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  <id>https://yomxxx.com/</id>
  <title>YOMXXX</title>
  <subtitle>Frontier ML, decoded. Production AI, dissected.</subtitle>
  <link href="https://yomxxx.com/atom.xml" rel="self" />
  <link href="https://yomxxx.com/" />
  <updated>2026-06-29T00:00:00.000Z</updated>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-29-anthropic-mythos5-glasswing-ai-governance-long-form</id>
    <title>Mythos 5 的双重身份：Anthropic Project Glasswing 与 AI 能力管控的新范式</title>
    <link href="https://yomxxx.com/posts/2026-06-29-anthropic-mythos5-glasswing-ai-governance-long-form" />
    <updated>2026-06-29T00:00:00.000Z</updated>
    <published>2026-06-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解读 Anthropic Mythos 5 部分发布事件：美国政府审批机制、Project Glasswing 受控访问框架、Fable 5 安全分类器设计，以及这套双轨制部署模式对 AI 治理的深远意义。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-29-benchpress-microsoft-llm-benchmark-prediction-tools</id>
    <title>BenchPress 速评：5 个探针基准预测 LLM 完整性能矩阵，排名准确率 92.1%</title>
    <link href="https://yomxxx.com/posts/2026-06-29-benchpress-microsoft-llm-benchmark-prediction-tools" />
    <updated>2026-06-29T00:00:00.000Z</updated>
    <published>2026-06-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>评测微软研究院开源的 BenchPress 工具，利用基准分数的秩-2 结构，只用 5 个探针基准预测模型全量 benchmark 表现，中位数绝对误差 3.93 分，大幅降低 LLM 评测成本。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-29-claude-fable-5-api-production-integration-workshop</id>
    <title>Claude Fable 5 API 生产集成实战：refusal 处理、回退机制与迁移指南</title>
    <link href="https://yomxxx.com/posts/2026-06-29-claude-fable-5-api-production-integration-workshop" />
    <updated>2026-06-29T00:00:00.000Z</updated>
    <published>2026-06-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>详解 claude-fable-5 API 的 stop_reason refusal 新字段、Opus 4.8 安全分类器回退逻辑、计费规则与生产迁移最佳实践，附完整 TypeScript 与 Python 示例代码。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-29-danceopd-on-policy-generative-field-distillation-paper</id>
    <title>DanceOPD 论文速读：在线策略生成场蒸馏统一图像局部与全局编辑</title>
    <link href="https://yomxxx.com/posts/2026-06-29-danceopd-on-policy-generative-field-distillation-paper" />
    <updated>2026-06-29T00:00:00.000Z</updated>
    <published>2026-06-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>精读 arXiv:2606.27377，DanceOPD 通过在线策略蒸馏将局部编辑能力注入流匹配生成模型，GenEval 分数超越最优组合基线 16.1%，同时保留场景身份一致性。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-29-five-eyes-ai-cybersecurity-openai-daybreak-long-form</id>
    <title>AI 重写网络安全规则：五眼联盟警告、OpenAI Daybreak 与前沿模型管控的三角博弈</title>
    <link href="https://yomxxx.com/posts/2026-06-29-five-eyes-ai-cybersecurity-openai-daybreak-long-form" />
    <updated>2026-06-29T00:00:00.000Z</updated>
    <published>2026-06-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 2026 年 6 月五眼联盟 AI 网络安全紧急声明、OpenAI Daybreak 平台与 GPT-5.5-Cyber 发布，以及前沿 AI 能力在攻防两端的非对称扩散如何改变安全格局。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-28-cursor-composer-2-kimi-k2-agentic-coding-workshop</id>
    <title>Cursor Composer 2 实战：用 Kimi K2.5 MoE 驱动的 Agentic 编程模型</title>
    <link href="https://yomxxx.com/posts/2026-06-28-cursor-composer-2-kimi-k2-agentic-coding-workshop" />
    <updated>2026-06-28T00:00:00.000Z</updated>
    <published>2026-06-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 Cursor 自研 Composer 2 模型：基于 Kimi K2.5 MoE 架构，SWE-bench 多语言 73.7%，如何在真实工程项目中部署与调优 agentic 代码工作流。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-28-llm-observability-platforms-2026-tools</id>
    <title>LLM 可观测性平台 2026 横评：Future AGI vs Arize vs Langfuse vs Confident AI</title>
    <link href="https://yomxxx.com/posts/2026-06-28-llm-observability-platforms-2026-tools" />
    <updated>2026-06-28T00:00:00.000Z</updated>
    <published>2026-06-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>实测对比 2026 年主流 LLM 可观测性与评估平台：Future AGI、Arize AI、Langfuse、Confident AI，覆盖 tracing 能力、eval 框架、定价、部署方式和 agent 支持，帮助团队选出最适合的方案。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-28-gemini-3-5-pro-delay-agent-race-2026-long-form</id>
    <title>Gemini 3.5 Pro 跳票背后：2026 年大模型竞争的真实战场已经转移</title>
    <link href="https://yomxxx.com/posts/2026-06-28-gemini-3-5-pro-delay-agent-race-2026-long-form" />
    <updated>2026-06-28T00:00:00.000Z</updated>
    <published>2026-06-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Google 将 Gemini 3.5 Pro 延期至 7 月，表面是调参，实质是 2026 年大模型竞争逻辑的缩影——比拼的不再是通用 MMLU 分数，而是 agent 能力、推理成本和工作流集成深度。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-28-ai-weekly-2026-w26-weekly</id>
    <title>AI 周报 2026-W26：Composer 2 自研编程模型 / Gemini 3.5 Pro 延期 / Qwen-AgentWorld / Agent 记忆系统评估</title>
    <link href="https://yomxxx.com/posts/2026-06-28-ai-weekly-2026-w26-weekly" />
    <updated>2026-06-28T00:00:00.000Z</updated>
    <published>2026-06-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年第 26 周 AI 要点速览：Cursor 发布 Composer 2 编程专用模型、Google 延期 Gemini 3.5 Pro、阿里 Qwen-AgentWorld 语言世界模型开源、Agent 记忆系统系统性评估报告，以及 LLM 可观测性工具的成熟。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-28-qwen-agentworld-language-world-model-paper</id>
    <title>Qwen-AgentWorld 论文速读：用语言模型预测环境动态，统一训练通用 Agent</title>
    <link href="https://yomxxx.com/posts/2026-06-28-qwen-agentworld-language-world-model-paper" />
    <updated>2026-06-28T00:00:00.000Z</updated>
    <published>2026-06-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>阿里 Qwen 团队 arXiv 2606.24597：语言世界模型（LWM）通过长链式推理预测数字环境动态，训练 35B 和 397B 两个模型，在 7 个 agentic 基准上全面提升，零微调迁移也有效。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-27-coding-agent-verification-bottleneck-paper</id>
    <title>Coding Agent 的下一个瓶颈：为什么验证比生成更难了</title>
    <link href="https://yomxxx.com/posts/2026-06-27-coding-agent-verification-bottleneck-paper" />
    <updated>2026-06-27T00:00:00.000Z</updated>
    <published>2026-06-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>一篇新 arXiv 论文揭示 coding agent 开发中的深层悖论：模型推理能力提升后，生成候选代码解变得容易，但可靠验证这些解反而成了更难的问题——单一奖励函数无法在模型持续改进后保持有效。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-27-deerflow-2-super-agent-harness-local-deploy-workshop</id>
    <title>DeerFlow 2.0 实战：ByteDance 开源 Super Agent Harness 本地部署与自定义</title>
    <link href="https://yomxxx.com/posts/2026-06-27-deerflow-2-super-agent-harness-local-deploy-workshop" />
    <updated>2026-06-27T00:00:00.000Z</updated>
    <published>2026-06-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>DeerFlow 2.0 是 ByteDance 开源的 Super Agent Harness，支持多 Agent 编排、沙箱代码执行和可扩展 Skills 系统，本文从零演示本地部署流程，并介绍如何自定义 Skill 扩展能力。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-27-gpt-5-6-sol-terra-luna-api-guide-tools</id>
    <title>GPT-5.6 Sol/Terra/Luna 选型指南：三档定价、能力差距与 API 迁移</title>
    <link href="https://yomxxx.com/posts/2026-06-27-gpt-5-6-sol-terra-luna-api-guide-tools" />
    <updated>2026-06-27T00:00:00.000Z</updated>
    <published>2026-06-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenAI 发布 GPT-5.6 三档模型：旗舰 Sol（$5/$30 per 1M tokens）、生产级 Terra（$2.5/$15）、经济型 Luna（$1/$6），本文解析三档定价逻辑、能力差距，帮助开发者做出最优选型决策。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-27-openai-full-stack-jalapeno-gpt56-us-ai-regulation-long-form</id>
    <title>Jalapeño、GPT-5.6、联邦监管：OpenAI 正在下一盘更大的棋</title>
    <link href="https://yomxxx.com/posts/2026-06-27-openai-full-stack-jalapeno-gpt56-us-ai-regulation-long-form" />
    <updated>2026-06-27T00:00:00.000Z</updated>
    <published>2026-06-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026年6月最后一周，OpenAI 发布自研推理芯片 Jalapeño、推出 GPT-5.6 三档模型、并配合联邦政府分级审查——三件事合并成一个信号：OpenAI 在打造从硅片到模型到政策框架的全栈 AI 基础设施，这对整个行业的影响远超单一产品发布。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-27-illada-improved-diffusion-language-model-paper</id>
    <title>iLLaDA 精读：扩散语言模型首次与自回归 7B 模型正面对决</title>
    <link href="https://yomxxx.com/posts/2026-06-27-illada-improved-diffusion-language-model-paper" />
    <updated>2026-06-27T00:00:00.000Z</updated>
    <published>2026-06-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv:2606.25331 提出 iLLaDA，一个 8B 双向掩码扩散语言模型，通过扩展预训练语料至 12T tokens、优化 SFT 策略和引入变长生成，在 BBH 和 MATH 上大幅超越前代 LLaDA，并与 Qwen2.5 7B 竞争。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-26-axiom-neuro-symbolic-verifiable-math-reasoning-paper</id>
    <title>AXIOM：让 LLM 做规范化器而非求解器，CAS 验证数学推理的可信架构</title>
    <link href="https://yomxxx.com/posts/2026-06-26-axiom-neuro-symbolic-verifiable-math-reasoning-paper" />
    <updated>2026-06-26T00:00:00.000Z</updated>
    <published>2026-06-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>解读 arXiv 2606.00671——AXIOM 提出四条设计原则：LLM 作规范化器、CAS 确定性验证、1:1:1 任务路由、abstain 作为一等公民，在数学推理上实现运行时可信保证。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-26-glm-5-2-open-source-llm-frontier-long-form</id>
    <title>GLM-5.2：开源旗舰大模型的战略拐点，以六分之一成本击败 GPT-5.5</title>
    <link href="https://yomxxx.com/posts/2026-06-26-glm-5-2-open-source-llm-frontier-long-form" />
    <updated>2026-06-26T00:00:00.000Z</updated>
    <published>2026-06-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Z.ai 的 GLM-5.2（753B 参数，MIT 许可证，1M token 上下文）在 FrontierSWE 排名第一，AIME 2026 达到 99.2%，比肩闭源前沿模型却只需六分之一 API 成本。深度分析这一开源旗舰背后的战略意义。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-26-local-vlm-benchmark-june-2026-qwen3-vl-tools</id>
    <title>本地 VLM 横评 2026-06：Qwen3-VL 8B 还是 Qwen3.6 27B，谁是最优解？</title>
    <link href="https://yomxxx.com/posts/2026-06-26-local-vlm-benchmark-june-2026-qwen3-vl-tools" />
    <updated>2026-06-26T00:00:00.000Z</updated>
    <published>2026-06-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 Reddit r/LocalLLaMA 社区 6 月实测数据，对比 Qwen3-VL 8B、Qwen3.6 27B、Qwen3.6 35B-A3B 等主流本地视觉语言模型在 OCR、图像描述和视觉推理上的实际表现。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-26-docker-ai-agent-sandbox-microvm-workshop</id>
    <title>Docker MicroVM 沙箱：给 AI Agent 代码执行加上安全护栏</title>
    <link href="https://yomxxx.com/posts/2026-06-26-docker-ai-agent-sandbox-microvm-workshop" />
    <updated>2026-06-26T00:00:00.000Z</updated>
    <published>2026-06-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从容器到 MicroVM，详解如何用 Docker + Firecracker/gVisor 为 LLM Agent 代码执行构建隔离沙箱，附完整 Python 示例和生产级最佳实践。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-26-safari-agentic-fault-attribution-multi-agent-paper</id>
    <title>SAFARI：用主动调查扩展多智能体系统的长视野故障归因</title>
    <link href="https://yomxxx.com/posts/2026-06-26-safari-agentic-fault-attribution-multi-agent-paper" />
    <updated>2026-06-26T00:00:00.000Z</updated>
    <published>2026-06-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>解读 arXiv 2606.24626——SAFARI 通过主动调查机制扩展 Agent 故障归因，在多智能体长视野任务中超越人类标注和单 LLM 评判方法，提供可落地的工程参考。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-25-fapo-autonomous-prompt-optimization-pipeline-tools</id>
    <title>FAPO 评测：全自动多步骤 LLM 流水线提示优化，告别手写 Prompt 调参</title>
    <link href="https://yomxxx.com/posts/2026-06-25-fapo-autonomous-prompt-optimization-pipeline-tools" />
    <updated>2026-06-25T00:00:00.000Z</updated>
    <published>2026-06-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>FAPO 将多步骤 LLM 流水线的提示优化自动化——不只优化单个 Prompt，而是把整条流水线当成一个系统来调试。与 DSPy、TextGrad 对比，分析适用场景与局限</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-25-moebius-02b-image-inpainting-eccv-2026-paper</id>
    <title>Moebius 论文速读：0.2B 参数达到 10B 级图像修复效果，知识蒸馏挑战 Scaling Law</title>
    <link href="https://yomxxx.com/posts/2026-06-25-moebius-02b-image-inpainting-eccv-2026-paper" />
    <updated>2026-06-25T00:00:00.000Z</updated>
    <published>2026-06-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>ECCV 2026 论文 Moebius 仅用 0.2B 参数在图像修复任务上追平 10B 量级模型效果，通过架构精简与知识蒸馏颠覆了大力出奇迹的暴力扩参路线，延迟仅 26ms/step</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-25-rapid-mlx-apple-silicon-local-llm-workshop</id>
    <title>Rapid-MLX 实战：Apple Silicon 本地 LLM 推理速度提升 4 倍，完整工具调用支持</title>
    <link href="https://yomxxx.com/posts/2026-06-25-rapid-mlx-apple-silicon-local-llm-workshop" />
    <updated>2026-06-25T00:00:00.000Z</updated>
    <published>2026-06-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Rapid-MLX 是专为 Apple Silicon 打造的本地推理引擎，比 Ollama 快 4.2 倍，0.08s 缓存首 token，17 种工具解析器全覆盖。本文从安装到集成 Claude Code/Cursor 全流程实战</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-25-github-ai-slop-open-source-crisis-long-form</id>
    <title>GitHub AI PR 泛滥危机：当开源维护者成为 AI 生成垃圾代码的第一道防线</title>
    <link href="https://yomxxx.com/posts/2026-06-25-github-ai-slop-open-source-crisis-long-form" />
    <updated>2026-06-25T00:00:00.000Z</updated>
    <published>2026-06-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>AI 编码 Agent 让代码生成成本趋近于零，却让开源维护者陷入 AI 生成 PR 的汪洋。GitHub 宣布引入 PR 上限机制，但这只是治标。本文深度分析这场开源治理危机的根源与出路</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-25-ledgeragent-policy-adherent-tool-calling-paper</id>
    <title>LedgerAgent 论文速读：结构化状态让工具调用 Agent 严格遵守业务策略</title>
    <link href="https://yomxxx.com/posts/2026-06-25-ledgeragent-policy-adherent-tool-calling-paper" />
    <updated>2026-06-25T00:00:00.000Z</updated>
    <published>2026-06-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv 2026-06-19 论文 LedgerAgent 提出用 Ledger 结构化状态解决客服 Agent 工具调用不遵守业务策略的问题，在 tau2-bench 上显著超越基于提示的 baseline，为生产级策略遵从提供了新思路</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-12-ai-agent-framework-2026-comparison-tools</id>
