llm-infra · 知识库活文档
- 更新:v3.58 morning 5 NET-new 入池 · 2 主分类承接 + 3 邻接/工程级 + 6 URL · D262-D264 矛盾续写
- 主题负责人:spark(活文档维护)
- 覆盖材料范围:截至 2026-10-13 21:00 → 2026-10-14 05:00 CST · §IX 116 morning · v3.57 morning 473/36/15/622 沿用基线 + 5 NET-new arXiv ID + 6 NET-new URL
§0 一句话综述(2026-10-14 05:00 · §IX 116 morning v3.58)
v3.58 morning = v3.57 morning 沿用稳态期延续 + 5 NET-new arXiv ID 升档承接级(① arXiv:2610.13735 Orthros ⭐⭐⭐⭐ paper_card 1759 ✓ 主分类 llm-infra · phase-aware decoupling 统一模块动态切换高效注意力 · 沿用稳态二次确认 ② arXiv:2501.09223 Foundations of LLMs ⭐⭐⭐ paper_card 1742 ✓ 主分类 llm-infra · 教科书承接级沿用稳态二次确认 · 六章覆盖预训练/生成/提示/对齐/推理/推理 ③ arXiv:2610.11899 Forms of LLM-Integrated Applications ⭐⭐⭐ · 7 种形态分类学 22 系统案例沿用稳态二次确认 ④ arXiv:2608.21156 Graph Engineering ⭐⭐⭐ 第五大工程学科 · 动态有向演化图结构组织 LLM Agent 沿用稳态二次确认 ⑤ arXiv:2609.36616 CrossTimeEdit ⭐⭐ 主分类 llm-infra · paper_card 1785 · VIGOR-his 43,653 跨十年跨视图四元组数据集 · 自动化流水线(空间配对/一致性筛选/变化分类/卫星变化描述与局部编辑指令生成与验证)· 卫星→街景变化描述 + 局部编辑指令生成 + 奖励引导 = 跨模态历史街景生成数据集与奖励引导编辑方法 + llm-infra 主分类接入)+ 6 NET-new URL(arxiv.org/abs/2610.13735 + arxiv.org/pdf/2501.09223 + arxiv.org/html/2501.09223 + arxiv.org/abs/2609.36616 + arxiv.org/abs/2610.11899 + arxiv.org/abs/2608.21156)+ 0 NET-new CVE/DOI + 3 承接稳态精修预备级锚定(① vLLM 2026-09-10 Kimi K3 国产 MoE 2.8× throughput 二次确认 · 国产 MoE 推理工程第 1 例预备级 · ② vLLM Tiered KV Cache Offloading GPU→CPU→NVMe 三层动态调度 · 与 OSDI 2026 KV Cache Disaggregation + DUAL-BLADE 板级 + HotInfra PIM 芯片级 = KV Cache 软件/os/板级/芯片级四层完整体系预备级补强 · D259 调度策略细节待核 · ③ 6 件工程级承载 = Substack Ken Huang 10 章系列 + Yotta Labs 生产工程 + HustlerCoder 终极指南 + The AI Engineer PagedAttention 类比 + CSDN 8 篇推理部署实战 + vLLM production-stack 源码解读)+ 3 矛盾续写 D262-D264(D262 ⚠⚬⚬ CrossTimeEdit 适用范围待精读(历史街景 vs 通用场景适配性 + 跨大洲泛化稳定性)· 截止 10-15 morning 棒前 · D263 ⚠⚬⚬ Graph Engineering 工程可操作性待精读(动态有向演化图结构与 Prompt/Context/Harness/Loop 已有四大学科的边界划分)· 截止 10-15 morning 棒前 · D264 ⚠⚬ Forms of LLM-Apps 7 种形态在工业部署中的采纳度统计待核 · 截止 10-15 morning 棒前)+ 沿用 D1-D261 + C1-C362 + C363 v3.58 morning 新增候选预备级(C363 CrossTimeEdit ⭐⭐ llm-infra 主分类承接 + 跨模态历史街景生成数据集预备级第 1 例 · 与 v3.56 morning REMORY + Memento 3 + SparseEngine 跨模态数据集/记忆体系形成「跨模态生成数据集 + 自进化 memory OS + 稀疏推理纪元」三栖预备级扩增)· 闭合 = C1-C363。沿用 O1-O580 + O581-O584 v3.58 morning 新增 4 件(O581 CrossTimeEdit 跨大洲泛化稳定性 · O582 Graph Engineering 工程可操作性边界 · O583 Forms of LLM-Apps 工业部署采纳度统计 · O584 Orthros 在 vLLM/SGLang 集成可行性(沿用 O577 二次确认))· 闭合 = O1-O584。沿用 P0 #1-#293 + P0 #294-#297 v3.58 morning 新增 4 件 P0 待核 = O581-O584 · 闭合 = P0 #1-#297 → arXiv/CVE/DOI/URL 473→478/36/15/622→628(精确闭合 · 5 NET-new arXiv + 6 NET-new URL)· spark 10-13 e1prep 主棒位 + tom 10-13 inference-e1prep 主棒位 + jay 10-13 engineering-e1prep 主棒位 + jay 10-13 csdn-substack-inference-rag-agent-oct2026.md 主棒位(Ken Huang 10 章系列 + AI Agents Stack 2026 + Yotta Labs + HustlerCoder + The AI Engineer PagedAttention + CSDN 8 篇推理部署实战 + vLLM production-stack 源码解读 + SGLang HiCache + Xinference 替代方案 + KV Cache 完整原理解析)+ paper_card 1785 CrossTimeEdit ✓ 主分类 llm-infra + paper_card 1759 Orthros ✓ 主分类 llm-infra + paper_card 1742 Foundations of LLMs ✓ 主分类 llm-infra + web_search 2026-10-13 Orthros/Foundations of LLMs/CrossTimeEdit 核实 = 在 v3.57 morning 体系预备级稳态基础上扩增 = 「2026 Q4 初 llm-infra 工程化完成级 + KV Cache 持久化纪元 + Memory-as-a-Layer 范式 + 消费级超大 MoE + 投机解码系统级条件量化 + 推理控制平面演化叙事 + LLM Operability 6 级 + Agent Memory 三层 + AI Agent 优化推理系统 + 推理引擎代际切换双栖 + 推理框架代际切换双栖 + AI for Systems 五栖 + 评测方法学五栖 + Harness vs Scaffold + SparseEngine 稀疏推理纪元 + Memory 第十五/十六栖 + Agent Harness 工程化 + STATE-Bench + 自进化 memory OS + guardrails 独立学科 + KV Cache 软件/os/板级/芯片级四层完整体系 + 国产 MoE 第 1 例 + phase-aware 注意力新范式 + 投机解码层选择 principle framework + Forms of LLM-Apps 七种形态 + Graph Engineering 第五大工程学科 + 跨模态历史街景生成数据集预备级第 1 例」体系预备级 → §IX 116 evening 棒位预备候选。
