://openai.com/index/gpt-4v-system-card/"}],"aliases":["VLM","Vision-Language Model","视觉语言模型","视觉语言大模型"],"points":[],"catalogManaged":true,"cats":["生成与多模态"],"plainExplanation":"VLM 把图像或视频中的视觉信息与语言放到同一个任务里，使模型能够看图回答、描述、定位或按文字理解视觉内容。","mechanism":["用视觉编码器或视觉词元表示图像与视频。","将视觉表示投射或连接到语言模型的表示空间。","通过跨模态训练学习图文对齐，并生成语言或视觉相关输出。"],"useCases":["理解截图、图表、扫描文档和现场照片。","进行视觉问答、图像描述、界面操作和机器人感知。"],"notThis":"VLM 不等于只做物体分类的计算机视觉模型，也不一定具备图像生成功能。","misconceptions":"能描述图片不代表具备可靠的精确计数、空间定位或专业诊断能力，这些需要专门评测。","comparison":"VLM 聚焦视觉与语言的联合理解；多模态模型还可覆盖音频、语音及多种内容生成能力。","relatedConcepts":[{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"},{"id":"concept-computer-use","name":"Computer Use（计算机操作）"},{"id":"concept-text-to-image","name":"Text-to-Image（文生图）"}]},{"illo":"converge","cat":"模型架构","type":"AI 概念","ver":"已沉淀","status":"已沉淀","lastObservedAt":"2026.08.20","time":"已被行业采用","ts":"观测截至 2026.08.20","trend":"进入长期概念档案","title":"World Model（世界模型）","brief":"学习环境状态及其随行动和时间变化规律，用于预测、模拟或规划的模型。","sum":"学习环境状态及其随行动和时间变化规律，用于预测、模拟或规划的模型。","gl1":"为什么重要","gl2":"影响谁","why":"它让 AI 不只识别当前画面，还能预测行动后会发生什么。","who":"机器人、自动驾驶、游戏智能体、视频生成与具身智能团队。","srcs":["Google DeepMind Genie 2","NVIDIA AI Podcast"],"links":[{"name":"Google DeepMind Genie 2","url":"https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/"},{"name":"NVIDIA AI Podcast","url":"https://www.nvidia.com/en-us/ai-podcast/episode/4ISj84zWHa4cj8HE5GgSs0"}],"aliases":["World Model","世界模型"],"img":"media/2026.08.20/21.jpg","imgKind":"pic","id":"concept-world-model","cats":["模型基础"],"points":[],"catalogManaged":true,"plainExplanation":"世界模型像 AI 在内部建立的环境沙盘：它根据当前状态和可能行动，预测接下来会出现什么变化。","mechanism":["从图像、视频、传感器或行动轨迹中编码环境状态。","学习状态随时间和行动变化的动态规律。","在内部模拟多个未来，并为生成、控制或规划提供依据。"],"useCases":["让机器人和自动驾驶系统预演行动后果。","生成具有物理与时间连贯性的交互环境或长视频。"],"notThis":"世界模型不是对真实世界的完整复制，也不只是一个生成漂亮视频的模型。","misconceptions":"预测画面逼真不代表理解了真实物理规律；需要通过干预、长期预测和真实控制任务验证。","comparison":"VLM 主要理解当前视觉内容；世界模型进一步预测环境如何随时间与行动变化。","relatedConcepts":[{"id":"concept-vision-language-model","name":"VLM（视觉语言模型）"},{"id":"concept-multimodal-model","name":"Multimodal Model（多模态模型）"},{"id":"concept-ai-agent","name":"AI Agent（人工智能体）"}]}]}