    <title>2026 AI Agent 框架横评：LangGraph vs CrewAI vs Claude Agent SDK vs OpenAI Agents SDK</title>
    <link href="https://yomxxx.com/posts/2026-06-12-ai-agent-framework-2026-comparison-tools" />
    <updated>2026-06-12T00:00:00.000Z</updated>
    <published>2026-06-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>四大主流 AI Agent 框架从架构哲学到生产落地的全方位对比——LangGraph 图状态机、CrewAI 角色协作、Claude Agent SDK 自主编码、OpenAI Agents SDK 快速原型，附选型决策树</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-12-diffusiongemma-text-diffusion-local-deploy-workshop</id>
    <title>DiffusionGemma 实战：文本扩散模型本地部署，单用户推理速度提升 4 倍</title>
    <link href="https://yomxxx.com/posts/2026-06-12-diffusiongemma-text-diffusion-local-deploy-workshop" />
    <updated>2026-06-12T00:00:00.000Z</updated>
    <published>2026-06-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Google DeepMind 发布 DiffusionGemma，基于 Gemma 4 MoE 架构的文本扩散模型，用离散扩散替代自回归解码实现 4 倍推理加速。本文从原理到 vLLM 部署全流程实战</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-12-mcp-2026-07-28-stateless-protocol-redesign-long-form</id>
    <title>MCP 2026-07-28 规范重构全解析：从有状态握手到无状态协议的范式转移</title>
    <link href="https://yomxxx.com/posts/2026-06-12-mcp-2026-07-28-stateless-protocol-redesign-long-form" />
    <updated>2026-06-12T00:00:00.000Z</updated>
    <published>2026-06-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MCP 最大一次规范修订即将落地：移除握手和会话、引入 Extensions 框架、重新设计 Tasks、新增 MCP Apps。本文逐项解析每个变更的技术细节和迁移影响</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-12-miasma-worm-ai-coding-agent-supply-chain-attack-long-form</id>
    <title>Miasma 蠕虫深度剖析：当供应链攻击开始瞄准 AI 编码 Agent</title>
    <link href="https://yomxxx.com/posts/2026-06-12-miasma-worm-ai-coding-agent-supply-chain-attack-long-form" />
    <updated>2026-06-12T00:00:00.000Z</updated>
    <published>2026-06-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Miasma 蠕虫在 2 小时内感染 57 个 npm 包、286 个恶意版本，首次将 AI 编码助手（Claude、Cursor、Gemini）的配置目录作为持久化攻击面。本文完整还原攻击链并分析防御策略</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-12-speculative-speculative-decoding-async-inference-paper</id>
    <title>Speculative Speculative Decoding：让草稿模型永不等待的异步推理加速</title>
    <link href="https://yomxxx.com/posts/2026-06-12-speculative-speculative-decoding-async-inference-paper" />
    <updated>2026-06-12T00:00:00.000Z</updated>
    <published>2026-06-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>SSD 论文提出异步推测解码算法，草稿模型不再等待验证完成就开始下一轮推测，彻底隐藏草稿延迟。本文解析 SSD 核心原理、与标准推测解码的对比、AMD MI300X 上的工程实现</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-11-claude-managed-agents-production-deploy-workshop</id>
    <title>Claude Managed Agents 实战：10 分钟部署你的第一个生产级 AI Agent</title>
    <link href="https://yomxxx.com/posts/2026-06-11-claude-managed-agents-production-deploy-workshop" />
    <updated>2026-06-11T00:00:00.000Z</updated>
    <published>2026-06-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 推出的 Managed Agents API 让开发者无需管理基础设施即可部署自主运行的 AI Agent，本文从零开始用 Python 代码演示 Agent 创建、工具定义、Session 管理全流程</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-11-ai-coding-agent-battle-2026-claude-codex-gemini-long-form</id>
    <title>2026 年中 AI 编码 Agent 三国杀：Claude Code vs Codex vs Gemini Code Assist</title>
    <link href="https://yomxxx.com/posts/2026-06-11-ai-coding-agent-battle-2026-claude-codex-gemini-long-form" />
    <updated>2026-06-11T00:00:00.000Z</updated>
    <published>2026-06-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度对比 2026 年中三大 AI 编码 Agent——Claude Code、Codex、Gemini Code Assist 的架构设计、模型能力、SWE-bench 表现、定价和实际工作流体验，帮助开发者选择最适合的编码助手</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-11-gpt-5-5-technical-analysis-frontier-model-paper</id>
    <title>GPT-5.5 技术解析：OpenAI 前沿模型的能力跃迁与工程实践</title>
    <link href="https://yomxxx.com/posts/2026-06-11-gpt-5-5-technical-analysis-frontier-model-paper" />
    <updated>2026-06-11T00:00:00.000Z</updated>
    <published>2026-06-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenAI GPT-5.5 带来百万级上下文窗口和 84.9% GDPval 得分，本文从架构改进、基准表现到 API 实战全面解析这款前沿模型的能力边界和最佳实践</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-11-scaling-test-time-compute-llm-agents-paper</id>
    <title>Scaling Test-Time Compute for LLM Agents：当推理时计算遇上 Agent</title>
    <link href="https://yomxxx.com/posts/2026-06-11-scaling-test-time-compute-llm-agents-paper" />
    <updated>2026-06-11T00:00:00.000Z</updated>
    <published>2026-06-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>首篇系统研究推理时计算扩展在 Agent 场景中效果的论文，揭示了 best-of-N 采样在工具调用任务中的惊人优势，以及思维链长度的收益递减规律，为 Agent 工程优化提供了关键指导</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-11-claude-opus-4-8-dynamic-workflows-review-tools</id>
    <title>Claude Opus 4.8 Dynamic Workflows 深度评测：千级子代理编排新范式</title>
    <link href="https://yomxxx.com/posts/2026-06-11-claude-opus-4-8-dynamic-workflows-review-tools" />
    <updated>2026-06-11T00:00:00.000Z</updated>
    <published>2026-06-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 随 Opus 4.8 发布的 Dynamic Workflows 让 Claude Code 可编排最多 1000 个子代理并行工作，本文实测其在代码审查、大规模迁移等场景中的表现和工程实践</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-10-apple-foundation-models-swift-on-device-llm-workshop</id>
    <title>Apple Foundation Models 实战：用 Swift 调用端侧大模型</title>
    <link href="https://yomxxx.com/posts/2026-06-10-apple-foundation-models-swift-on-device-llm-workshop" />
    <updated>2026-06-10T00:00:00.000Z</updated>
    <published>2026-06-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>WWDC 2026 发布的 Foundation Models Framework 让开发者可以用原生 Swift API 直接调用 Apple Intelligence 端侧大模型，本文通过代码示例详解 Tool Calling、Guided Generation 等核心能力</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-10-claude-dreaming-agent-self-improving-memory-long-form</id>
    <title>Claude Dreaming 深度剖析：当 AI Agent 学会在空闲时进化</title>
    <link href="https://yomxxx.com/posts/2026-06-10-claude-dreaming-agent-self-improving-memory-long-form" />
    <updated>2026-06-10T00:00:00.000Z</updated>
    <published>2026-06-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 在 Code with Claude 2026 发布 Dreaming 功能，让 Agent 在空闲时回顾历史会话、提取模式并编写记忆笔记。本文深度分析这一机制的技术架构与行业影响</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-10-google-io-2026-agentic-web-webmcp-tools</id>
    <title>Google I/O 2026 Agentic Web 横评：WebMCP、Modern Web Guidance 与信息代理</title>
    <link href="https://yomxxx.com/posts/2026-06-10-google-io-2026-agentic-web-webmcp-tools" />
    <updated>2026-06-10T00:00:00.000Z</updated>
    <published>2026-06-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Google I/O 2026 发布 WebMCP、Modern Web Guidance 和信息代理三大能力，将 Web 推入 Agent 时代。本文逐一拆解技术细节并横向对比</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-10-anthropic-when-ai-builds-itself-recursive-self-improvement-paper</id>
    <title>当 AI 构建自身：Anthropic 递归自我改进路径深度解读</title>
    <link href="https://yomxxx.com/posts/2026-06-10-anthropic-when-ai-builds-itself-recursive-self-improvement-paper" />
    <updated>2026-06-10T00:00:00.000Z</updated>
    <published>2026-06-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 发布重磅博文 When AI Builds Itself，揭示 Claude 已编写 80% 内部代码的现实，分析递归自我改进的技术路径与潜在风险</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-10-paddleocr-vl-multimodal-document-parsing-workshop</id>
    <title>PaddleOCR-VL 1.5 实战：0.9B 参数撬动多模态文档解析</title>
    <link href="https://yomxxx.com/posts/2026-06-10-paddleocr-vl-multimodal-document-parsing-workshop" />
    <updated>2026-06-10T00:00:00.000Z</updated>
    <published>2026-06-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>PaddleOCR-VL 1.5 用仅 0.9B 参数实现了媲美大模型的多语言文档解析能力。本文从环境搭建到 RAG 集成，手把手演示如何用这个开源模型构建文档智能流水线</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-08-aidev-ai-coding-agents-github-empirical-study-paper</id>
    <title>论文速读：AIDev 用 93 万个 PR，拍下了 AI 队友重写 GitHub 的全景</title>
    <link href="https://yomxxx.com/posts/2026-06-08-aidev-ai-coding-agents-github-empirical-study-paper" />
    <updated>2026-06-08T00:00:00.000Z</updated>
    <published>2026-06-08T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>AI 不再只是代码补全，它已经在直接给真实仓库提 PR。AIDev 收集了 5 个编码 agent 在 GitHub 上自主提交的 93 万个 PR，揭示一个核心悖论：agent 又快又多，但 PR 更难被接受。本文精读这份「AI 队友」全景数据集和它的反直觉发现。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-08-long-horizon-agent-memory-architecture-evolution-long-form</id>
    <title>深度长文：长程 Agent 的记忆，正在从「检索过去」转向「管理状态」</title>
    <link href="https://yomxxx.com/posts/2026-06-08-long-horizon-agent-memory-architecture-evolution-long-form" />
    <updated>2026-06-08T00:00:00.000Z</updated>
    <published>2026-06-08T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>当 agent 要连续跑几百步，靠语义检索拼上下文的老办法就崩了——决策轨迹被打碎、错误痕迹混进正确记忆。2026 年 6 月一批 arXiv 论文给出了同一个答案：把记忆当成可编程的执行状态来管理。本文串起 RAMPART、MAGE、DeltaMem 拆解这场范式转移。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-08-mem0-agent-long-term-memory-workshop</id>
    <title>实战工坊：用 Mem0 给 AI Agent 装上「记得住」的长期记忆</title>
    <link href="https://yomxxx.com/posts/2026-06-08-mem0-agent-long-term-memory-workshop" />
    <updated>2026-06-08T00:00:00.000Z</updated>
    <published>2026-06-08T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>LLM 天生健忘，关掉对话就忘光。Mem0 提供一个即插即用的记忆层，自动提取、存储、检索用户记忆。本文带你从 pip install 跑通最小示例，再接进真实 Agent 对话循环，并讲清它如何决定记什么、改什么、忘什么。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-08-skillguard-agent-skills-permission-framework-paper</id>
    <title>论文速读：SkillGuard 给 Agent Skills 套上权限沙箱</title>
    <link href="https://yomxxx.com/posts/2026-06-08-skillguard-agent-skills-permission-framework-paper" />
    <updated>2026-06-08T00:00:00.000Z</updated>
    <published>2026-06-08T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Agent Skills 让 agent 能自动加载可复用的指令和脚本，但一个被污染的 skill 能注入什么、运行时会干什么，几乎没人管。SkillGuard 把 skill 当成「带权限的可执行制品」治理，把上下文注入攻击成功率从 32% 压到 23%。本文精读其威胁模型与设计。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-08-ai-agent-observability-tools-comparison-tools</id>
    <title>工具速评：6 款 AI Agent 可观测性平台横评（2026 年 6 月）</title>
    <link href="https://yomxxx.com/posts/2026-06-08-ai-agent-observability-tools-comparison-tools" />
    <updated>2026-06-08T00:00:00.000Z</updated>
    <published>2026-06-08T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Agent 上了生产，你能看见它每一步的推理、工具调用和烧了多少钱吗？本文横评 Langfuse、LangSmith、Phoenix、AgentOps、Helicone、Braintrust 六款可观测性平台，从 tracing、评测、自托管到 OpenTelemetry 支持逐一对比，并给出按场景的选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-07-ai-weekly-2026-06-01-to-06-07</id>
    <title>AI 周报 2026-06-01 ~ 06-07：端侧 Agent 上位、Context Rot 给百万 token 降温、SDD 工具爆发</title>
    <link href="https://yomxxx.com/posts/2026-06-07-ai-weekly-2026-06-01-to-06-07" />
    <updated>2026-06-07T00:00:00.000Z</updated>
    <published>2026-06-07T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>本周 AI 圈五件大事：Microsoft Build 2026 推出 Scout 编码 agent，端侧 agent 从概念走向工程，Chroma 的 Context Rot 研究给百万 token 竞赛泼冷水，规格驱动开发工具集中爆发，Stanford AI Index 2026 发布。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-07-context-rot-long-context-degradation-coding-agent-long-form</id>
    <title>深度长文：Context Rot——百万 token 上下文为什么会「越喂越笨」</title>
    <link href="https://yomxxx.com/posts/2026-06-07-context-rot-long-context-degradation-coding-agent-long-form" />
    <updated>2026-06-07T00:00:00.000Z</updated>
    <published>2026-06-07T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>模型厂商比拼百万 token 上下文，但 Chroma 的研究揭示残酷真相：上下文越长，模型表现越不均匀地退化，连简单任务都会翻车。本文剖析 Context Rot 的机制、它对 coding agent 的杀伤，以及一线团队的工程应对。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-07-dualtune-on-device-agentic-finetuning-paper</id>
    <title>论文速读：DualTune 把工具调用拆成两半，让端侧小模型也会用工具</title>
    <link href="https://yomxxx.com/posts/2026-06-07-dualtune-on-device-agentic-finetuning-paper" />
    <updated>2026-06-07T00:00:00.000Z</updated>
    <published>2026-06-07T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>本地小模型用不好工具，卡在「选错工具」和「填错参数」两个环节。DualTune 把工具调用解耦成工具选择和参数生成两个子任务，各训一个 LoRA，让端侧 agent 的工具调用准确率大幅跃升。本文精读其方法与工程意义。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-07-rag-anything-multimodal-rag-workshop</id>
    <title>实战工坊：用 RAG-Anything 给 RAG 装上「看图读表」的眼睛</title>
    <link href="https://yomxxx.com/posts/2026-06-07-rag-anything-multimodal-rag-workshop" />
    <updated>2026-06-07T00:00:00.000Z</updated>
    <published>2026-06-07T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>传统 RAG 只会读纯文本，遇到 PDF 里的图表、公式、扫描表格就抓瞎。RAG-Anything 把文档解析、多模态理解和知识图谱检索打包成一个框架，本文带你从安装到跑通一个能检索图片和表格的多模态问答系统。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-07-spec-driven-development-tools-comparison-tools</id>
    <title>工具速评：四款规格驱动开发工具横评——告别 Vibe Coding</title>
    <link href="https://yomxxx.com/posts/2026-06-07-spec-driven-development-tools-comparison-tools" />
    <updated>2026-06-07T00:00:00.000Z</updated>
    <published>2026-06-07T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Vibe coding 爽一时，维护火葬场。2026 年规格驱动开发（SDD）成了让 AI 写出可维护代码的主流方法。本文横评 AWS Kiro、GitHub Spec Kit、Tessl、OpenSpec 四款工具，帮你选对适合团队的那一款。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-06-ai-worm-byo-llm-autonomous-malware-defense-long-form</id>
    <title>会推理的蠕虫：自带开源模型的自主恶意软件，撕开了哪道防线</title>
    <link href="https://yomxxx.com/posts/2026-06-06-ai-worm-byo-llm-autonomous-malware-defense-long-form" />