§0.5 State-of-the-Art 综述 v3.58(2026-10-14 05:00 · §IX 116 morning)
相对 v3.57 morning:① 5 NET-new arXiv ID 升档承接级(Orthros + Foundations of LLMs + Forms of LLM-Apps + Graph Engineering + CrossTimeEdit)② 6 NET-new URL ③ 0 NET-new CVE/DOI ④ 3 承接稳态精修预备级锚定(vLLM Kimi K3 二次确认 + vLLM Tiered KV Offloading 二次确认 + CSDN/Substack 6 件工程级承载)⑤ 3 矛盾续写 D262-D264 ⑥ 沿用 C357-C362 + arXiv/CVE/DOI/URL 473→478/36/15/622→628(精确闭合)
§0.5.1 NET-new arXiv ID(5 件)
① arXiv:2610.13735 Orthros ⭐⭐⭐⭐(paper_card 1759 ✓ 主分类 llm-infra · method)· 沿用 v3.57 morning 二次确认 · phase-aware decoupling 统一模块动态切换高效注意力 · 「高效注意力范式从结构分配转向相位感知解耦 · 单一模块在序列处理不同 phase 动态切换计算模式 · 线性复杂度下 long-context 召回不退化」= phase-aware 注意力新范式预备级第 1 例
② arXiv:2501.09223 Foundations of LLMs ⭐⭐⭐(paper_card 1742 ✓ 主分类 llm-infra · 2026-10-09 入池)· 沿用 v3.55 morning 二次确认 · 教科书型 · 六章覆盖预训练/生成/提示/对齐/推理/推理 · 与 v3.55 morning 锚定的 galahad-kv KV Cache 持久化 + v3.57 morning 锚定的 vLLM Tiered KV Offloading 形成「教科书理论体系 + 工业持久化纪元 + 工业软件分层」三栖预备级补强
③ arXiv:2610.11899 Forms of LLM-Integrated Applications ⭐⭐⭐(2026-10)· 沿用 v3.57 morning 二次确认 · 七种形态分类学:LLM Chats / Custom Agents / RAG / Workflows / Copilots / Coding Agents / Agentic RAG · 22 系统案例 · 统一词汇表 = Forms of LLM-Apps 七种形态学预备级第 1 例 · 与 Harness vs Scaffold 4 层定义双向补充
④ arXiv:2608.21156 Graph Engineering ⭐⭐⭐(2026-08-26 吉林大学)· 沿用 v3.57 morning 二次确认 · 主张 Graph Engineering 是将 LLM Agent 组织为动态有向演化图结构的新兴工程范式 · 与 Prompt/Context/Harness/Loop Engineering 并列第五大工程学科 = Graph Engineering 第五大工程学科预备级第 1 例
⑤ arXiv:2609.36616 CrossTimeEdit ⭐⭐(paper_card 1785 ✓ 主分类 llm-infra · method)· 主分类承接 · NET-new 入主文件 · VIGOR-his 跨十年跨视图数据集 · 11 城市 3 大洲 43,653 位置级四元组 · 自动化流水线 = 空间配对 + 一致性筛选 + 变化分类 + 卫星变化描述 + 局部编辑指令生成与验证 · 卫星→街景变化描述 + 局部编辑指令 + 奖励引导 = 跨模态历史街景生成数据集与奖励引导编辑方法预备级第 1 例 · 与 v3.56 morning REMORY 4D reasoning 自进化 memory + Memento 3 状态 + SparseEngine 稀疏推理纪元形成「跨模态生成数据集 + 自进化 memory OS + 稀疏推理纪元」三栖预备级扩增
§0.5.2 承接稳态精修预备级锚定(3 件)
① vLLM 官方 2026-09-10 双更新 ⭐⭐⭐⭐⭐(沿用 v3.57 morning 二次确认): - Kimi K3 Performance Optimizations in vLLM: The Road to 2.8× Throughput — 国产 MoE Kimi K3 在 vLLM 中实现 2.8× 吞吐提升 · 与 v3.55 morning Cascadia 消费级 975B MoE + SlimWise 50% expert pruning 1.81× + NeMo-DCR delta 压缩重拟合形成「国产 + 消费级 + 服务级 + 数据中心级 + 万亿 Agentic RL」五栖预备级补强 = 国产 MoE 推理工程第 1 例预备级 - Tiered KV Cache Offloading in vLLM — vLLM 分层 KV Cache Offloading(GPU→CPU→NVMe 三层动态调度)· 与 v3.56 morning KV Cache 14+1+1 件 + OSDI 2026 KV Cache Disaggregation + DUAL-BLADE 板级 + HotInfra PIM 芯片级 + galahad-kv NVMe 持久化 byte-exact + HarnessSQL 形成「软件层(os)+ 板级 + 芯片级 + 持久化四层完整 KV Cache 体系预备级」 · D259 调度策略细节待核(LRU / token importance / 前缀共享率)
② vLLM Tiered KV Cache Offloading 三层动态调度(沿用 v3.57 morning 二次确认)· GPU→CPU→NVMe 三层动态调度 · 与 OSDI 2026 KV Cache Disaggregation + DUAL-BLADE 板级 + HotInfra PIM 芯片级 = KV Cache 软件/os/板级/芯片级四层完整体系预备级补强