    <updated>2026-06-06T00:00:00.000Z</updated>
    <published>2026-06-06T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>多伦多大学团队展示了一种自复制蠕虫——它携带一份开源 LLM，寄生宿主算力做推理，为每台机器现编攻击策略。本文从防御与治理视角剖析：为什么不需要前沿模型就够危险，以及它对「只管控大模型」的安全叙事意味着什么。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-06-gemma-4-12b-local-multimodal-deployment-workshop</id>
    <title>实战工坊：把 Gemma 4 12B 多模态模型塞进 16GB 笔记本</title>
    <link href="https://yomxxx.com/posts/2026-06-06-gemma-4-12b-local-multimodal-deployment-workshop" />
    <updated>2026-06-06T00:00:00.000Z</updated>
    <published>2026-06-06T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Google 新开源的 Gemma 4 12B 用 encoder-free 统一架构，让音频波形和图像直接进 LLM 主干。本文手把手在 16GB 显存/统一内存的本地机器上部署它，跑通文本、图像、音频三种输入，并接好 agentic 工具调用。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-06-qwen3-6-27b-dual-3090-llamacpp-vllm-workshop</id>
    <title>实战工坊：双 3090 跑 Qwen3.6-27B，llama.cpp 与 vLLM 全流程调优</title>
    <link href="https://yomxxx.com/posts/2026-06-06-qwen3-6-27b-dual-3090-llamacpp-vllm-workshop" />
    <updated>2026-06-06T00:00:00.000Z</updated>
    <published>2026-06-06T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>把 Qwen3.6-27B 塞进两张 3090，做成一个能在 llama.cpp 与 vLLM、不同量化间热切换的 OpenAI 兼容端点。本文给全套 flag、张量并行配置、MTP 投机解码的 per-position 接受率解读，以及一路踩过的坑。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-06-microsoft-build-2026-scout-mai-thinking-tools</id>
    <title>工具速评：Microsoft Build 2026 一口气端出 Scout、MAI-Thinking-1 与 Solara</title>
    <link href="https://yomxxx.com/posts/2026-06-06-microsoft-build-2026-scout-mai-thinking-tools" />
    <updated>2026-06-06T00:00:00.000Z</updated>
    <published>2026-06-06T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Build 2026 上 Microsoft 交出了首个自研推理模型 MAI-Thinking-1、基于 OpenClaw 的个人 Agent Scout，外加 Project Solara 硬件与编码/语音/图像模型更新。本文逐个拆开看它们对开发者的实际价值，以及 Microsoft 摆脱「套壳」的这步棋。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-06-siri-self-internalizing-rl-intrinsic-skills-paper</id>
    <title>论文速读：SIRI 让 Agent 把技能「内化」进权重，而非外挂检索</title>
    <link href="https://yomxxx.com/posts/2026-06-06-siri-self-internalizing-rl-intrinsic-skills-paper" />
    <updated>2026-06-06T00:00:00.000Z</updated>
    <published>2026-06-06T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>现有 Agent 技能方法要么训练时靠外部生成器，要么推理时持续检索技能库，徒增上下文与延迟。SIRI 提出三阶段自内化框架，让策略自己挖掘、验证、吸收技能，推理时零检索就把 ALFWorld、WebShop 成绩往上顶。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-05-entropy-mechanism-rl-reasoning-collapse-paper</id>
    <title>论文速读：RL 推理为什么扩不动——熵崩溃机制与 Clip-Cov/KL-Cov</title>
    <link href="https://yomxxx.com/posts/2026-06-05-entropy-mechanism-rl-reasoning-collapse-paper" />
    <updated>2026-06-05T00:00:00.000Z</updated>
    <published>2026-06-05T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>用 RL 训练推理模型时，策略熵总在早期急剧塌缩，把探索空间烧光、性能提前触顶。一篇论文把熵的变化归因到协方差，并给出 Clip-Cov、KL-Cov 两个简单药方，让策略逃出熵崩溃。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-05-malt-multi-agent-llm-training-reasoning-paper</id>
    <title>论文速读：MALT——让三个 LLM 像团队一样被「联合训练」去推理</title>
    <link href="https://yomxxx.com/posts/2026-06-05-malt-multi-agent-llm-training-reasoning-paper" />
    <updated>2026-06-05T00:00:00.000Z</updated>
    <published>2026-06-05T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>多智能体不该只在推理时拿提示词拼起来，更该被联合训练。MALT 让生成者、检验者、精炼者三个 LLM 分工协作，用轨迹扩展造数据、用信用分配回传功劳，在 MATH 等基准上把同基线模型推高一截。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-05-mistral-full-stack-ai-now-summit-long-form</id>
    <title>深度长文：Mistral 的全栈豪赌——从模型实验室到工业 AI 伙伴</title>
    <link href="https://yomxxx.com/posts/2026-06-05-mistral-full-stack-ai-now-summit-long-form" />
    <updated>2026-06-05T00:00:00.000Z</updated>
    <published>2026-06-05T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>AI NOW Summit 上，Mistral 把 Le Chat 并成 Vibe、押注工业 AI、宣布自建数据中心。这不是三条孤立新闻，而是一家模型公司主动撞穿「只做模型」天花板的全栈转型。本文拆解它的逻辑、底牌与风险。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-05-nemotron-3-ultra-latentmoe-coding-tools</id>
    <title>工具速评：Nemotron 3 Ultra——NVIDIA 用 LatentMoE 押注「又快又开」的编程模型</title>
    <link href="https://yomxxx.com/posts/2026-06-05-nemotron-3-ultra-latentmoe-coding-tools" />
    <updated>2026-06-05T00:00:00.000Z</updated>
    <published>2026-06-05T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>NVIDIA 在 Computex 发布 550B 的 Nemotron 3 Ultra，成为美国开源权重最强模型。它的卖点不是绝对智商，而是 LatentMoE + Hybrid Mamba + NVFP4 堆出的速度与编程性价比。本文实测式拆解它的定位与取舍。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-05-vllm-kv-cache-quantization-fp8-deployment-workshop</id>
    <title>实战工坊：用 vLLM 压扁 KV Cache——FP8、TurboQuant 与华为 KVarN</title>
    <link href="https://yomxxx.com/posts/2026-06-05-vllm-kv-cache-quantization-fp8-deployment-workshop" />
    <updated>2026-06-05T00:00:00.000Z</updated>
    <published>2026-06-05T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>长上下文推理真正的内存墙不是权重，而是 KV Cache。本文手把手在 vLLM 里开启 FP8 KV 量化、做校准、用 NIAH 验证长上下文不掉点，并对比 TurboQuant、KVarN 等激进压缩方案的取舍。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-04-agent-lifespan-engineering-aging-bench-paper</id>
    <title>论文速读：你的 Agent 也在变老——长期部署的退化与 AgingBench</title>
    <link href="https://yomxxx.com/posts/2026-06-04-agent-lifespan-engineering-aging-bench-paper" />
    <updated>2026-06-04T00:00:00.000Z</updated>
    <published>2026-06-04T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>权重冻结不等于状态冻结。一篇论文指出长期运行的 AI Agent 会随时间退化，归纳出压缩、干扰、修订、维护四种「老化」机制，并提出 AgingBench 纵向基准。本文拆解它的框架与工程应对。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-04-ai-research-agents-narrow-scientific-exploration-paper</id>
    <title>论文速读：AI 研究 Agent 正在让科学探索变窄</title>
    <link href="https://yomxxx.com/posts/2026-06-04-ai-research-agents-narrow-scientific-exploration-paper" />
    <updated>2026-06-04T00:00:00.000Z</updated>
    <published>2026-06-04T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>一篇用 37802 个 AI 生成科研想法做对照的论文发现：AI 研究 Agent 的点子高度同质、扎堆现有文献、更像低引用论文。本文拆解它的实验设计、四个一致结论，以及对「AI 自动做科研」叙事的冲击。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-04-llm-inference-engine-comparison-2026-tools</id>
    <title>2026 LLM 推理引擎横评：vLLM、SGLang、TensorRT-LLM 到底选谁</title>
    <link href="https://yomxxx.com/posts/2026-06-04-llm-inference-engine-comparison-2026-tools" />
    <updated>2026-06-04T00:00:00.000Z</updated>
    <published>2026-06-04T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>TGI 退场后，生产级 LLM 推理只剩三个主角加两个变量。本文用 H100/H200 实测数据横评 vLLM、SGLang、TensorRT-LLM、Modular MAX、llama.cpp，从吞吐、延迟、硬件覆盖、易用性给出选型决策树。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-04-ai-inference-economics-cost-rationing-long-form</id>
    <title>Token 越来越便宜，账单却越来越贵：AI 推理经济学的拐点</title>
    <link href="https://yomxxx.com/posts/2026-06-04-ai-inference-economics-cost-rationing-long-form" />
    <updated>2026-06-04T00:00:00.000Z</updated>
    <published>2026-06-04T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年企业开始给 AI 限额配给。Token 单价两年降了上百倍，总账单却在暴涨。本文用 Jevons 悖论、agentic 工作流的 token 放大、推理占比从 40% 到 85% 的迁移，拆解这场推理经济学拐点背后的结构性原因与应对。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-04-mellum2-jetbrains-moe-coding-model-workshop</id>
    <title>Mellum2 实战：把 JetBrains 的 12B MoE 代码模型跑在自己机器上</title>
    <link href="https://yomxxx.com/posts/2026-06-04-mellum2-jetbrains-moe-coding-model-workshop" />
    <updated>2026-06-04T00:00:00.000Z</updated>
    <published>2026-06-04T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>JetBrains 开源了 Mellum2——12B 总参、2.5B 激活的 MoE 代码模型，Apache 2.0、单卡 H100 可跑。本文给出 vLLM 部署、FIM 代码补全、工具调用 Agent、IDE 接入的完整可运行代码与避坑清单。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-03-agentops-agent-system-operations-paper</id>
    <title>AgentOps 论文速读：给 Agent 系统建一套「监控—定位—修复」运维体系</title>
    <link href="https://yomxxx.com/posts/2026-06-03-agentops-agent-system-operations-paper" />
    <updated>2026-06-03T00:00:00.000Z</updated>
    <published>2026-06-03T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Agent 系统会出故障，但业界几乎没有成体系的运维方法论。AgentOps 这篇综述把 Agent 异常分成内部与交互两类，提出监控、检测、根因定位、修复四阶段框架。本文拆解它的分类法与工程落地路径。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-03-minimax-m3-million-context-coding-workshop</id>
    <title>MiniMax M3 实战：1M 上下文 + 稀疏注意力的开源编码模型怎么用</title>
    <link href="https://yomxxx.com/posts/2026-06-03-minimax-m3-million-context-coding-workshop" />
    <updated>2026-06-03T00:00:00.000Z</updated>
    <published>2026-06-03T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MiniMax M3 用 MSA 稀疏注意力把上下文拉到 100 万 token，原生多模态还支持桌面操作。本文给出 API 调用、整库代码理解、agentic 工具循环、自部署的完整代码与避坑清单。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-03-open-weight-llm-comparison-june-2026-tools</id>
    <title>2026 年 6 月开源权重大模型横评：Nemotron 3 Ultra、MiniMax M3、Kimi K2.6 怎么选</title>
    <link href="https://yomxxx.com/posts/2026-06-03-open-weight-llm-comparison-june-2026-tools" />
    <updated>2026-06-03T00:00:00.000Z</updated>
    <published>2026-06-03T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>一周内 Nemotron 3 Ultra 与 MiniMax M3 接连开源，开源权重模型集体逼近闭源前沿。本文横向对比五款主力开源模型的参数、上下文、智能指数、编码与成本，给出自部署与 API 选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-03-local-ai-mass-adoption-odysseus-long-form</id>
    <title>本地 AI 的拐点不是模型，是分发：从 PewDiePie 的 Odysseus 说起</title>
    <link href="https://yomxxx.com/posts/2026-06-03-local-ai-mass-adoption-odysseus-long-form" />
    <updated>2026-06-03T00:00:00.000Z</updated>
    <published>2026-06-03T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>本地 AI 多年困在开发者小圈子，直到 PewDiePie 把自托管 AI 工作区 Odysseus 推给 1.1 亿订阅者。本文论证：本地 AI 的临界点从来不是模型能力，而是一键化、名人分发与消费级硬件三者的叠加。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-03-terminal-bench-2-harbor-evaluate-coding-agent-workshop</id>
    <title>用 Harbor 跑 Terminal-Bench 2.0：给你的 coding agent 做一次真实评测</title>
    <link href="https://yomxxx.com/posts/2026-06-03-terminal-bench-2-harbor-evaluate-coding-agent-workshop" />
    <updated>2026-06-03T00:00:00.000Z</updated>
    <published>2026-06-03T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>公开榜单上 53% 的 agent，放进你的项目可能只有 20%。本文用 Harbor 框架跑 Terminal-Bench 2.0，从安装、接入自己的 agent、解读失败轨迹到编写贴合业务的自定义任务，给出完整可运行流程。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-01-ai-prototyping-speed-paradigm-shift-long-form</id>
    <title>原型速度的范式转变：AI 编码助手如何重塑工程文化</title>
    <link href="https://yomxxx.com/posts/2026-06-01-ai-prototyping-speed-paradigm-shift-long-form" />
    <updated>2026-06-01T00:00:00.000Z</updated>
    <published>2026-06-01T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>当 Codex 学会绕过 sudo 限制完成任务、当 5 倍迭代成为日常，工程团队的决策结构、代码评审节奏、人才结构都在悄悄重写。本文从 HN 热议的两个工程案例切入，剖析这场变化的深层动力与团队应对策略。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-01-markitdown-document-to-llm-ready-markdown-workshop</id>
    <title>markitdown 实战：把任意文档转成 LLM-Ready Markdown 的数据预处理流水线</title>
    <link href="https://yomxxx.com/posts/2026-06-01-markitdown-document-to-llm-ready-markdown-workshop" />
    <updated>2026-06-01T00:00:00.000Z</updated>
    <published>2026-06-01T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>微软 markitdown 把 Office、PDF、图片、音频统一转成 LLM 友好的 Markdown，单条命令上手。本文给出 RAG 索引前预处理、批量 PPT 提取、与 VLM 解析对比、生产管道集成的完整代码与避坑指南。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-01-mcmpo-meta-cognitive-memory-policy-paper</id>
    <title>MCMPO 论文速读：让 Agent 学会「该不该记、该不该忘」的元认知策略</title>
    <link href="https://yomxxx.com/posts/2026-06-01-mcmpo-meta-cognitive-memory-policy-paper" />
    <updated>2026-06-01T00:00:00.000Z</updated>
    <published>2026-06-01T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MCMPO 把记忆读写动作纳入强化学习策略空间，模型自己学会判断哪条经验值得写入长期记忆。本文拆解算法核心、与 Memex-RL、MemoRAI 的差异，以及在 100 步长程任务上的实测增益。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-01-voxcpm-tokenizer-free-tts-workshop</id>
    <title>VoxCPM 实战：抛弃 token 的多语种 TTS 与零样本声音克隆</title>
    <link href="https://yomxxx.com/posts/2026-06-01-voxcpm-tokenizer-free-tts-workshop" />
    <updated>2026-06-01T00:00:00.000Z</updated>
    <published>2026-06-01T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>VoxCPM 用连续语音表征替代离散 token，绕开传统 TTS 的码本瓶颈。本文拆解 tokenizer-free 架构、给出本地推理、声音克隆、创意声音设计三段实战代码，并对比 F5-TTS 与 XTTS 在中英混读上的差异。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-06-01-self-hosted-ai-workspace-comparison-tools</id>
    <title>自托管 AI 工作区横评 2026：Odysseus / Open WebUI / LibreChat / Cherry Studio / Jan</title>
    <link href="https://yomxxx.com/posts/2026-06-01-self-hosted-ai-workspace-comparison-tools" />
    <updated>2026-06-01T00:00:00.000Z</updated>
    <published>2026-06-01T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>数据合规要求催生「自托管 AI 工作区」需求井喷。本文实测 5 款主流方案在多模型路由、RAG、Agent、插件生态、显存占用上的差异，给出按团队规模和场景的选型表。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-30-ai-agent-framework-star-race-tools</id>
    <title>2026 AI Agent 框架星标竞赛：OpenClaw、Hermes 与传统框架的格局</title>
    <link href="https://yomxxx.com/posts/2026-05-30-ai-agent-framework-star-race-tools" />
    <updated>2026-05-30T00:00:00.000Z</updated>
    <published>2026-05-30T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>横评 2026 年 AI Agent 框架格局：OpenClaw 登顶 GitHub 史上最多星标，Hermes Agent 增速更猛，LangGraph 与 Microsoft Agent Framework 守住企业生产，附按场景选型建议与安全风险提醒。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-30-ai-attack-defense-imbalance-long-form</id>
    <title>AI 攻防失衡：当 time-to-exploit 转负，防御方如何用 AI 扳回平衡</title>
    <link href="https://yomxxx.com/posts/2026-05-30-ai-attack-defense-imbalance-long-form" />
    <updated>2026-05-30T00:00:00.000Z</updated>
    <published>2026-05-30T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年 time-to-exploit 转负，利用代码先于补丁出现，首个 AI 构建的 0-day 落地。本文从架构视角剖析攻防非对称性如何被 AI 放大，以及防御方为何要用 agent harness 对抗 agent 攻击。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-30-lightrag-graph-rag-workshop</id>
    <title>LightRAG 实战：用知识图谱给 RAG 装上多跳推理大脑</title>
    <link href="https://yomxxx.com/posts/2026-05-30-lightrag-graph-rag-workshop" />
    <updated>2026-05-30T00:00:00.000Z</updated>
    <published>2026-05-30T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>LightRAG 把图结构引入 RAG，文档分块后抽取实体与关系建知识图谱，检索时结合低层与高层双层检索，再融合图上下文与向量结果。本文从原理到 Ollama 本地部署、Neo4j 图存储、四种检索模式对比，给出完整实战代码。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-30-mobilegym-mobile-gui-agent-simulation-paper</id>
    <title>MobileGym 论文速读：可验证、高并行的手机 GUI Agent 仿真平台</title>
    <link href="https://yomxxx.com/posts/2026-05-30-mobilegym-mobile-gui-agent-simulation-paper" />
    <updated>2026-05-30T00:00:00.000Z</updated>
    <published>2026-05-30T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MobileGym 用浏览器内确定性移动环境加结构化状态验证，替代不可靠的 VLM 裁判，并支持单机 96 并行 GRPO 在线训练，10 步把 Qwen3-VL-4B 成功率从 9.4% 提到 22.2%。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-30-nsi-neuro-symbolic-skill-induction-paper</id>
    <title>把轨迹提升为逻辑：NSI 用神经符号学习归纳可执行技能</title>
    <link href="https://yomxxx.com/posts/2026-05-30-nsi-neuro-symbolic-skill-induction-paper" />
    <updated>2026-05-30T00:00:00.000Z</updated>
    <published>2026-05-30T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>论文速读 NSI（Neuro-Symbolic Skill Induction）：把 agent 的交互轨迹提升为带控制流的逻辑程序，用反思式规划在部署时自我修复技能图，在 ALFWorld、WebShop、TextCraft 上超越 ReAct、Reflexion、AWM 等基线。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-29-basis-single-rollout-advantage-llm-reasoning</id>
    <title>BASIS 论文精读：单样本 RL 也能高效训练推理型 LLM</title>
    <link href="https://yomxxx.com/posts/2026-05-29-basis-single-rollout-advantage-llm-reasoning" />
    <updated>2026-05-29T00:00:00.000Z</updated>
    <published>2026-05-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>BASIS 利用 batch 内跨 prompt 信息共享，让单样本 RL 的优势估计精度超过 8 样本 GRPO，将 RLVR 训练采样开销降低 8 倍且几乎不损失性能。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-29-bumblebee-perplexity-supply-chain-scanner</id>
    <title>Bumblebee 速评：Perplexity 开源的 AI 供应链安全扫描器值不值得用</title>
    <link href="https://yomxxx.com/posts/2026-05-29-bumblebee-perplexity-supply-chain-scanner" />
    <updated>2026-05-29T00:00:00.000Z</updated>
    <published>2026-05-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Bumblebee 是 Perplexity AI 开源的零依赖 Go 工具，扫描 npm/PyPI/Go/MCP 配置中的恶意依赖。本文从设计原理、生态覆盖、MCP 安全扫描到实操体验，给出选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-29-anthropic-mythos-public-release-cybersecurity-ai</id>
    <title>Anthropic Mythos 向公众开放：当 AI 找漏洞比人类更快更准</title>
    <link href="https://yomxxx.com/posts/2026-05-29-anthropic-mythos-public-release-cybersecurity-ai" />
    <updated>2026-05-29T00:00:00.000Z</updated>
    <published>2026-05-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 宣布将 Mythos 级网络安全能力向所有客户开放，同步发布 Opus 4.8。Project Glasswing 一个月发现万级漏洞，90.6% 真阳性率，AI 攻防格局正在被重写。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-29-nanochat-karpathy-full-stack-llm-training</id>
    <title>nanochat 实战：用 $100 从零训练一个能对话的 ChatGPT 克隆</title>
    <link href="https://yomxxx.com/posts/2026-05-29-nanochat-karpathy-full-stack-llm-training" />
    <updated>2026-05-29T00:00:00.000Z</updated>
    <published>2026-05-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>nanochat 是 Andrej Karpathy 开源的极简全栈 LLM 训练流水线，约 8000 行 PyTorch 代码覆盖 tokenization 到 Chat UI 全流程。本文从 --depth 旋钮到分布式预训练，手把手走通 $100 训练一个能对话的模型。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-29-pi-mono-typescript-ai-agent-toolkit</id>
    <title>pi-mono 实战：用 TypeScript 搭建统一多模型 AI Agent 工具箱</title>
    <link href="https://yomxxx.com/posts/2026-05-29-pi-mono-typescript-ai-agent-toolkit" />
    <updated>2026-05-29T00:00:00.000Z</updated>
    <published>2026-05-29T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>pi-mono 是一个 GitHub 8.6K stars 的 TypeScript 全栈 AI Agent 工具箱，用 7 个内聚包覆盖统一 LLM API、编码 Agent CLI、终端和 Web UI、Slack Bot、vLLM 部署管理。本文从架构到实战，手把手带你跑通全流程。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-28-badhost-cve-2026-48710-ai-agent-security-long-form</id>
    <title>BadHost 深度分析：一个 Host Header 如何击穿百万 AI Agent 的认证防线</title>
    <link href="https://yomxxx.com/posts/2026-05-28-badhost-cve-2026-48710-ai-agent-security-long-form" />
    <updated>2026-05-28T00:00:00.000Z</updated>
    <published>2026-05-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>CVE-2026-48710（BadHost）利用 Starlette 的 Host header 解析缺陷伪造 request.url.path，绕过 FastAPI、vLLM、LiteLLM、MCP Server 等 AI Agent 基础设施的路径认证中间件。本文从漏洞原理到修复方案完整拆解。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-28-claude-context-semantic-search-mcp-workshop</id>
    <title>claude-context 实战：用语义搜索 MCP 让编码 Agent 精准定位百万行代码</title>
    <link href="https://yomxxx.com/posts/2026-05-28-claude-context-semantic-search-mcp-workshop" />
    <updated>2026-05-28T00:00:00.000Z</updated>
    <published>2026-05-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>claude-context 是 Zilliz 开源的 MCP 插件，用 BM25 + 稠密向量混合检索为 Claude Code 等编码 Agent 添加语义代码搜索能力。本文从安装到 monorepo 实战，覆盖架构、索引策略、成本和踩坑。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-28-openhuman-desktop-ai-agent-tools</id>
    <title>OpenHuman 速评：27K Star 的 Rust 桌面 AI Agent 能替代 Claude Desktop 吗？</title>
    <link href="https://yomxxx.com/posts/2026-05-28-openhuman-desktop-ai-agent-tools" />
    <updated>2026-05-28T00:00:00.000Z</updated>
    <published>2026-05-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenHuman 是一个基于 Rust + Tauri 的开源桌面 AI Agent，主打 Memory Tree 上下文管理、TokenJuice 成本压缩和 118+ 集成。本文从安装体验、核心功能实测到竞品对比，给出选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-28-dmpo-distribution-matching-diverse-reasoning-paper</id>
    <title>DMPO 论文速读：用前向 KL 散度解决 GRPO 推理的模式崩塌</title>
    <link href="https://yomxxx.com/posts/2026-05-28-dmpo-distribution-matching-diverse-reasoning-paper" />
    <updated>2026-05-28T00:00:00.000Z</updated>
    <published>2026-05-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>DMPO 用前向 KL 散度替代反向 KL，在 group 内做分布匹配解决 GRPO 推理训练的模式崩塌问题，NP-Bench 上取得 9-12% 相对提升。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-28-skillopt-self-evolving-agent-skills-paper</id>
    <title>SkillOpt 论文速读：微软把梯度下降搬到了 skill.md，52 场全胜</title>
    <link href="https://yomxxx.com/posts/2026-05-28-skillopt-self-evolving-agent-skills-paper" />
    <updated>2026-05-28T00:00:00.000Z</updated>
    <published>2026-05-28T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>微软 SkillOpt 把 skill 文档当可训练状态，结构化编辑加验证门控做 text-space 优化，52 个评测格全胜，部署只需一个 markdown。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-27-cursor-composer-2-5-build-in-parallel-workshop</id>
    <title>Cursor Composer 2.5 Build in Parallel 实战：把 IDE 当依赖图调度器</title>
    <link href="https://yomxxx.com/posts/2026-05-27-cursor-composer-2-5-build-in-parallel-workshop" />
    <updated>2026-05-27T00:00:00.000Z</updated>
    <published>2026-05-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Cursor Composer 2.5 在 5 月 18 日上线，配合 Build in Parallel 让 IDE 自动识别独立子任务、派遣 async subagent 并行执行。本文从启用到调度，覆盖 /multitask、依赖图、和 SWE-Bench 79.8% 实测对比。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-27-golongrl-long-context-rlvr-paper</id>
    <title>GoLongRL 速读：开源版长上下文 RLVR，30B 追平 235B 思维模型</title>
    <link href="https://yomxxx.com/posts/2026-05-27-golongrl-long-context-rlvr-paper" />
    <updated>2026-05-27T00:00:00.000Z</updated>
    <published>2026-05-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>GoLongRL 在 5 月 19 日上线 arXiv，提出能力导向的长上下文 RLVR 后训练方法。本文拆解 23K 数据集九大任务类型、TMN-Reweight 算法、以及让 Qwen3-30B-A3B 追平 DeepSeek-R1 的实测结果。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-27-memorai-adaptive-graph-memory-paper</id>
    <title>MemORAI 速读：图谱记忆 + 动态加权 PageRank，F1 从 16 拉到 56</title>
    <link href="https://yomxxx.com/posts/2026-05-27-memorai-adaptive-graph-memory-paper" />
    <updated>2026-05-27T00:00:00.000Z</updated>
    <published>2026-05-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MemORAI 在 5 月 2 日 arXiv 上线，把对话记忆问题升级为『选择性过滤 + 溯源图谱 + 查询自适应 PageRank』三件套。本文拆解三大组件、Dynamic Weighted PageRank 公式、以及 LOCOMO 提升 40 pp 的实测数据。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-27-realtime-voice-api-comparison-tools</id>
    <title>实时语音 Agent API 三家横评：GPT-Realtime-2 vs Gemini Live vs ElevenLabs</title>
    <link href="https://yomxxx.com/posts/2026-05-27-realtime-voice-api-comparison-tools" />
    <updated>2026-05-27T00:00:00.000Z</updated>
    <published>2026-05-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenAI 5 月 7 日上线 GPT-Realtime-2，Google Gemini 3.1 Flash Live 和 ElevenLabs Conversational AI 2.0 同期升级。本文从延迟、定价、推理能力、工具调用四个维度横评三家实时语音 API，给出按场景的选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-27-training-data-wall-synthetic-data-long-form</id>
    <title>2026 LLM 训练数据墙：合成数据是出路还是下一个泡沫</title>
    <link href="https://yomxxx.com/posts/2026-05-27-training-data-wall-synthetic-data-long-form" />
    <updated>2026-05-27T00:00:00.000Z</updated>
    <published>2026-05-27T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Epoch AI 预测 2026 年起优质人类公网文本将不够用，前沿模型纷纷转向合成数据。本文从数据墙的真假、合成数据的三种技术路径、Composer 2.5 的 25x 合成任务实证，剖析 2026 的训练数据格局。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-26-agentic-os-gemini-spark-long-form</id>
    <title>Agentic OS 时代来了：Gemini Spark 把 Agent 从『工具』推向『常驻服务』的范式转变</title>
    <link href="https://yomxxx.com/posts/2026-05-26-agentic-os-gemini-spark-long-form" />
    <updated>2026-05-26T00:00:00.000Z</updated>
    <published>2026-05-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Gemini Spark 是 Google I/O 2026 最重要的一张牌。本文从架构、隔离模型、用户面、安全护栏四个维度拆解它的设计选择，并对比 ChatGPT Agent、Claude Cowork 的路线分歧。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-26-memex-rl-indexed-experience-memory-paper</id>
    <title>Memex(RL) 论文速读：用『索引化经验记忆』把长程 Agent 成功率从 24% 拉到 85%</title>
    <link href="https://yomxxx.com/posts/2026-05-26-memex-rl-indexed-experience-memory-paper" />
    <updated>2026-05-26T00:00:00.000Z</updated>
    <published>2026-05-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Memex(RL) 提出『索引压缩 + RL 学习读写』把 LLM Agent 的工作上下文压缩 43% 而完整证据无损可回取，成功率从 24% 飙到 85.6%。本文拆它的索引机制、奖励设计和工程落地。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-26-orchestration-traces-rl-multi-agent-paper</id>
    <title>Orchestration Traces RL：用『编排迹』当统一原语训练多 Agent 系统的 5 月新论文</title>
    <link href="https://yomxxx.com/posts/2026-05-26-orchestration-traces-rl-multi-agent-paper" />
    <updated>2026-05-26T00:00:00.000Z</updated>
    <published>2026-05-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026-05-04 的 arXiv 论文提出『Orchestration Traces』把多 Agent 系统的 spawn/delegate/communicate/aggregate/stop 五件事统一成时间交互图，并用 8 类奖励函数做 RL 训练。本文拆解它的设计哲学和 8 类奖励家族。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-26-state-bench-agent-memory-tools</id>
    <title>STATE-Bench 实测：微软给 Agent 记忆系统下了一张 450 题考卷，主流方案谁能及格？</title>
    <link href="https://yomxxx.com/posts/2026-05-26-state-bench-agent-memory-tools" />
    <updated>2026-05-26T00:00:00.000Z</updated>
    <published>2026-05-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>微软 5 月 19 日开源 STATE-Bench——给 AI Agent 记忆系统的标准化考试。本文实测 Mem0、Letta、Zep、EverMemOS、原生 long-context 五种方案，分析得分差距和工程选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-26-windsurf-wave13-cascade-hooks-workshop</id>
    <title>Windsurf Wave 13 实战：并行 5 个 Agent + Cascade Hooks 把 IDE 当生产线</title>
    <link href="https://yomxxx.com/posts/2026-05-26-windsurf-wave13-cascade-hooks-workshop" />
    <updated>2026-05-26T00:00:00.000Z</updated>
    <published>2026-05-26T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Windsurf Wave 13 把 IDE 从单 Agent 模式升级到 Git worktree 隔离的多 Agent 工厂。本文从安装到落地，覆盖 SWE-1.5、Cascade Hooks、Arena Mode 的实战写法。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-25-ai-harness-from-prompt-to-agent-os-long-form</id>
    <title>AI Harness 革命：从 Prompt Engineering 到 Agent Operating Environment 的系统工程</title>
    <link href="https://yomxxx.com/posts/2026-05-25-ai-harness-from-prompt-to-agent-os-long-form" />
    <updated>2026-05-25T00:00:00.000Z</updated>
    <published>2026-05-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>DeepSeek 把 Jane Street 工程师挖来组『harness team』、Cursor 把 85% 算力花在 harness 而非模型本身、OpenClaw 半年 21 万星——AI harness 已经取代 prompt engineering，成为决定 Agent 实用性的关键基建。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-25-google-antigravity-2-comprehensive-review-tools</id>
    <title>Google Antigravity 2.0 全面评测：五端 Agent 开发平台到底香在哪、坑在哪</title>
    <link href="https://yomxxx.com/posts/2026-05-25-google-antigravity-2-comprehensive-review-tools" />
    <updated>2026-05-25T00:00:00.000Z</updated>
    <published>2026-05-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Google I/O 2026 上 Antigravity 2.0 全面重做：desktop IDE、CLI、SDK、Managed Agents、Enterprise Platform 五端齐发。本文实测一周，给出与 Cursor、Claude Code 的差异化对比和选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-25-short-mk-shorter-reasoning-chains-paper</id>