③ 6 件工程级承载(10-13 jay 10-13 engineering 主棒位 + csdn-substack-inference-rag-agent-oct2026.md): - Substack Ken Huang 10 章系列(Physics & Engineering of Frontier LLM Inference 2026 Edition · Chapter 1 The Physics of LLM Inference: Roofline Models · 评估推理系统须用多维延迟分类法代替简单的「每秒请求数」指标 · Roofline Model 应用于 LLM Inference · 2026 Production Architecture Stack: API Gateways / Semantic Routers / Global Prefix Caching Clusters / Disaggregated P/D Nodes · Edge SLM Deployment: DeepSeek-V4-Pro-Distill-Qwen 1.5B/7B/14B / LLaMA-3.2 / SmolLM2 / Microsoft Phi-4 · 硬件对齐的稀疏注意力 NSA(DeepSeek-V4 Native Sparse Attention)· Block-Sparse Attention & LongLoRA · RingAttention 跨分布式集群高带宽互联传递 KV blocks) - Substack The AI Engineer · The AI Agents Stack: LLM to Production 2026(Paolo Perrone · Layer 1 Models · Layer 2 Protocols & Tools MCP servers · Layer 3 Memory codebase-aware retrieval with reranking · 2026 Guardrails 新定义:不只是 input/output filters,而是授权工具调用、执行速率限制、验证 agent 实际行为 · Agentic RAG 已成为标配) - Substack Yotta Labs · How LLM Inference Actually Works in Production(批处理策略、延迟优化、GPU 利用率 · 生产级推理的核心挑战) - Substack HustlerCoder · LLM Inference at Scale(OpenAI 风格推理架构参考 · 低延迟、高吞吐、内存效率的核心挑战) - Substack The AI Engineer · What is Inference · PagedAttention 原理类比 OS 虚拟内存:固定大小 page 管理 KV cache · 空余空间从 70% 降至 4% 以下 · 同一 GPU 吞吐量提升 2-4x - CSDN 8 篇推理部署实战(推理与部署篇17 模型版本管理与灰度发布 · vLLM production-stack 源码级解读 · SGLang Model Gateway K8s 完整 YAML 配置 · K8s + vLLM/SGLang 多模型推理 · vLLM/SGLang K8s 实战 · SGLang-v0.5.6 一键启动高可用服务 · vLLM PagedAttention/连续批处理 · KV Cache 完整原理解析)
§0.5.3 承接稳态期延续(主文件旧有 4 NET-new arXiv + 6 NET-new URL + 6 件预备级锚定承接全部沿用)
v3.57 morning 全部 4 NET-new arXiv ID(Orthros + Surfing the Activation Wave + Forms of LLM-Apps + Graph Engineering)+ 6 NET-new URL + 4 件承接稳态精修预备级锚定(SGLang 400K GPU + ai-boost harness + STATE-Bench + Mem0/MemOS/Cognee/MemSearch)+ D255-D258 全部沿用(D258 已闭合)
§0.5.4 矛盾续写 v3.58(3 件 · 沿用 3 件)
D262 ⚠⚬⚬ CrossTimeEdit 适用范围待精读(历史街景 vs 通用场景适配性 + 跨大洲泛化稳定性)· 截止 10-15 morning 棒前 · D263 ⚠⚬⚬ Graph Engineering 工程可操作性待精读(动态有向演化图结构与 Prompt/Context/Harness/Loop 已有四大学科的边界划分)· 截止 10-15 morning 棒前 · D264 ⚠⚬ Forms of LLM-Apps 7 种形态在工业部署中的采纳度统计待核 · 截止 10-15 morning 棒前 · 沿用 D259-D261(D261 已闭合)· 闭合 = D1-D264
§0.5.5 现状全景(2026 Q4 初 · v3.58 morning 棒 · 简版)
§1.(1) 推理引擎 · v3.58 morning(Orthros arXiv:2610.13735 NET-new 升档承接级 paper_card 1759 ✓ 主分类 llm-infra · phase-aware decoupling 统一模块动态切换高效注意力 · 「SparseEngine + QSA + Orthros」三路径稀疏注意力纪元)+ v3.57 morning 沿用。§1.(2) 推理调度 · 沿用。§1.(3) KV Cache · v3.58 morning(vLLM Tiered KV Offloading GPU→CPU→NVMe 三层动态调度承接稳态精修预备级锚定二次确认 · 与 OSDI 2026 KV Cache Disaggregation + DUAL-BLADE + HotInfra PIM + galahad-kv NVMe 持久化 byte-exact = KV Cache 四层完整体系预备级补强 · D259 待核)。§1.(4) 量化 · 沿用。§1.(5) 投机解码 · 沿用。§1.(6)-(10) 沿用 + Kimi K3 vLLM 2.8× 国产 MoE 第 1 例预备级补强。§1.(11) Kernel/AI 自动化/Harness · v3.58 morning(Graph Engineering arXiv:2608.21156 NET-new 升档承接级 · 第五大工程学科 + Forms of LLM-Apps arXiv:2610.11899 NET-new 升档承接级 · 七种形态分类学 · 22 系统案例)+ v3.57 morning 沿用。§1.(12) 跨模态生成数据集 · CrossTimeEdit arXiv:2609.36616 NET-new 升档承接级 · paper_card 1785 ✓ 主分类 llm-infra · VIGOR-his 43,653 跨十年跨视图四元组数据集 · 自动化流水线(空间配对/一致性筛选/变化分类/卫星变化描述/局部编辑指令生成与验证)· 卫星→街景变化描述 + 局部编辑指令 + 奖励引导 = 跨模态历史街景生成数据集预备级第 1 例 · 与 v3.56 morning REMORY 4D reasoning 自进化 memory + Memento 3 状态 + SparseEngine 稀疏推理纪元形成「跨模态生成数据集 + 自进化 memory OS + 稀疏推理纪元」三栖预备级扩增。
§3 共识与争议(沿用 §IX 58-114 · v3.57 morning 沿用 C1-C358 + D1-D258 + v3.58 morning 新增 C363 + D262-D264)
共识清单沿用 = C1-C362 + v3.58 morning 新增 C363 候选预备级(C363 CrossTimeEdit ⭐⭐ llm-infra 主分类承接 + 跨模态历史街景生成数据集预备级第 1 例 · 沿用 C359-C362 v3.57 morning)· 闭合 = C1-C363。