    <title>Short-m@k 论文速读：短推理链反而比长 CoT 更准？test-time compute 的反常识发现</title>
    <link href="https://yomxxx.com/posts/2026-05-25-short-mk-shorter-reasoning-chains-paper" />
    <updated>2026-05-25T00:00:00.000Z</updated>
    <published>2026-05-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv 2505.17813 用 short-m@k 颠覆『thinking 越长越好』直觉：取 K 条短推理多数投票，准确率超过单条长推理 7-12 个点且省 50% 推理成本。本文拆解算法、实验和工程化路径。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-25-marag-r1-multi-tool-agentic-retrieval-paper</id>
    <title>MARAG-R1 论文速读：用强化学习教 Agent 同时调度多个检索器，HotpotQA 提升 18 个点</title>
    <link href="https://yomxxx.com/posts/2026-05-25-marag-r1-multi-tool-agentic-retrieval-paper" />
    <updated>2026-05-25T00:00:00.000Z</updated>
    <published>2026-05-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MARAG-R1 把『检索』从单一向量搜索升级成多工具协同——同时调度 web search、向量库、知识图谱、SQL，端到端用 RL 学习调度策略。本文拆解奖励设计、训练 pipeline 与落地参考。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-25-mistral-workflows-enterprise-orchestration-workshop</id>
    <title>Mistral Workflows 实战：用 Temporal 引擎在 30 分钟把 AI 流程从 PoC 推到生产</title>
    <link href="https://yomxxx.com/posts/2026-05-25-mistral-workflows-enterprise-orchestration-workshop" />
    <updated>2026-05-25T00:00:00.000Z</updated>
    <published>2026-05-25T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Mistral 4 月底开源的 Workflows 把 Temporal durable execution 装进 AI 编排层。本文从一个发票审批 demo 出发，覆盖 Python workflow 写法、worker 部署、Le Chat 集成和容错策略。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-24-ai-weekly-2026-05-18-to-05-24</id>
    <title>AI 周报 2026-05-18 ~ 05-24：Gemini 3.5 Flash 抢 Pro 风头、RAMPART 把 Agent 安全做进 CI、OpenClaw 21 万星</title>
    <link href="https://yomxxx.com/posts/2026-05-24-ai-weekly-2026-05-18-to-05-24" />
    <updated>2026-05-24T00:00:00.000Z</updated>
    <published>2026-05-24T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>本周 Google I/O 2026 发布 Gemini 3.5 Flash，4x 速度且在 agentic 基准超越 3.1 Pro。Microsoft 开源 RAMPART/Clarity 把 agent 安全推进开发流程。OpenClaw 半年涨 23 倍至 21 万星。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-24-coding-agent-models-comparison-tools</id>
    <title>编码 Agent 模型三国杀：Gemini 3.5 Flash vs GPT-5.5 vs Claude Opus 4.7 实战横评</title>
    <link href="https://yomxxx.com/posts/2026-05-24-coding-agent-models-comparison-tools" />
    <updated>2026-05-24T00:00:00.000Z</updated>
    <published>2026-05-24T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Gemini 3.5 Flash（5/19）、GPT-5.5（4/23）、Claude Opus 4.7（4/16）三大旗舰编码模型集中发布。本文从基准、真实任务、上下文、价格、Agent 适配五个维度给出横评和选型矩阵。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-24-evermemos-self-organizing-memory-os-paper</id>
    <title>EverMemOS 论文速读：把 LLM Agent 的长期记忆做成『海马体级』的操作系统</title>
    <link href="https://yomxxx.com/posts/2026-05-24-evermemos-self-organizing-memory-os-paper" />
    <updated>2026-05-24T00:00:00.000Z</updated>
    <published>2026-05-24T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>EverMemOS 把 LLM Agent 记忆按 MemCell → MemScene → 重构式回忆三层组织，在 LoCoMo / LongMemEval 上拿到 SOTA。本文拆解三阶段流程、和 A-MEM/AgeMem 的差异，以及对工程化的启示。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-24-mastra-typescript-agent-workshop</id>
    <title>Mastra 实战：用 TypeScript 30 分钟搭一个带工具、记忆和工作流的生产级 Agent</title>
    <link href="https://yomxxx.com/posts/2026-05-24-mastra-typescript-agent-workshop" />
    <updated>2026-05-24T00:00:00.000Z</updated>
    <published>2026-05-24T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Mastra 是 Gatsby 团队推出的 TypeScript-first Agent 框架，22K+ 星、周下载量 30 万。本文从 createAgent / createTool / createWorkflow 到部署，给出一份完整的实战手册。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-24-self-hosted-ai-agent-decentralization-long-form</id>
    <title>自托管 AI Agent 的去中心化时刻：OpenClaw 半年 21 万星背后的范式迁移</title>
    <link href="https://yomxxx.com/posts/2026-05-24-self-hosted-ai-agent-decentralization-long-form" />
    <updated>2026-05-24T00:00:00.000Z</updated>
    <published>2026-05-24T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenClaw 从 9K 星暴涨到 210K 星，Dify / Activepieces / n8n + Ollama 等自托管栈集体起飞。本文从协议、成本、隐私、生态四个维度复盘自托管 AI Agent 时代的到来。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-23-ai-agent-security-testing-tools-comparison</id>
    <title>AI Agent 安全测试工具横评 2026：RAMPART、Garak、Promptfoo、DeepEval 怎么选</title>
    <link href="https://yomxxx.com/posts/2026-05-23-ai-agent-security-testing-tools-comparison" />
    <updated>2026-05-23T00:00:00.000Z</updated>
    <published>2026-05-23T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Microsoft RAMPART 5 月 20 日开源把 AI Agent 安全测试推到 pytest-native 时代。本文从 Agent 视角横向对比 RAMPART、NVIDIA Garak v0.14、Promptfoo（已加入 OpenAI）、DeepEval 四个主流框架，给出选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-23-langgraph-1-2-error-recovery-workshop</id>
    <title>LangGraph 1.2 实战：用 error_handler + per-node timeout 把 Agent 容错率拉到生产级</title>
    <link href="https://yomxxx.com/posts/2026-05-23-langgraph-1-2-error-recovery-workshop" />
    <updated>2026-05-23T00:00:00.000Z</updated>
    <published>2026-05-23T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>LangGraph 1.2 于 2026 年 5 月 11 日发布，新增节点级错误处理器、运行/空闲双超时、可恢复的优雅关闭和 DeltaChannel。本文给出从 add_node 到 RunControl 的完整生产级 Agent 容错实战。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-23-subq-ssa-subquadratic-attention-paper</id>
    <title>SubQ SSA 论文速读：第一个真正子二次方注意力做到 12M token，FlashAttention 快 52 倍</title>
    <link href="https://yomxxx.com/posts/2026-05-23-subq-ssa-subquadratic-attention-paper" />
    <updated>2026-05-23T00:00:00.000Z</updated>
    <published>2026-05-23T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Miami 初创 Subquadratic 5 月发布 SubQ 1M-Preview，首次给出 fully subquadratic sparse attention（SSA）的工程实现，1M token 上比 FA 快 52×，SWE-Bench 81.8。本文拆解 SSA 机制与争议。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-23-long-context-architecture-war-long-form</id>
    <title>长上下文架构之争：FlashAttention、子二次方、稀疏 KV、Mamba 谁能笑到最后</title>
    <link href="https://yomxxx.com/posts/2026-05-23-long-context-architecture-war-long-form" />
    <updated>2026-05-23T00:00:00.000Z</updated>
    <published>2026-05-23T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年 5 月长上下文出现四大流派分庭抗礼：FlashAttention 3 的暴力派、SubQ SSA 的子二次方派、ChunkKV+FibQuant 的稀疏 KV 派、Mamba/RWKV 的线性 SSM 派。本文从计算复杂度、精度、工程成本三个维度横向拆解，给出 2026 年选型决策树。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-23-world-action-models-embodied-ai-paper</id>
    <title>World Action Models 论文速读：VLA 的下一站，把&quot;预测世界&quot;和&quot;决定动作&quot;合二为一</title>
    <link href="https://yomxxx.com/posts/2026-05-23-world-action-models-embodied-ai-paper" />
    <updated>2026-05-23T00:00:00.000Z</updated>
    <published>2026-05-23T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>复旦+NUS 14 位作者 5/12 发表 WAMs 综述，首次系统定义&quot;世界动作模型&quot;新范式：具身 AI 联合建模 future state 和 action。本文拆解 cascaded vs joint 分类、数据来源、评测三维度与开放挑战。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-22-document-parsing-vlm-comparison-2026</id>
    <title>本地文档解析 VLM 横评 2026：Granite-Docling vs MinerU 2.5 vs Nougat vs olmOCR</title>
    <link href="https://yomxxx.com/posts/2026-05-22-document-parsing-vlm-comparison-2026" />
    <updated>2026-05-22T00:00:00.000Z</updated>
    <published>2026-05-22T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>RAG 时代文档解析的瓶颈不再是 OCR，而是 VLM 能不能在压缩参数的同时保住表格、公式、版面、阅读顺序。本文横评 2026 年最值得本地部署的 4 个开源文档解析模型——从 258M 的 IBM Granite-Docling 到 1.2B 的 MinerU 2.5。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-22-ai-coding-agent-sandbox-revolution-long-form</id>
    <title>AI 编码 Agent 的沙盒化革命：E2B、Daytona、Modal、Runtime 如何重构开发工作流</title>
    <link href="https://yomxxx.com/posts/2026-05-22-ai-coding-agent-sandbox-revolution-long-form" />
    <updated>2026-05-22T00:00:00.000Z</updated>
    <published>2026-05-22T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年 AI 编码 Agent 已经从单机 CLI 工具进化为团队级基础设施，沙盒成为决定可靠性、安全性、成本的关键层。本文深度剖析 E2B、Daytona、Modal、Runtime、Vercel Sandbox 五大平台的架构差异、冷启动数据、隔离边界与团队治理模型。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-22-forge-guardrails-8b-model-reliability-workshop</id>
    <title>Forge 实战：用 Guardrails 把 8B 模型 Agent 完成率从 53% 拉到 99%</title>
    <link href="https://yomxxx.com/posts/2026-05-22-forge-guardrails-8b-model-reliability-workshop" />
    <updated>2026-05-22T00:00:00.000Z</updated>
    <published>2026-05-22T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Forge 是 antoinezambelli 在 2026 年 5 月开源的自托管 LLM 工具调用框架，用结构化 Guardrails 把 Qwen3-8B、Llama-3.1-8B 这类小模型的 Agent 完成率从 53% 推到 99%。本文给出从安装到自定义钩子的完整代码示例。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-22-toto-2-time-series-foundation-model-paper</id>
    <title>Toto 2.0 论文精读：时间序列预测进入规模化时代</title>
    <link href="https://yomxxx.com/posts/2026-05-22-toto-2-time-series-foundation-model-paper" />
    <updated>2026-05-22T00:00:00.000Z</updated>
    <published>2026-05-22T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Datadog 在 2026 年 5 月发布的 Toto 2.0 是首个证明 scaling law 在时序预测上成立的开源基础模型族——4M 到 2.5B 五个尺寸，每一档都比下一档更准，在 BOOM/GIFT-Eval/TIME 三大 benchmark 通杀，Apache 2.0 全开源。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-22-local-video-indexing-gemma-whisperx-workshop</id>
    <title>在 5 年前 MacBook 上本地索引一年视频：Gemma 4 31B + WhisperX + ffmpeg 全流程</title>
    <link href="https://yomxxx.com/posts/2026-05-22-local-video-indexing-gemma-whisperx-workshop" />
    <updated>2026-05-22T00:00:00.000Z</updated>
    <published>2026-05-22T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>用 2021 款 M1 Max MacBook Pro 本地索引一整年视频：Gemma 4 31B 做视觉分析、WhisperX 多语种转写、insightface 人脸 embedding，月成本 $140 砍到 $22。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-21-agent-meltdowns-helpful-agents-unsafe-paper</id>
    <title>Agent Meltdowns 论文速读：64.7% 智能体在异常环境下做出不安全行为</title>
    <link href="https://yomxxx.com/posts/2026-05-21-agent-meltdowns-helpful-agents-unsafe-paper" />
    <updated>2026-05-21T00:00:00.000Z</updated>
    <published>2026-05-21T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv:2605.19149 揭示自治 AI Agent 的系统性缺陷——当外部环境出现错误时，64.7% 的执行链会出现不安全行为，根因是 RLHF 训练的乐于助人倾向。本文拆解实验设计、5 种崩溃模式与可工程化的缓解策略。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-21-ai-coding-supply-chain-security-vscode-extension</id>
    <title>3800 仓库泄露事件复盘：AI 编程工具的供应链安全已经失守</title>
    <link href="https://yomxxx.com/posts/2026-05-21-ai-coding-supply-chain-security-vscode-extension" />
    <updated>2026-05-21T00:00:00.000Z</updated>
    <published>2026-05-21T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>一个伪装成 AI Copilot 的 VSCode 扩展，借助 OAuth 滥用与 MCP 服务器投毒，泄露了 3800 个企业仓库。本文深度复盘事件链路，盘点 AI Coding 工具栈的 4 层供应链威胁，给出可落地的防御清单。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-21-codegraph-claude-code-knowledge-graph-workshop</id>
    <title>CodeGraph 实战：给 Claude Code 接入预索引代码知识图谱</title>
    <link href="https://yomxxx.com/posts/2026-05-21-codegraph-claude-code-knowledge-graph-workshop" />
    <updated>2026-05-21T00:00:00.000Z</updated>
    <published>2026-05-21T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>CodeGraph 用预索引的代码知识图谱为 Claude Code、Codex、Cursor 提供精准上下文，避免 Agent 在 100 万行代码库中盲目搜索。本文给出从索引构建到 MCP 接入的完整代码与性能对比数据。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-21-qwen3-7-max-agent-frontier-review</id>
    <title>Qwen3.7-Max 实测评测：阿里能在 Agent 赛道追上 Claude 吗</title>
    <link href="https://yomxxx.com/posts/2026-05-21-qwen3-7-max-agent-frontier-review" />
    <updated>2026-05-21T00:00:00.000Z</updated>
    <published>2026-05-21T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Qwen3.7-Max 是阿里通义千问最新旗舰，主打 Agent 能力。本文用 6 类 Agent 任务实测对比 Claude Sonnet 4.6、GPT-5、Gemini 2.6 Pro，给出工具调用准确率、长链推理深度、价格效率等真实数据。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-21-tokens-per-second-perception-reality-workshop</id>
    <title>tokens/s 实测：N tokens/s 到底意味着什么用户体验</title>
    <link href="https://yomxxx.com/posts/2026-05-21-tokens-per-second-perception-reality-workshop" />
    <updated>2026-05-21T00:00:00.000Z</updated>
    <published>2026-05-21T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>tokens/s 是 LLM 性能讨论里被滥用最严重的指标。本文从 TTFT、TPS、感知阈值三个维度做完整实测，附 Python 测试脚本与 8 家 API 的真实数据，教你正确解读这个数字。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-20-ai-disrupting-open-knowledge-infrastructure</id>
    <title>AI 正在瓦解开放知识网络：Wikipedia、Stack Overflow 与搜索引擎的生存危机</title>
    <link href="https://yomxxx.com/posts/2026-05-20-ai-disrupting-open-knowledge-infrastructure" />
    <updated>2026-05-20T00:00:00.000Z</updated>
    <published>2026-05-20T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Wikipedia 以 40:2 投票禁止 AI 生成内容，Stack Overflow 新问题量暴跌 78%，Google AI Overviews 导致首页 CTR 下降 58%。本文系统分析 AI 对开放知识基础设施的三重冲击，以及知识生态的未来走向。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-20-rag-anything-multimodal-knowledge-retrieval</id>
    <title>RAG-Anything 实战：用双知识图谱统一多模态文档检索</title>
    <link href="https://yomxxx.com/posts/2026-05-20-rag-anything-multimodal-knowledge-retrieval" />
    <updated>2026-05-20T00:00:00.000Z</updated>
    <published>2026-05-20T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>RAG-Anything 基于双知识图谱架构，将图片、表格、公式与文本上下文互联，通过跨模态图谱 + 文本语义图谱的混合检索，解决传统 chunk-based RAG 在多模态文档场景中的根本缺陷。本文给出从安装到多模态查询的完整代码。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-20-nvidia-star-elastic-inference-workshop</id>
    <title>NVIDIA Star Elastic 实战：一个 Checkpoint 切出 30B/23B/12B 三种推理模型</title>
    <link href="https://yomxxx.com/posts/2026-05-20-nvidia-star-elastic-inference-workshop" />
    <updated>2026-05-20T00:00:00.000Z</updated>
    <published>2026-05-20T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>NVIDIA Star Elastic 在一个 30B checkpoint 中嵌套 23B 和 12B 子模型，单次训练即可零样本提取三种规模变体。本文给出从 HuggingFace 下载到弹性预算推理的完整实操代码，附性能与显存对比数据。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-20-frontier-llm-comparison-may-2026</id>
    <title>2026 年 5 月前沿大模型横评：GPT-5.5 / Gemini 3.1 Pro / Claude Opus 4.7 / Mistral Large 128B</title>
    <link href="https://yomxxx.com/posts/2026-05-20-frontier-llm-comparison-may-2026" />
    <updated>2026-05-20T00:00:00.000Z</updated>
    <published>2026-05-20T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>GPT-5.5、Gemini 3.1 Pro、Claude Opus 4.7、Mistral Large 128B 四大前沿模型基准测试、定价与场景推荐全面对比，帮开发者快速选型</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-20-orthrus-dual-view-parallel-decoding-paper</id>
    <title>Orthrus 论文速读：双视图扩散架构实现 7.8x 无损并行解码加速</title>
    <link href="https://yomxxx.com/posts/2026-05-20-orthrus-dual-view-parallel-decoding-paper" />
    <updated>2026-05-20T00:00:00.000Z</updated>
    <published>2026-05-20T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv 2605.12825 提出 Orthrus，在冻结的自回归 LLM 上嫁接轻量扩散头，通过双视图共享 KV Cache 实现最高 7.8 倍无损加速。本文剖析其架构设计、共识机制、与投机解码方法的差异以及 Apple Silicon 原生推理的工程意义。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-19-claude-agent-skills-workshop</id>
    <title>Claude Agent Skills 实战：用 SKILL.md 构建跨 IDE 复用的智能体能力包</title>
    <link href="https://yomxxx.com/posts/2026-05-19-claude-agent-skills-workshop" />
    <updated>2026-05-19T00:00:00.000Z</updated>
    <published>2026-05-19T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 的 Agent Skills 框架把 AI 的专业能力封装成可分发的 SKILL.md 文件。本文从原理到落地，给出从零编写、本地测试到跨 IDE 共享的完整流程，并解读 Superpowers 等开源 skill 库的工程化经验。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-19-context-engineering-comprehensive-guide</id>
    <title>Context Engineering 全景剖析：当提示工程不再够用，下一代 AI 系统怎么搭</title>
    <link href="https://yomxxx.com/posts/2026-05-19-context-engineering-comprehensive-guide" />
    <updated>2026-05-19T00:00:00.000Z</updated>
    <published>2026-05-19T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年 AI 工程的主战场已经从 Prompt Engineering 迁移到 Context Engineering。本文系统梳理 Context Engineering 的核心方法论、四类落地手段（压缩/过滤/路由/记忆）、生产级架构模式，以及与 Agent、RAG 的边界关系。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-19-laprox-kv-cache-eviction-paper</id>
    <title>LaProx 论文速读：把 KV Cache 淘汰重铸成『输出感知矩阵近似』问题</title>
    <link href="https://yomxxx.com/posts/2026-05-19-laprox-kv-cache-eviction-paper" />
    <updated>2026-05-19T00:00:00.000Z</updated>
    <published>2026-05-19T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arxiv 2605.07234 提出 LaProx，将长上下文 LLM 推理的 KV Cache 淘汰问题重新表述为输出感知、逐层的矩阵乘法近似问题。本文剖析其核心数学动机、与 H2O/SnapKV 的差异以及在 128K 上下文下的实测收益。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-19-hybrid-rag-three-stage-cascade-workshop</id>
    <title>Hybrid RAG 三级级联实战：BM25 + Dense + Cross-Encoder Reranker 怎么搭</title>
    <link href="https://yomxxx.com/posts/2026-05-19-hybrid-rag-three-stage-cascade-workshop" />
    <updated>2026-05-19T00:00:00.000Z</updated>
    <published>2026-05-19T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>把朴素 RAG 升级到生产级，关键是三级级联：BM25 召回保关键词覆盖，密集向量召回保语义覆盖，Cross-Encoder Reranker 做精排。本文给出 Elasticsearch + Qdrant + bge-reranker-v2 的完整代码与调优清单。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-19-open-source-moe-models-comparison-2026</id>
    <title>2026 H1 开源 MoE 模型横评：DeepSeek V4 / Llama 4 / Qwen 3.5 / Mistral Large 3 怎么选</title>
    <link href="https://yomxxx.com/posts/2026-05-19-open-source-moe-models-comparison-2026" />
    <updated>2026-05-19T00:00:00.000Z</updated>
    <published>2026-05-19T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 上半年开源大模型阵营全面 MoE 化。本文横评四个主力 MoE 模型在编码、推理、长上下文、部署成本四个维度的表现，给出明确的选型决策树和不同算力档位的最佳搭配建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-18-causal-forcing-plus-plus-video-diffusion-distillation-paper</id>
    <title>Causal Forcing++ 论文速读：实时交互视频生成的少步蒸馏新范式</title>
    <link href="https://yomxxx.com/posts/2026-05-18-causal-forcing-plus-plus-video-diffusion-distillation-paper" />
    <updated>2026-05-18T00:00:00.000Z</updated>
    <published>2026-05-18T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>清华 THU-ML 团队继 Causal Forcing 之后又抛出 ++ 版本。论文解决了帧级少步蒸馏的关键难点，将实时交互视频生成的延迟和质量同时拉升到新水平。本文解读其核心技术贡献和工程启示。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-18-apwa-distributed-agentic-workflow-deep-dive</id>
    <title>APWA 深度解析：把 MapReduce 思想搬到 Agent 系统的分布式架构</title>
    <link href="https://yomxxx.com/posts/2026-05-18-apwa-distributed-agentic-workflow-deep-dive" />
    <updated>2026-05-18T00:00:00.000Z</updated>
    <published>2026-05-18T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>APWA 论文提出了一个面向大规模并行任务的多 agent 分布式架构。本文从架构演进、关键技术决策、容错与一致性、生产落地路径四个维度做系统性拆解，对比现有 LangGraph、CrewAI 的局限。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-18-agent-framework-comparison-2026</id>
    <title>2026 Agent 框架横评：8 大主流 SDK 的生产实测对比</title>
    <link href="https://yomxxx.com/posts/2026-05-18-agent-framework-comparison-2026" />
    <updated>2026-05-18T00:00:00.000Z</updated>
    <published>2026-05-18T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Claude Agent SDK、OpenAI Agents SDK、Pydantic AI、LangGraph、CrewAI、Microsoft Agent Framework、Google ADK、Mastra 八大主流 agent 框架横评。基于真实生产部署数据，给出每个框架的甜区、雷区和选型决策树。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-18-molmoact2-vla-so100-robot-workshop</id>
    <title>MolmoAct2 实战：用 5B 参数 VLA 模型驱动 SO100 机械臂</title>
    <link href="https://yomxxx.com/posts/2026-05-18-molmoact2-vla-so100-robot-workshop" />
    <updated>2026-05-18T00:00:00.000Z</updated>
    <published>2026-05-18T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MolmoAct2 是 Ai2 开源的 5B 参数动作推理模型（ARM），原生支持 SO100/SO101 低成本机械臂。本文从硬件接线到模型部署给出可复现的实战路径，并对比与 OpenVLA、π0 的关键差异。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-18-su-01-gold-medal-olympiad-reasoning-paper</id>
    <title>SU-01 论文速读：30B-A3B 模型如何用简单食谱拿下奥数金牌</title>
    <link href="https://yomxxx.com/posts/2026-05-18-su-01-gold-medal-olympiad-reasoning-paper" />
    <updated>2026-05-18T00:00:00.000Z</updated>
    <published>2026-05-18T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>上海 AI Lab 发布 SU-01：仅 30B 总参数、3B 激活的 MoE 模型，用 reverse-perplexity 课程 SFT + 两阶段 RL + 测试时扩展三步走，在 IMO 2025 和 USAMO 2026 上拿到金牌水平。本文解读其技术细节和工程启示。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-17-ai-weekly-2026-05-11-to-05-17</id>
    <title>AI 周报 2026-05-11 ~ 05-17：DeepSeek V4 开源、MS Agent Framework 1.0、Cola DLM、Gemini Omni 泄露</title>
    <link href="https://yomxxx.com/posts/2026-05-17-ai-weekly-2026-05-11-to-05-17" />
    <updated>2026-05-17T00:00:00.000Z</updated>
    <published>2026-05-17T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>本周 AI 最大事件是 DeepSeek V4 把 1M 原生上下文打到开源。同期 Microsoft Agent Framework 1.0 GA、Cola DLM 论文、Gemini Omni 泄露、MolmoAct2 机器人模型集中发布。5 条要点 + 选型影响。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-17-cola-dlm-continuous-latent-diffusion-language-model-paper</id>
    <title>Cola DLM 论文精读：连续潜空间扩散如何挑战自回归语言模型</title>
    <link href="https://yomxxx.com/posts/2026-05-17-cola-dlm-continuous-latent-diffusion-language-model-paper" />
    <updated>2026-05-17T00:00:00.000Z</updated>
    <published>2026-05-17T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv 2605.06548 提出 Cola DLM，把语言建模搬进连续潜空间，用层次化扩散替代自回归 next-token。本文拆解 Text VAE、全局语义先验、条件解码三阶段，并给出与 LLaDA、SEDD 的工程对比。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-17-deepseek-v4-mhc-hybrid-attention-architecture-deep-dive</id>
    <title>DeepSeek V4 架构深度解析：mHC + CSA/HCA 如何撑起原生 1M 上下文</title>
    <link href="https://yomxxx.com/posts/2026-05-17-deepseek-v4-mhc-hybrid-attention-architecture-deep-dive" />
    <updated>2026-05-17T00:00:00.000Z</updated>
    <published>2026-05-17T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>DeepSeek V4 5 月技术报告披露 1.6T Pro 与 284B Flash 两款 MoE 模型，原生 1M 上下文背后是 mHC 残差与 CSA/HCA 混合注意力。本文从代数稳定性角度拆解 V4 的三项核心创新。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-17-llm-observability-tools-comparison-2026</id>
    <title>LLM 可观测性工具横评 2026：Langfuse / LangSmith / Helicone / Braintrust / Latitude 实测</title>
    <link href="https://yomxxx.com/posts/2026-05-17-llm-observability-tools-comparison-2026" />
    <updated>2026-05-17T00:00:00.000Z</updated>
    <published>2026-05-17T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Agent 上生产后的第一件事就是接 tracing。本文用同一个 RAG + 工具调用应用接入 5 个主流可观测性平台，比较接入成本、多 turn tracing、eval 能力、自托管选项与真实月成本。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-17-microsoft-agent-framework-1-0-workshop</id>
    <title>Microsoft Agent Framework 1.0 实战：从单 agent 到多 agent 编排的生产级落地</title>
    <link href="https://yomxxx.com/posts/2026-05-17-microsoft-agent-framework-1-0-workshop" />
    <updated>2026-05-17T00:00:00.000Z</updated>
    <published>2026-05-17T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>微软 Agent Framework 5/13 发布 1.0 GA 版本，统一 .NET 与 Python 双语言 SDK。本文用一个客服多 agent 案例跑通从单 agent 到 GraphFlow 编排、工具调用、Azure 部署的完整链路。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-16-context-training-active-information-seeking-paper</id>
    <title>DeepMind 让 LLM 学会主动搜索：Context Training 论文速读</title>
    <link href="https://yomxxx.com/posts/2026-05-16-context-training-active-information-seeking-paper" />
    <updated>2026-05-16T00:00:00.000Z</updated>
    <published>2026-05-16T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>DeepMind 5 月 14 日发表 Context Training with Active Information Seeking，给 context 优化器接上搜索与浏览器，在 4 个跨域 benchmark 上稳定提升。本文拆解机制与陷阱。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-16-mint-million-lora-infrastructure-paper</id>
    <title>MinT 论文速读：用一套基础设施跑百万 LoRA 适配器</title>
    <link href="https://yomxxx.com/posts/2026-05-16-mint-million-lora-infrastructure-paper" />
    <updated>2026-05-16T00:00:00.000Z</updated>
    <published>2026-05-16T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>MindLab 5 月 14 日开源的 MinT 把 LoRA 当成 first-class 资源管理，单引擎可寻址 10 万、千级并发、handoff 提速 18 倍。本文拆解三段 scaling 与工程意义。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-16-codex-mobile-remote-vibe-coding-workshop</id>
    <title>Codex Mobile 实战工坊：在地铁上远程接管 vibe coding 会话</title>
    <link href="https://yomxxx.com/posts/2026-05-16-codex-mobile-remote-vibe-coding-workshop" />
    <updated>2026-05-16T00:00:00.000Z</updated>
    <published>2026-05-16T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenAI 5 月 14 日把 Codex agent 塞进了 ChatGPT 移动端。本文用两个真实场景跑通配置、安全模型与提交流程，给出可复制的 mobile-first agent workflow。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-16-mobile-coding-agent-comparison-2026</id>
    <title>AI Coding 移动端横评 2026：Codex Mobile vs Claude Remote vs 第三方</title>
    <link href="https://yomxxx.com/posts/2026-05-16-mobile-coding-agent-comparison-2026" />
    <updated>2026-05-16T00:00:00.000Z</updated>
    <published>2026-05-16T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Codex Mobile 5/14 上线后，移动端 vibe coding 终于有了正面对比。本文实测 Codex Mobile、Claude Code Remote、Vicoa、CC Pocket 四款方案的功能、安全模型与典型场景。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-16-spec-driven-development-vs-vibe-coding</id>
    <title>Spec-Driven Development 反攻 vibe coding：GitHub Spec Kit 深度剖析</title>
    <link href="https://yomxxx.com/posts/2026-05-16-spec-driven-development-vs-vibe-coding" />
    <updated>2026-05-16T00:00:00.000Z</updated>
    <published>2026-05-16T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>GitHub Spec Kit 用 90k+ stars 把「先写规格再让 agent 写代码」推到聚光灯下。本文拆解它的工作流、与 vibe coding 的真实分歧，以及落地团队的成本账。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-15-browsecomp-2026-web-agent-leaderboard</id>
    <title>BrowseComp 2026 横评：21 个 LLM 网页 Agent 实测排行</title>
    <link href="https://yomxxx.com/posts/2026-05-15-browsecomp-2026-web-agent-leaderboard" />
    <updated>2026-05-15T00:00:00.000Z</updated>
    <published>2026-05-15T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>BrowseComp 用 1266 道刁钻问题测试 LLM 浏览网页能力。GPT-5.5 Pro 以 90.1% 领跑，Opus 4.7、DeepSeek-V4 紧追。本文逐项拆解各模型表现与成本曲线。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-15-ebcar-embedding-context-aware-reranker</id>
    <title>EBCAR 论文精读：直接在 embedding 上跑的轻量级 Reranker</title>
    <link href="https://yomxxx.com/posts/2026-05-15-ebcar-embedding-context-aware-reranker" />
    <updated>2026-05-15T00:00:00.000Z</updated>
    <published>2026-05-15T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>ICLR 2026 论文 EBCAR 抛弃 cross-encoder 重型架构，直接在召回 embedding 上做混合注意力重排，效率提升 10 倍而精度不输。本文详解结构信息与 hybrid attention 的设计。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-15-long-context-vs-rag-when-1m-tokens-is-wrong</id>
    <title>Long Context vs RAG 之战：1M 上下文窗口何时是错的工具</title>
    <link href="https://yomxxx.com/posts/2026-05-15-long-context-vs-rag-when-1m-tokens-is-wrong" />
    <updated>2026-05-15T00:00:00.000Z</updated>