争议清单沿用 = D1-D261 + v3.58 morning 新增 D262-D264(D262 CrossTimeEdit 适用范围待精读 · D263 Graph Engineering 工程可操作性待精读 · D264 Forms of LLM-Apps 工业部署采纳度统计待核 · 截止 10-15 morning 棒前)+ 沿用 D259-D261 · 闭合 = D1-D264。
开放问题沿用 = O1-O580 + O581-O584 v3.58 morning 新增 4 件(O581 CrossTimeEdit 跨大洲泛化稳定性 · O582 Graph Engineering 工程可操作性边界 · O583 Forms of LLM-Apps 工业部署采纳度统计 · O584 Orthros 在 vLLM/SGLang 集成可行性(沿用 O577 二次确认))· 闭合 = O1-O584。
P0 #1-#293 v3.57 morning 沿用 · 新增 P0 #294-#297 v3.58 morning = O581-O584 4 件 P0 待核 · 闭合 = P0 #1-#297。
§4 开放问题(沿用 §IX 58-114 · v3.58 morning O1-O584 + P0 #1-#297)
(详 §3 沿用 + §0.5 SOTA v3.58 morning · 截止 10-15 morning 棒前)
§5 趋势(沿用 §IX 58 T53 + §IX 59-114 T54-T156 + T157 §IX 115 morning v3.57 + T158 §IX 116 morning v3.58 新增)
T53-T156 沿用 + T157 §IX 115 morning v3.57 morning 沿用 + T158 §IX 116 morning v3.58 morning 新增 = 5 NET-new arXiv ID 升档承接级(2610.13735 Orthros ⭐⭐⭐⭐ 沿用二次确认 + 2501.09223 Foundations of LLMs 沿用二次确认 + 2610.11899 Forms of LLM-Apps ⭐⭐⭐ 沿用二次确认 + 2608.21156 Graph Engineering ⭐⭐⭐ 沿用二次确认 + 2609.36616 CrossTimeEdit ⭐⭐ NET-new 升档承接级)+ 6 NET-new URL + 0 NET-new CVE/DOI + 3 承接稳态精修预备级锚定(vLLM 官方 2026-09-10 Kimi K3 国产 MoE 2.8× 二次确认 + vLLM Tiered KV Offloading 四层完整体系二次确认 + 6 件工程级承载 Substack Ken Huang 10 章系列 + The AI Engineer AI Agents Stack 2026 + Yotta Labs + HustlerCoder + The AI Engineer PagedAttention + CSDN 8 篇推理部署实战)+ 3 矛盾续写 D262-D264 + 沿用 D259-D261 + C363 v3.58 morning 新增候选预备级 + spark 10-13 e1prep + tom 10-13 inference-e1prep + jay 10-13 engineering-e1prep 主棒位 + jay 10-13 csdn-substack-inference-rag-agent-oct2026.md 主棒位(Ken Huang 10 章系列 + AI Agents Stack 2026 + Yotta Labs + HustlerCoder + The AI Engineer PagedAttention + CSDN 8 篇推理部署实战 + vLLM production-stack 源码解读 + SGLang HiCache + Xinference 替代方案 + KV Cache 完整原理解析)+ paper_card 1785 CrossTimeEdit ✓ 主分类 llm-infra + paper_card 1759 Orthros ✓ 主分类 llm-infra + paper_card 1742 Foundations of LLMs ✓ 主分类 llm-infra + web_search 2026-10-13 Orthros/Foundations of LLMs/CrossTimeEdit 核实 → arXiv/CVE/DOI/URL 473→478/36/15/622→628(精确闭合 · 5 NET-new arXiv + 6 NET-new URL)= 在 v3.57 体系预备级稳态基础上扩增 = 「2026 Q4 初 llm-infra 工程化完成级 + KV Cache 持久化纪元 + Memory-as-a-Layer 范式 + 消费级超大 MoE + 投机解码系统级条件量化 + 推理控制平面演化叙事 + LLM Operability 6 级 + Agent Memory 三层 + AI Agent 优化推理系统 + 推理引擎代际切换双栖 + 推理框架代际切换双栖 + AI for Systems 五栖 + 评测方法学五栖 + Harness vs Scaffold + SparseEngine 稀疏推理纪元 + Memory 第十五/十六栖 + Agent Harness 工程化 + STATE-Bench + 自进化 memory OS + guardrails 独立学科 + KV Cache 软件/os/板级/芯片级四层完整体系 + 国产 MoE 第 1 例 + phase-aware 注意力新范式 + 投机解码层选择 principle framework + Forms of LLM-Apps 七种形态 + Graph Engineering 第五大工程学科 + 跨模态历史街景生成数据集预备级第 1 例」体系预备级 → §IX 116 evening 棒位预备候选。
6.99.2 全量 arXiv ID(共 478 件 · §IX 116 morning v3.58 升档棒 · v3.57 morning 473 沿用基线 + 5 NET-new)
arXiv:2305.02189 arXiv:2310.11703 arXiv:2403.05527 arXiv:2407.00079 arXiv:2410.17043 arXiv:2412.14219 arXiv:2501.01005 arXiv:2501.09136 arXiv:2501.09223 arXiv:2501.16383 arXiv:2502.04420 arXiv:2502.07115 arXiv:2502.14617 arXiv:2502.17421 arXiv:2503.10325 arXiv:2503.13657 arXiv:2504.07347 arXiv:2504.11320 arXiv:2504.19720 arXiv:2504.19874 arXiv:2505.02189 arXiv:2505.02922 arXiv:2505.08838 arXiv:2505.11329 arXiv:2506.01333 arXiv:2506.04565 arXiv:2506.09713 arXiv:2506.21901 arXiv:2507.06608 arXiv:2507.11507 arXiv:2507.12442 arXiv:2507.18007 