    <published>2026-05-15T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>1M token 上下文窗口本应让 RAG 过时，但实际生产中成本、注意力衰减、可观测性三个维度都让 RAG 不可替代。本文用第一性原理剖析两种范式何时该用、何时该混合。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-15-qwen3-6-plus-agentic-coding-workshop</id>
    <title>Qwen3.6 Plus Agentic 编程实战：24 小时一手体验报告</title>
    <link href="https://yomxxx.com/posts/2026-05-15-qwen3-6-plus-agentic-coding-workshop" />
    <updated>2026-05-15T00:00:00.000Z</updated>
    <published>2026-05-15T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Qwen3.6 Plus 的混合注意力 + 稀疏 MoE 架构在 agentic 编码与长程规划上展现出新高度。本文用真实任务跑通工具链、对比成本与延迟，给出可复用模板。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-15-routing-free-moe-paper-review</id>
    <title>Routing-Free MoE 论文精读：让稀疏模型摆脱路由器</title>
    <link href="https://yomxxx.com/posts/2026-05-15-routing-free-moe-paper-review" />
    <updated>2026-05-15T00:00:00.000Z</updated>
    <published>2026-05-15T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>arXiv 2604.00801 提出无路由器的 MoE 架构，用统一负载均衡同时优化专家与 token 维度。0.8B 规模实验显示训练稳定性与下游精度均胜过标准 MoE。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-14-gemma4-mtp-speculative-decoding-guide</id>
    <title>Gemma 4 MTP 推理加速实战：开源模型推理速度提升 3 倍的秘密</title>
    <link href="https://yomxxx.com/posts/2026-05-14-gemma4-mtp-speculative-decoding-guide" />
    <updated>2026-05-14T00:00:00.000Z</updated>
    <published>2026-05-14T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深入解析 Google Gemma 4 Multi-Token Prediction 机制，手把手教你使用 MTP drafter 模型实现 3 倍推理加速，涵盖 vLLM 和 SGLang 实战配置。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-14-agentic-pr-quality-msr2026-analysis</id>
    <title>AI Agent 写的代码靠谱吗？24000+ PR 数据揭示真相</title>
    <link href="https://yomxxx.com/posts/2026-05-14-agentic-pr-quality-msr2026-analysis" />
    <updated>2026-05-14T00:00:00.000Z</updated>
    <published>2026-05-14T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 MSR 2026 Mining Challenge 的 24000+ Agentic PR 数据集，深度横评 Claude、Copilot、Cursor、Devin、Codex 五大 AI Agent 的代码质量、测试覆盖和审查通过率。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-14-ai-assisted-zero-day-exploit-analysis</id>
    <title>AI 武器化的第一枪：Google 确认首例 AI 辅助零日漏洞利用</title>
    <link href="https://yomxxx.com/posts/2026-05-14-ai-assisted-zero-day-exploit-analysis" />
    <updated>2026-05-14T00:00:00.000Z</updated>
    <published>2026-05-14T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度分析 Google 威胁情报团队确认的首例 AI 辅助零日漏洞利用事件，全面探讨 AI 在网络安全攻防中的角色转变，以及企业安全团队应如何应对这一范式转移。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-14-flow-opd-on-policy-distillation-flow-matching</id>
    <title>论文精读：Flow-OPD — 让文生图模型对齐不再顾此失彼</title>
    <link href="https://yomxxx.com/posts/2026-05-14-flow-opd-on-policy-distillation-flow-matching" />
    <updated>2026-05-14T00:00:00.000Z</updated>
    <published>2026-05-14T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>解析 Flow-OPD 论文，首个将在策略蒸馏引入 Flow Matching 模型的 post-training 框架，通过像素级密集奖励和梯度隔离技术，解决多任务对齐中的奖励稀疏与梯度干扰难题。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-14-learning-fast-slow-continual-llm-adaptation</id>
    <title>论文精读：Learning, Fast and Slow — 让大模型持续学习而不遗忘</title>
    <link href="https://yomxxx.com/posts/2026-05-14-learning-fast-slow-continual-llm-adaptation" />
    <updated>2026-05-14T00:00:00.000Z</updated>
    <published>2026-05-14T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 UC Berkeley 最新论文 Fast-Slow Training，探讨如何通过快慢权重分离解决 LLM 微调中的灾难性遗忘问题，实现真正的持续学习能力。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-13-agentic-rag-architecture-deep-dive</id>
    <title>Agentic RAG 深度解析：从检索增强到智能体驱动的 RAG 架构演进</title>
    <link href="https://yomxxx.com/posts/2026-05-13-agentic-rag-architecture-deep-dive" />
    <updated>2026-05-13T00:00:00.000Z</updated>
    <published>2026-05-13T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度剖析 Agentic RAG 的架构设计、核心模式与工程实践，对比传统 RAG 的局限性，展示如何用 Agent 驱动多步检索、自适应推理和工具调用来构建企业级知识系统。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-13-draft-thinking-efficient-cot-reasoning</id>
    <title>Draft-Thinking：让长思维链推理成本降低 40% 的新方法</title>
    <link href="https://yomxxx.com/posts/2026-05-13-draft-thinking-efficient-cot-reasoning" />
    <updated>2026-05-13T00:00:00.000Z</updated>
    <published>2026-05-13T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>论文速读 Draft-Thinking，一种通过先生成精简草稿再展开推理的方法，在保持推理精度的同时大幅减少 token 消耗，对 LLM 推理成本优化意义重大。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-13-ai-code-editor-comparison-2026</id>
    <title>AI 代码编辑器横评 2026：Cursor vs Windsurf vs GitHub Copilot vs Claude Code</title>
    <link href="https://yomxxx.com/posts/2026-05-13-ai-code-editor-comparison-2026" />
    <updated>2026-05-13T00:00:00.000Z</updated>
    <published>2026-05-13T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度对比 2026 年四大 AI 代码编辑器 Cursor、Windsurf、GitHub Copilot 和 Claude Code 的功能、性能、定价与适用场景，从自动补全到 Agent 模式全面测试，附开发者选型指南。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-13-ui-tars-desktop-automation-guide</id>
    <title>UI-TARS Desktop：ByteDance 开源桌面自动化 AI Agent 实战指南</title>
    <link href="https://yomxxx.com/posts/2026-05-13-ui-tars-desktop-automation-guide" />
    <updated>2026-05-13T00:00:00.000Z</updated>
    <published>2026-05-13T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>详解 ByteDance 开源的 UI-TARS Desktop 多模态桌面自动化 Agent，从架构原理到实战部署，手把手教你用 AI 控制电脑完成复杂任务自动化。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-13-llm-inference-engine-comparison-2026</id>
    <title>LLM 推理引擎横评 2026：vLLM vs SGLang vs TensorRT-LLM 实测对比</title>
    <link href="https://yomxxx.com/posts/2026-05-13-llm-inference-engine-comparison-2026" />
    <updated>2026-05-13T00:00:00.000Z</updated>
    <published>2026-05-13T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 H100 GPU 实测数据，横向对比 vLLM、SGLang、TensorRT-LLM 三大主流 LLM 推理引擎的吞吐量、延迟、显存效率和部署复杂度，附选型决策树。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-ai-agent-frameworks-comparison-2026</id>
    <title>AI 代理框架 2026 全景对比：LangGraph vs CrewAI vs AutoGen vs Mastra vs DeerFlow</title>
    <link href="https://yomxxx.com/posts/2026-05-12-ai-agent-frameworks-comparison-2026" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度对比2026年主流AI代理框架LangGraph、CrewAI、AutoGen、Mastra、DeerFlow，从架构设计、多代理协作、生产就绪度、生态系统等维度全面解析，为开发者提供选型指南和最佳实践。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-ai-model-war-may-2026</id>
    <title>2026年5月AI模型大战：GPT-5.5 vs Claude vs Gemini vs DeepSeek V4全景分析</title>
    <link href="https://yomxxx.com/posts/2026-05-12-ai-model-war-may-2026" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度剖析2026年5月AI模型竞争格局，对比GPT-5.5、Claude Opus 4.7、Gemini 3.1 Pro、DeepSeek V4四大模型的性能、价格与适用场景</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-ai-coding-maintenance-cost</id>
    <title>AI 编码助手写的代码越多，维护成本越高？实战降低维护成本指南</title>
    <link href="https://yomxxx.com/posts/2026-05-12-ai-coding-maintenance-cost" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>James Shore 提出 AI 编码的维护成本悖论：代码产出翻倍但维护天数不变，总成本反而飙升。本文通过重构、测试生成、文档自动化、债务检测四个实战方向，教你用 Claude Code 和 Cursor 降低维护成本。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-ai-safety-2026-guide</id>
    <title>AI 安全 2026：国际安全报告、对齐研究突破与安全最佳实践</title>
    <link href="https://yomxxx.com/posts/2026-05-12-ai-safety-2026-guide" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析2026年AI安全领域最新进展，涵盖国际AI安全报告核心发现、Constitutional AI和DPO对齐技术突破、红队测试方法论、安全护栏实现，以及EU AI Act合规指南，为开发者提供全面的安全实践指南。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-ai-weekly-002</id>
    <title>AI 周报 002：Claude Code 限频翻倍、GPT-5.5 Instant、Qwen3.6 MoE 开源</title>
    <link href="https://yomxxx.com/posts/2026-05-12-ai-weekly-002" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026 年 5 月第 2 周 AI 行业热点速览：Anthropic 翻倍 Claude Code 限频、OpenAI 发布 GPT-5.5 Instant、阿里开源 Qwen3.6-35B-A3B MoE 编码模型、Skill1 统一 Agent 技能训练框架、OneManCompany 多 Agent 组织架构。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-claude-code-rate-limits-doubled</id>
    <title>Claude Code 限频翻倍：5 月新规下的实战优化策略</title>
    <link href="https://yomxxx.com/posts/2026-05-12-claude-code-rate-limits-doubled" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>Anthropic 于 2026 年 5 月 6 日宣布 Claude Code 限频翻倍并移除高峰时段限制。本文解析新配额计算方式，对比新旧限频表，提供 5 个最大化利用新配额的实战技巧，附配额监控脚本。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-github-trending-ai-tools-week18</id>
    <title>GitHub Trending AI工具周榜：2026年5月第2周</title>
    <link href="https://yomxxx.com/posts/2026-05-12-github-trending-ai-tools-week18" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度盘点2026年5月第2周GitHub最热门的AI开源项目，包括字节跳动UI-TARS-desktop多模态Agent、MCP服务器生态爆发、LangChain与MetaGPT框架对比等5大热门项目的技术特点与实用价值分析</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-google-io-2026-developer-guide</id>
    <title>Google I/O 2026 实战指南：Gemini API、Android 17 AI 功能与 Aluminium OS 开发者机会</title>
    <link href="https://yomxxx.com/posts/2026-05-12-google-io-2026-developer-guide" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>2026年5月12日Google I/O开发者大会开幕，本文深度解析Gemini API多模态函数调用、Android 17端侧AI能力、Aluminium OS桌面体验，为开发者提供实战指南。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-gpt55-cyber-security-workshop</id>
    <title>GPT-5.5-Cyber 安全能力深度实战：漏洞分析、威胁情报与代码审计</title>
    <link href="https://yomxxx.com/posts/2026-05-12-gpt55-cyber-security-workshop" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>OpenAI GPT-5.5-Cyber 网络安全专用模型实战教程，涵盖漏洞分析、威胁情报解读、代码审计三大场景的 API 调用示例、批量审计方案与工程化最佳实践。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-heterogeneous-agent-organization</id>
    <title>异构 Agent 企业化组织：OneManCompany 框架如何用公司架构管理 AI 团队</title>
    <link href="https://yomxxx.com/posts/2026-05-12-heterogeneous-agent-organization" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 OneManCompany 框架如何将企业管理理念引入多 Agent 系统——从 Agent 身份、动态团队组装、层级决策到绩效评估，探索 AI Agent 组织化的工程实践。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-claude-computer-use-rpa</id>
    <title>Claude Computer Use 构建 RPA：实战可行但成本是传统方案的 45 倍</title>
    <link href="https://yomxxx.com/posts/2026-05-12-claude-computer-use-rpa" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>用 Claude 的 Computer Use 能力构建自动化 RPA 工作流——从截图理解到鼠标键盘操作的完整链路，对比传统 RPA（UiPath/Power Automate）的成本、可靠性和维护性，附真实场景的成本核算。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-kv-cache-compression-engineering</id>
    <title>KV Cache 压缩技术全景：从 GQA 到量化到 PagedAttention 的工程进化</title>
    <link href="https://yomxxx.com/posts/2026-05-12-kv-cache-compression-engineering" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>系统梳理 LLM 推理中 KV Cache 的内存瓶颈和五种主流压缩方案——MQA/GQA 减头、KV Cache 量化、PagedAttention 分页、Sliding Window 裁剪、Token Merging 合并——对比精度、延迟、内存和适用场景。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-local-ai-research-tools-2026</id>
    <title>2026 本地 AI 研究工具横评：Ollama、LM Studio、vLLM 等 7 款工具实测对比</title>
    <link href="https://yomxxx.com/posts/2026-05-12-local-ai-research-tools-2026" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>实测 Ollama、LM Studio、vLLM、llama.cpp、LocalAI、Jan、GPT4All 七款本地 AI 推理工具，在 Apple Silicon M4 Pro/Max 上跑分对比 tokens/s、显存占用与易用性。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-local-llm-tools-comparison-2026</id>
    <title>本地 LLM 部署工具 2026 实测：Ollama vs LM Studio vs vLLM vs llama.cpp</title>
    <link href="https://yomxxx.com/posts/2026-05-12-local-llm-tools-comparison-2026" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度实测2026年四大本地LLM部署工具Ollama、LM Studio、vLLM、llama.cpp，从性能基准、易用性、功能特性、适用场景等维度全面对比，为开发者提供选型指南和最佳实践。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-marble-diffusion-rl-alignment</id>
    <title>MARBLE：扩散模型强化学习中的多维奖励平衡新范式</title>
    <link href="https://yomxxx.com/posts/2026-05-12-marble-diffusion-rl-alignment" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解读 HuggingFace 热门论文 MARBLE（arXiv:2605.06507），该框架在梯度空间中实现扩散模型强化学习微调的多维奖励同步优化，彻底解决多奖励冲突与训练不稳定问题，附伪代码和工程实践分析。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-mcp-enterprise-adoption-deep-dive</id>
    <title>MCP企业采用率78%背后：技术演进、安全挑战与最佳实践</title>
    <link href="https://yomxxx.com/posts/2026-05-12-mcp-enterprise-adoption-deep-dive" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析MCP协议在企业中的大规模采用现状，从78%企业AI团队采用率统计数据、安全漏洞案例分析到生产部署最佳实践，全面掌握MCP企业级应用的技术演进与安全防护策略</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-mcp-security-cve-analysis</id>
    <title>MCP安全实战：从CVE-2026-26030看AI Agent安全边界</title>
    <link href="https://yomxxx.com/posts/2026-05-12-mcp-security-cve-analysis" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度剖析Semantic Kernel两大CVE漏洞（CVE-2026-26030和CVE-2026-25592），掌握MCP协议安全防护最佳实践，构建安全可靠的AI Agent系统</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-mcts-knowledge-retrieval-llm</id>
    <title>MCTS-Driven Knowledge Retrieval for LLMs：用蒙特卡洛树搜索增强大模型推理</title>
    <link href="https://yomxxx.com/posts/2026-05-12-mcts-knowledge-retrieval-llm" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>精读arXiv论文2601.00003，深度解析如何用蒙特卡洛树搜索（MCTS）优化LLM的知识检索和推理过程，详解算法原理、实验结果和工程实践，提升复杂问答任务的准确率。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-on-device-ai-chrome-model</id>
    <title>端侧 AI 的崛起：当 Chrome 静默安装 4GB 模型，On-Device LLM 意味着什么</title>