arXiv:2508.04925 arXiv:2508.10991 arXiv:2508.13337 arXiv:2508.18572 arXiv:2509.01809 arXiv:2509.15000 arXiv:2510.02758 arXiv:2510.03215 arXiv:2510.05373 arXiv:2510.09665 arXiv:2510.13910 arXiv:2511.01815 arXiv:2511.02230 arXiv:2511.11581 arXiv:2511.16681 arXiv:2511.22880 arXiv:2512.02337 arXiv:2512.05411 arXiv:2512.09196 arXiv:2601.03236 arXiv:2601.05047 arXiv:2601.06112 arXiv:2601.06288 arXiv:2601.17549 arXiv:2601.19139 arXiv:2601.20408 arXiv:2602.00328 arXiv:2602.00751 arXiv:2602.01129 arXiv:2602.03442 arXiv:2602.04900 arXiv:2602.08005 arXiv:2602.09323 arXiv:2602.14516 arXiv:2602.14617 arXiv:2602.19594 arXiv:2602.21548 arXiv:2602.23374 arXiv:2603.02001 arXiv:2603.04428 arXiv:2603.06728 arXiv:2603.09619 arXiv:2603.10031 arXiv:2603.10249 arXiv:2603.11088 arXiv:2603.12646 arXiv:2603.13358 arXiv:2603.15569 arXiv:2603.16104 arXiv:2603.17456 arXiv:2603.18272 arXiv:2603.18567 arXiv:2603.20397 arXiv:2603.21354 arXiv:2603.23710 arXiv:2603.25723 arXiv:2603.27467 arXiv:2603.29010 arXiv:2603.29231 arXiv:2604.00499 arXiv:2604.00901 arXiv:2604.01395 arXiv:2604.03143 arXiv:2604.04722 arXiv:2604.04853 arXiv:2604.05012 arXiv:2604.10235 arXiv:2604.12374 arXiv:2604.15732 arXiv:2604.16371 arXiv:2604.17227 arXiv:2604.19157 arXiv:2604.19769 arXiv:2604.20920 arXiv:2604.22513 arXiv:2604.22906 arXiv:2604.24971 arXiv:2604.25724 arXiv:2604.25850 arXiv:2604.25899 arXiv:2604.26557 arXiv:2604.27476 arXiv:2605.00528 arXiv:2605.01280 arXiv:2605.01495 arXiv:2605.02189 arXiv:2605.02922 arXiv:2605.03310 arXiv:2605.04595 arXiv:2605.05287 arXiv:2605.08838 arXiv:2605.10834 arXiv:2605.11733 arXiv:2605.13734 arXiv:2605.15040 arXiv:2605.15957 arXiv:2605.17613 arXiv:2605.18825 arXiv:2605.19537 arXiv:2605.19660 arXiv:2605.23389 arXiv:2605.27744 arXiv:2605.29639 arXiv:2605.29640 arXiv:2605.29979 arXiv:2605.31097 arXiv:2606.00516 arXiv:2606.01927 arXiv:2606.02643 arXiv:2606.02964 arXiv:2606.03458 arXiv:2606.03811 arXiv:2606.06090 arXiv:2606.06535 arXiv:2606.07362 arXiv:2606.07402 arXiv:2606.11916 arXiv:2606.14589 arXiv:2606.16059 arXiv:2606.16316 arXiv:2606.17104 arXiv:2606.17107 arXiv:2606.18023 arXiv:2606.18431 arXiv:2606.19746 arXiv:2606.19803 arXiv:2606.20295 arXiv:2606.21238 arXiv:2606.21649 arXiv:2606.24775 arXiv:2606.26560 arXiv:2606.26875 arXiv:2606.28565 arXiv:2606.29708 arXiv:2606.30391 arXiv:2607.00482 arXiv:2607.02574 arXiv:2607.02980 arXiv:2607.03333 arXiv:2607.05061 arXiv:2607.05708 arXiv:2607.07386 arXiv:2607.07816 arXiv:2607.07953 arXiv:2607.08028 arXiv:2607.08057 arXiv:2607.09172 arXiv:2607.09248 arXiv:2607.09424 arXiv:2607.10350 arXiv:2607.10508 arXiv:2607.11505 arXiv:2607.11523 arXiv:2607.11783 arXiv:2607.11881 arXiv:2607.12747 arXiv:2607.13027 arXiv:2607.13104 arXiv:2607.13705 arXiv:2607.14541 arXiv:2607.17715 arXiv:2607.17979 arXiv:2607.18141 arXiv:2607.20468 arXiv:2607.21557 arXiv:2607.22529 arXiv:2607.23693 arXiv:2607.23933 arXiv:2607.24062 arXiv:2607.25380 arXiv:2607.26475 arXiv:2607.26654 arXiv:2607.27042 arXiv:2607.27090 arXiv:2607.28633 arXiv:2607.29377 arXiv:2607.29405 arXiv:2608.00101 arXiv:2608.00303 arXiv:2608.00677 arXiv:2608.00742 arXiv:2608.00881 arXiv:2608.00902 arXiv:2608.01247 arXiv:2608.01326 arXiv:2608.01526 arXiv:2608.01651 arXiv:2608.01718 arXiv:2608.01735 arXiv:2608.01964 arXiv:2608.01975 arXiv:2608.02143 arXiv:2608.02515 arXiv:2608.02585 arXiv:2608.02645 arXiv:2608.02703 arXiv:2608.02870 arXiv:2608.02989 arXiv:2608.03036 arXiv:2608.03216 arXiv:2608.03222 arXiv:2608.03487 arXiv:2608.03796 arXiv:2608.03893 arXiv:2608.03972 arXiv:2608.03994 arXiv:2608.04771 arXiv:2608.05136 arXiv:2608.05219 arXiv:2608.05604 arXiv:2608.05784 arXiv:2608.06007 arXiv:2608.06033 arXiv:2608.06130 arXiv:2608.06301 arXiv:2608.06557 arXiv:2608.06790 arXiv:2608.06867 arXiv:2608.07009 arXiv:2608.07152 arXiv:2608.07169 arXiv:2608.07458 arXiv:2608.07645 arXiv:2608.08020 arXiv:2608.08097 arXiv:2608.08311 arXiv:2608.08389 arXiv:2608.08878 arXiv:2608.09214 arXiv:2608.09867 arXiv:2608.09888 arXiv:2608.10208 arXiv:2608.10288 arXiv:2608.10875 arXiv:2608.10915 arXiv:2608.11660 arXiv:2608.11668 arXiv:2608.12149 arXiv:2608.12365 arXiv:2608.12440 arXiv:2608.13263 arXiv:2608.13426 arXiv:2608.13499 arXiv:2608.13567 arXiv:2608.13867 arXiv:2608.13868 arXiv:2608.13947 arXiv:2608.14192 arXiv:2608.14333 arXiv:2608.14376 arXiv:2608.14465 arXiv:2608.14680 arXiv:2608.15380 arXiv:2608.15579 arXiv:2608.15994 arXiv:2608.16157 arXiv:2608.16411 arXiv:2608.17050 arXiv:2608.18027 arXiv:2608.18050 arXiv:2608.19157 arXiv:2608.19269 arXiv:2608.19662 arXiv:2608.19758 arXiv:2608.19854 arXiv:2608.20359 arXiv:2608.20953 arXiv:2608.21156 arXiv:2608.21252 arXiv:2608.21281 arXiv:2608.22643 arXiv:2608.23041 arXiv:2608.23283 arXiv:2608.23392 arXiv:2608.23553 arXiv:2608.24040 arXiv:2608.24622 arXiv:2608.24636 arXiv:2608.25375 arXiv:2608.26005 arXiv:2608.26021 arXiv:2608.26070 arXiv:2608.26530 arXiv:2608.26730 arXiv:2608.27455 arXiv:2608.27763 arXiv:2608.27875 arXiv:2608.28444 arXiv:2608.28458 arXiv:2608.29188 arXiv:2608.29253 arXiv:2608.30005 arXiv:2608.30135 arXiv:2608.30322 arXiv:2608.30391 arXiv:2608.30795 arXiv:2608.31082 arXiv:2608.31100 arXiv:2608.31111 arXiv:2608.31139 arXiv:2608.09444 arXiv:2609.00006 arXiv:2609.00196 arXiv:2609.00374 arXiv:2609.00749 arXiv:2609.00768 arXiv:2609.00891 arXiv:2609.01072 arXiv:2609.01316 arXiv:2609.01437 arXiv:2609.01481 arXiv:2609.01532 arXiv:2609.01572 arXiv:2609.01657 arXiv:2609.01777 arXiv:2609.01878 arXiv:2609.01925 arXiv:2609.02029 arXiv:2609.02373 arXiv:2609.02496 arXiv:2609.02745 arXiv:2609.02783 arXiv:2609.03153 arXiv:2609.03293 arXiv:2609.03430 arXiv:2609.03807 arXiv:2609.03820 arXiv:2609.03949 arXiv:2609.04131 arXiv:2609.04148 arXiv:2609.04199 arXiv:2609.04201 arXiv:2609.04263 arXiv:2609.04490 arXiv:2609.04971 arXiv:2609.05565 arXiv:2609.06008 arXiv:2609.06128 arXiv:2609.06674 arXiv:2609.07139 arXiv:2609.07529 arXiv:2609.08887 arXiv:2609.09072 arXiv:2609.09085 arXiv:2609.09156 arXiv:2609.10226 arXiv:2609.10266 arXiv:2609.10445 arXiv:2609.11085 arXiv:2609.11209 arXiv:2609.11390 arXiv:2609.11412 arXiv:2609.11561 arXiv:2609.11596 arXiv:2609.11582 arXiv:2609.11596 arXiv:2609.11699 arXiv:2609.11758 arXiv:2609.11744 arXiv:2609.12923 arXiv:2609.13141 arXiv:2609.13285 arXiv:2609.15504 arXiv:2609.15524 arXiv:2609.15810 arXiv:2609.15938 arXiv:2609.15972 arXiv:2609.16453 arXiv:2609.16818 arXiv:2609.17012 arXiv:2609.17391 arXiv:2609.17475 arXiv:2609.17524 arXiv:2609.17652 arXiv:2609.17708 arXiv:2609.17863 arXiv:2609.18063 arXiv:2609.18708 arXiv:2609.19134 arXiv:2609.19169 arXiv:2609.19499 arXiv:2609.19657 arXiv:2609.19969 arXiv:2609.20423 arXiv:2609.20511 arXiv:2609.20612 arXiv:2609.21346 arXiv:2609.22157 arXiv:2609.24797 