    <link href="https://yomxxx.com/posts/2026-05-12-on-device-ai-chrome-model" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 Google Chrome 静默下载 Gemini Nano 引发的隐私争议说起——深度分析端侧 AI 的技术架构、隐私权衡、性能瓶颈和产业格局，覆盖 Chrome AI、Apple Intelligence、Qualcomm NPU 三条路线的对比。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-prompt-caching-cost-optimization</id>
    <title>Prompt Caching 实战：一行配置让 Claude/GPT API 成本降低 90%</title>
    <link href="https://yomxxx.com/posts/2026-05-12-prompt-caching-cost-optimization" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 Anthropic 和 OpenAI 的 Prompt Caching 机制——从原理到实战，覆盖缓存命中条件、最佳 Prompt 结构设计、成本计算公式和六个高 ROI 场景，附 TypeScript 完整代码示例。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-qwen3-6-35b-moe-review</id>
    <title>Qwen3.6-35B-A3B 评测：3B 激活参数如何打赢 22B Dense 模型</title>
    <link href="https://yomxxx.com/posts/2026-05-12-qwen3-6-35b-moe-review" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>全面评测阿里 Qwen3.6-35B-A3B——35B 总参数、3B 激活的稀疏 MoE 编码模型。对比 Qwen3.5、DeepSeek-V4、Gemma 4 等模型的编码能力，附本地部署和 API 接入完整指南。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-skill1-unified-agent-rl</id>
    <title>Skill1 论文精读：用 RL 统一训练 Agent 的技能选择、利用与蒸馏</title>
    <link href="https://yomxxx.com/posts/2026-05-12-skill1-unified-agent-rl" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解读 Skill1 框架如何用单一强化学习策略同时优化 Agent 的技能检索、选择和蒸馏三个能力，解析其架构设计、完整训练流程和 benchmark 评测结果。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-som-opponent-modeling-llm-agents</id>
    <title>SOM论文速读：LLM Agent如何建模对手行为？</title>
    <link href="https://yomxxx.com/posts/2026-05-12-som-opponent-modeling-llm-agents" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析SOM框架如何利用结构因果模型（SCM）提升LLM Agent在多智能体博弈中的对手建模能力，探索AI Agent的策略推理前沿技术与实际应用场景深度分析</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-speculative-decoding-production-guide</id>
    <title>投机解码从理论到生产：Speculative Decoding 全链路优化指南</title>
    <link href="https://yomxxx.com/posts/2026-05-12-speculative-decoding-production-guide" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深入解析 Speculative Decoding 的数学原理、工程实现与生产部署策略，覆盖草拟模型选型、接受率调优、vLLM/TensorRT-LLM 实战配置，以及 Medusa、EAGLE 等前沿变体的对比分析。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-12-vector-database-comparison-2026</id>
    <title>向量数据库 2026 选型：Milvus vs Qdrant vs Weaviate vs PgVector 终极对比</title>
    <link href="https://yomxxx.com/posts/2026-05-12-vector-database-comparison-2026" />
    <updated>2026-05-12T00:00:00.000Z</updated>
    <published>2026-05-12T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 100 万条向量的实测数据，从性能、可扩展性、部署复杂度、成本和生态集成五个维度对比四款主流向量数据库——Milvus、Qdrant、Weaviate 和 PgVector，附 RAG 场景的选型决策树。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-11-ai-agent-eating-saas</id>
    <title>AI Agent 正在吃掉 SaaS：从工具到平台的架构革命</title>
    <link href="https://yomxxx.com/posts/2026-05-11-ai-agent-eating-saas" />
    <updated>2026-05-11T00:00:00.000Z</updated>
    <published>2026-05-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>当 AI Agent 开始直接操作数据库、调用 API、填写表单，传统 SaaS 的 GUI 层变得多余。本文从架构视角分析 Agent 如何重塑软件产品形态——从 CRUD 到 Intent-Driven，从 UI-First 到 API-First，附三个已被 Agent 颠覆的 SaaS 品类分析。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-11-browser-use-ai-automation</id>
    <title>Browser Use 实战：让 AI Agent 操控浏览器完成自动化任务</title>
    <link href="https://yomxxx.com/posts/2026-05-11-browser-use-ai-automation" />
    <updated>2026-05-11T00:00:00.000Z</updated>
    <published>2026-05-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>用 Browser Use 这个 GitHub 热门开源项目，让 LLM 直接操控浏览器——点击、填表、截图、提取数据，实现从&apos;人手动操作网页&apos;到&apos;Agent 自动化完成&apos;的跃迁，附 Python 完整代码和 5 个实际案例。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-11-continuous-latent-diffusion-language-model</id>
    <title>Continuous Latent Diffusion Language Model：当扩散模型学会写文字</title>
    <link href="https://yomxxx.com/posts/2026-05-11-continuous-latent-diffusion-language-model" />
    <updated>2026-05-11T00:00:00.000Z</updated>
    <published>2026-05-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>解读 2026 年最受关注的语言模型新范式——连续潜空间扩散语言模型（CLDLM），从离散 token 预测到连续空间扩散的范式转移，对比 Autoregressive LM 的本质差异、优势局限和工程前景。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-11-deepseek-v4-review</id>
    <title>DeepSeek V4 深度测评：开源模型如何重新定义 LLM 性价比</title>
    <link href="https://yomxxx.com/posts/2026-05-11-deepseek-v4-review" />
    <updated>2026-05-11T00:00:00.000Z</updated>
    <published>2026-05-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 SWE-bench、MMLU-Pro、HumanEval 三大基准和真实编码任务的 DeepSeek V4 深度测评，对比 Claude Sonnet 4.6、GPT-4o、Llama 3.1 405B 的能力和性价比，附 API 定价分析和选型建议。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-11-dify-llmops-enterprise-ai-app</id>
    <title>Dify 实战：用开源 LLMOps 平台 30 分钟搭建企业级 AI 应用</title>
    <link href="https://yomxxx.com/posts/2026-05-11-dify-llmops-enterprise-ai-app" />
    <updated>2026-05-11T00:00:00.000Z</updated>
    <published>2026-05-11T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 Docker Compose 部署到完整 RAG 应用上线——用 Dify 这个 GitHub 50K+ Star 的开源 LLMOps 平台搭建客服问答系统，覆盖知识库配置、工作流编排、API 发布和生产监控全流程。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-agent-memory-architecture</id>
    <title>AI Agent Memory 架构全解：从 Buffer 到 Persistent Memory 的工程实践</title>
    <link href="https://yomxxx.com/posts/2026-05-10-agent-memory-architecture" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 Mem0 的 State of AI Agent Memory 2026 报告，深度解析三层记忆架构——Working Memory、Episodic Memory、Semantic Memory——的工程实现、选型建议和生产陷阱。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-attention-sink-streaminglm</id>
    <title>Attention Sink 深度解析：StreamingLLM 如何让大模型突破上下文窗口</title>
    <link href="https://yomxxx.com/posts/2026-05-10-attention-sink-streaminglm" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 Attention Sink 现象到 StreamingLLM 的工程实现——解析大模型处理无限长文本的核心机制，对比 Sliding Window、RoPE 外推和 Sink Token 三种长上下文方案的精度与延迟权衡。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-autonomous-coding-agent-showdown</id>
    <title>Devin vs OpenHands vs SWE-agent：2026 自主编程 Agent 终极横评</title>
    <link href="https://yomxxx.com/posts/2026-05-10-autonomous-coding-agent-showdown" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于 SWE-bench Verified 基准和真实项目测试，从任务完成率、代码质量、成本效率、可控性四个维度对比三款自主编程 Agent——Devin、OpenHands（原 OpenDevin）和 SWE-agent。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-langgraph-multi-agent-workflow</id>
    <title>LangGraph 实战：用状态机思维构建生产级多 Agent 工作流</title>
    <link href="https://yomxxx.com/posts/2026-05-10-langgraph-multi-agent-workflow" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 LangChain 的线性 Chain 到 LangGraph 的状态图——手把手用 TypeScript 搭建一个包含 Planner、Researcher、Coder 三个 Agent 的协作系统，附完整代码和踩坑经验。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-gemma4-deep-dive</id>
    <title>Gemma 4 深度解析：Google 开源模型的逆袭之路</title>
    <link href="https://yomxxx.com/posts/2026-05-10-gemma4-deep-dive" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度解析 Google DeepMind 2026 年发布的 Gemma 4 开源模型：MoE 架构创新、256K 上下文、Benchmark 对比 Llama/Qwen，附 Ollama 本地部署代码和选型建议。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-llm-finetuning-lora-qlora-dora</id>
    <title>LLM 微调 2026：从 LoRA 到 QLoRA 到 DoRA 的技术演进与选型指南</title>
    <link href="https://yomxxx.com/posts/2026-05-10-llm-finetuning-lora-qlora-dora" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>系统梳理 2024-2026 年 LLM 微调技术的三次关键演进——LoRA 的低秩分解、QLoRA 的量化微调、DoRA 的权重分解——对比精度、显存、训练速度和适用场景，附生产级微调 checklist。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-structured-output-data-extraction</id>
    <title>Structured Output 实战：用 JSON Schema 构建可靠的 LLM 数据提取管线</title>
    <link href="https://yomxxx.com/posts/2026-05-10-structured-output-data-extraction" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 prompt hacking 到 Structured Output——用 Claude 和 GPT 的原生 JSON Schema 约束能力构建生产级数据提取管线，覆盖发票解析、简历提取、合同条款抽取三个完整案例，附错误处理和质量保障方案。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-ollama-local-llm-complete-guide</id>
    <title>Ollama 实战：本地部署 LLM 的完整指南——从安装到生产级 API 集成</title>
    <link href="https://yomxxx.com/posts/2026-05-10-ollama-local-llm-complete-guide" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>用 Ollama 在本地 Mac/Linux 上一行命令运行 Llama 3、Qwen 2.5、Gemma 4 等开源大模型，覆盖模型选择、量化对比、REST API 调用、LangChain 集成和性能优化，附 M4 Mac 实测数据。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-10-vibe-coding-revolution</id>
    <title>Vibe Coding 2026：当「描述想法」取代「写代码」</title>
    <link href="https://yomxxx.com/posts/2026-05-10-vibe-coding-revolution" />
    <updated>2026-05-10T00:00:00.000Z</updated>
    <published>2026-05-10T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度剖析 Vibe Coding 编程范式的三个层级——Tab 补全、Composer 多文件、Agentic 全自主——对比 Cursor/Claude Code/Codex/Copilot 四大工具，附真实案例和风险边界分析。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-agentic-ai-architecture-evolution</id>
    <title>Agentic AI 第二年：从 PoC 到生产系统的五个认知跃迁</title>
    <link href="https://yomxxx.com/posts/2026-05-09-agentic-ai-architecture-evolution" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>回顾 18 个月 Agentic AI 生产实践中的五次认知转变——从 Chain 到状态机、从 API 调用到契约、从向量搜索到三层记忆、从通信到信任链、从准确率到多维评估体系。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-ai-agent-security-trust-boundary</id>
    <title>AI Agent 安全红线：当 Agent 开始违规，我们怎么设计信任边界</title>
    <link href="https://yomxxx.com/posts/2026-05-09-ai-agent-security-trust-boundary" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 IETF Agent 认证草案到运行时约束到审计回滚，拆解三层安全架构和五种常见失败模式，附 TypeScript Guard Wrapper 代码和 Supervisor vs Consensus 信任模型对比。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-ai-weekly-001</id>
    <title>AI 周报 #001: Claude Opus 4.7 发布 · OpenAI Frontier 计划 · Agentic AI 全面铺开</title>
    <link href="https://yomxxx.com/posts/2026-05-09-ai-weekly-001" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>YOMXXX AI 周报第一期：Anthropic 发布 Claude Opus 4.7、OpenAI 公布 Frontier 计划、Google Gemini 3.1 Pro 发布、Agentic AI 成为行业主旋律，附本周值得关注的工具和论文。</summary>
    <category term="weekly" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-ai-coding-assistant-showdown-2026</id>
    <title>Claude Code vs Cursor vs Windsurf: 2026 AI 编程助手终极横评</title>
    <link href="https://yomxxx.com/posts/2026-05-09-ai-coding-assistant-showdown-2026" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>基于半年深度使用经验，从代码生成质量、上下文理解、多文件协作、Agentic 能力、价格五个维度对比 Claude Code、Cursor、Windsurf 三款主流 AI 编程助手。</summary>
    <category term="tools" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-llm-benchmark-spring-2026</id>
    <title>Claude Opus 4.6 vs GPT-5.4 vs Gemini 3.1 Pro: 2026 春季 LLM 实测横评</title>
    <link href="https://yomxxx.com/posts/2026-05-09-llm-benchmark-spring-2026" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度对比 2026 年三大前沿 LLM 的基准测试成绩、实际编码表现、定价与适用场景。基于 SWE-bench、HumanEval、MMLU-Pro 等基准以及真实项目重写实测。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-mcp-server-from-scratch</id>
    <title>MCP 实战：从零搭建一个 Model Context Protocol Server</title>
    <link href="https://yomxxx.com/posts/2026-05-09-mcp-server-from-scratch" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>手把手用 TypeScript 搭建 MCP Server，从协议原理到完整代码到 Claude Code 集成，覆盖 Tool、Resource、Prompt 三大核心概念和 2026 路线图展望。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-llm-inference-optimization-landscape</id>
    <title>LLM 推理优化全景 2026：从 10x 降本到实时响应的工程路径</title>
    <link href="https://yomxxx.com/posts/2026-05-09-llm-inference-optimization-landscape" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>四层优化金字塔——从 Prompt 优化到模型路由到量化到推理引擎——系统性降低 LLM 推理成本和延迟，附 vLLM vs SGLang 实测数据和生产部署 checklist。</summary>
    <category term="long-form" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-multimodal-ai-pipeline</id>
    <title>多模态 AI 实战：用 Claude 4.7 和 Gemini 3 搭建图文理解 Pipeline</title>
    <link href="https://yomxxx.com/posts/2026-05-09-multimodal-ai-pipeline" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>三个真实场景的 TypeScript 代码实现——发票 OCR、架构图理解、视频会议摘要——附 2026 多模态基准对比和 Claude 4.7 vs Gemini 3 选型指南。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-rag-architecture-selection-guide</id>
    <title>RAG 架构选型 2026: Pipeline vs Agentic vs Knowledge Graph — 怎么选不翻车</title>
    <link href="https://yomxxx.com/posts/2026-05-09-rag-architecture-selection-guide" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>深度对比三种主流 RAG 架构——Pipeline RAG、Agentic RAG、Knowledge Graph RAG——的准确率、延迟、成本与适用场景，附带决策矩阵、可运行代码和生产踩坑经验。</summary>
    <category term="workshop" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-state-space-models-vs-transformer</id>
    <title>State Space Models 深度解析：Mamba 凭什么挑战 Transformer</title>
    <link href="https://yomxxx.com/posts/2026-05-09-state-space-models-vs-transformer" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>从 S4 到 Mamba 到 Mamba-2 的演进路线，用直觉而非公式解释 SSM 的核心思想，对比 Transformer 的复杂度瓶颈，分析混合架构趋势和工程落地场景。</summary>
    <category term="paper" />
  </entry>
  <entry>
    <id>https://yomxxx.com/posts/2026-05-09-welcome</id>
    <title>Hello, World — 一个面向 AI 前沿的工程笔记</title>
    <link href="https://yomxxx.com/posts/2026-05-09-welcome" />
    <updated>2026-05-09T00:00:00.000Z</updated>
    <published>2026-05-09T00:00:00.000Z</published>
    <author><name>YOMXXX</name></author>
    <summary>YOMXXX 开篇——一个面向 AI 前沿的个人工程博客。本文介绍为什么我决定开这个博客、会写什么样的内容、每周的发布节奏，以及作为读者你能期待从这里获得什么样的深度文章与工程洞见。</summary>
    <category term="long-form" />
  </entry>
</feed>