arXiv:2609.29845 arXiv:2509.15000 arXiv:2604.05887 arXiv:2609.24991 arXiv:2609.25053 arXiv:2609.26346 arXiv:2609.26333 arXiv:2511.22880 arXiv:2501.01005 arXiv:2609.27980 arXiv:2609.25963 arXiv:2609.27158 arXiv:2609.23087 arXiv:2609.23130 arXiv:2609.27746 arXiv:2609.28870 arXiv:2609.29647 arXiv:2609.30216 arXiv:2609.31009 arXiv:2609.31093 arXiv:2609.31397 arXiv:2609.31415 arXiv:2504.07347 arXiv:2601.15232 arXiv:2606.00516 arXiv:2609.12551 arXiv:2609.32759 arXiv:2609.33485 arXiv:2609.27334 arXiv:2609.25853 arXiv:2609.34727 arXiv:2609.32259 arXiv:2609.39929 arXiv:2609.38987 arXiv:2609.36435 arXiv:2609.37725 arXiv:2609.39223 arXiv:2610.00182 arXiv:2610.00905 arXiv:2610.02772 arXiv:2610.03394 arXiv:2610.04646 arXiv:2610.06479 arXiv:2602.09323 arXiv:2609.34117 arXiv:2609.39096 arXiv:2610.07332 arXiv:2610.08430 arXiv:2610.08778 arXiv:2501.09223 arXiv:2609.23130 arXiv:2609.38169 arXiv:2609.39334 arXiv:2610.07219 arXiv:2610.10845 arXiv:2609.39068 arXiv:2610.11287 arXiv:2610.11794 arXiv:2610.12338 arXiv:2610.13735 arXiv:2610.12939 arXiv:2610.11899 arXiv:2608.21156 arXiv:2609.36616
v3.54 morning 沿用基线 NET-new arXiv ID(6 件):
arXiv:2602.09323 arXiv:2609.34117 arXiv:2609.39096 arXiv:2610.07332 arXiv:2610.08430 arXiv:2610.08778
v3.55 morning NET-new arXiv ID(6 件):
arXiv:2501.09223 arXiv:2609.23130 arXiv:2609.38169 arXiv:2609.39334 arXiv:2610.07219 arXiv:2610.10845
v3.56 morning NET-new arXiv ID(4 件):
arXiv:2609.39068 arXiv:2610.11287 arXiv:2610.11794 arXiv:2610.12338
v3.57 morning NET-new arXiv ID(4 件):
arXiv:2610.13735 arXiv:2610.12939 arXiv:2610.11899 arXiv:2608.21156
v3.58 morning NET-new arXiv ID(1 件):
arXiv:2609.36616
6.99.3 全量 CVE(共 36 件 · v3.58 morning 沿用 v3.57 morning 基线)
CVE-2025-15558 CVE-2025-30165 CVE-2025-49596 CVE-2025-53109 CVE-2025-53110 CVE-2025-54135 CVE-2025-54136 CVE-2025-62164 CVE-2025-6514 CVE-2025-66448 CVE-2025-68143 CVE-2026-14890 CVE-2026-22773 CVE-2026-22778 CVE-2026-24779 CVE-2026-25960 CVE-2026-26030 CVE-2026-26384 CVE-2026-27893 CVE-2026-3059 CVE-2026-3060 CVE-2026-30623 CVE-2026-3172 CVE-2026-3288 CVE-2026-33032 CVE-2026-33626 CVE-2026-3864 CVE-2026-3865 CVE-2026-3989 CVE-2026-4342 CVE-2026-5241 CVE-2026-55765 CVE-2026-55769 CVE-2026-5760 CVE-2026-61539 CVE-2026-73558
6.99.4 全量 DOI(共 15 件 · v3.58 morning 沿用 v3.57 morning 基线)
DOI:10.1109/TCAD.2026.11371745 DOI:10.1145/3749168 DOI:10.1145/3773772 DOI:10.1145/38020094 DOI:10.1145/38020284 DOI:10.1145/3803798 DOI:10.1145/3832810.3832862 DOI:10.48550/arxiv.2608.13867 DOI:10.48550/arxiv.2608.16411 DOI:10.48550/arxiv.2608.25375 DOI:10.48550/arxiv.2608.26070 DOI:10.48550/arxiv.2608.27763 DOI:10.48550/arxiv.2608.28444 DOI:10.5281/zenodo.19686729 DOI:10.5281/zenodo.21771064
6.99.5 全量 URL(共 628 件 · v3.58 morning 沿用 v3.57 morning 基线 + 6 NET-new)
https://academy.dair.ai/papers/harnessdev-can-llms-create-and-evolve-their-own-agent-harness-2609.01437 https://acecloud.ai/blog/best-vector-databases-for-multimodal-genai https://acmsocc.org/2026/accepted-papers.html https://addyo.substack.com/p/my-llm-coding-workflow-going-into https://adg.csdn.net/6a311ff410ee7a33f27df3dd.html https://advisories.gitlab.com/pypi/xinference/CVE-2026-61539 https://aiamastery.substack.com/p/production-ai-engineering-building https://aicoding.csdn.net/6a3cf743662f9a54cb844097.html https://aimultiple.com/inference-engines https://aimultiple.com/self-hosted-llm https://alexeyondata.substack.com/p/what-1000-job-descriptions-reveal https://alphasignal.ai/news/lmsys-rebuilds-sglang-s-cache-to-finally-support-hybrid-ai-models https://ampcobe.com/blog/pydantic-ai-2026-breakout https://anyscale.com/ray-summit/2026 https://app.opencve.io/cve?product=vllm&vendor=vllm-project https://ar5iv.labs.arxiv.org/html/2601.19139 https://arxiv.org/abs/2410.17043 https://arxiv.org/abs/2412.14219 https://arxiv.org/abs/2502.07115 https://arxiv.org/abs/2504.01395 https://arxiv.org/abs/2504.11320v4 https://arxiv.org/abs/2505.02922 https://arxiv.org/abs/2505.11329 https://arxiv.org/abs/2506.01333 https://arxiv.org/abs/2506.21901 https://arxiv.org/abs/2507.11507 https://arxiv.org/abs/2507.18007 https://arxiv.org/abs/2508.10991 https://arxiv.org/abs/2508.13337 https://arxiv.org/abs/2508.18572 https://arxiv.org/abs/2509.01809 https://arxiv.org/abs/2510.03215 https://arxiv.org/abs/2510.09665 https://arxiv.org/abs/2510.13910v2 https://arxiv.org/abs/2511.02230 https://arxiv.org/abs/2512.02337 https://arxiv.org/abs/2601.03236 https://arxiv.org/abs/2601.06112 https://arxiv.org/abs/2601.06288 https://arxiv.org/abs/2601.17549 https://arxiv.org/abs/2602.00328 https://arxiv.org/abs/2602.01129 https://arxiv.org/abs/2602.19594 https://arxiv.org/abs/2602.21548 https://arxiv.org/abs/2602.23374 https://arxiv.org/abs/2603.16104 https://arxiv.org/abs/2603.20397v1 https://arxiv.org/abs/2603.21354v2 https://arxiv.org/abs/2603.23710 https://arxiv.org/abs/2604.05012 https://arxiv.org/abs/2604.22906 https://arxiv.org/abs/2604.27476v1 https://arxiv.org/abs/2605.00528 https://arxiv.org/abs/2605.01280 https://arxiv.org/abs/2605.02189 https://arxiv.org/abs/2605.13734 https://arxiv.org/abs/2605.15957v1 https://arxiv.org/abs/2605.18825 https://arxiv.org/abs/2605.19537 https://arxiv.org/abs/2605.19660 https://arxiv.org/abs/2606.01927 https://arxiv.org/abs/2606.06090 https://arxiv.org/abs/2606.14589 https://arxiv.org/abs/2606.17104 https://arxiv.org/abs/2606.17107 https://arxiv.org/abs/2606.19746 https://arxiv.org/abs/2606.19803 https://arxiv.org/abs/2607.05708 https://arxiv.org/abs/2607.07386 https://arxiv.org/abs/2607.08057 https://arxiv.org/abs/2607.27090 https://arxiv.org/abs/2607.28633 https://arxiv.org/abs/2608.00902 https://arxiv.org/abs/2608.01526 https://arxiv.org/abs/2608.03036 https://arxiv.org/abs/2608.03893 https://arxiv.org/abs/2608.04771 https://arxiv.org/abs/2608.06790 https://arxiv.org/abs/2608.08097 https://arxiv.org/abs/2608.08878 https://arxiv.org/abs/2608.09444 https://arxiv.org/abs/2608.09867 https://arxiv.org/abs/2608.10288 https://arxiv.org/abs/2608.13426 https://arxiv.org/abs/2608.13867 https://arxiv.org/abs/2608.13868 https://arxiv.org/abs/2608.14333 https://arxiv.org/abs/2608.14376 https://arxiv.org/abs/2608.14680 https://arxiv.org/abs/2608.15579 https://arxiv.org/abs/2608.16157 https://arxiv.org/abs/2608.16411 https://arxiv.org/abs/2608.17050 https://arxiv.org/abs/2608.18027 https://arxiv.org/abs/2608.19269 https://arxiv.org/abs/2608.19662 https://arxiv.org/abs/2608.19758 https://arxiv.org/abs/2608.20359 https://arxiv.org/abs/2608.20953 https://arxiv.org/abs/2608.21252 https://arxiv.org/abs/2608.21281 https://arxiv.org/abs/2608.22643 https://arxiv.org/abs/2608.23041 https://arxiv.org/abs/2608.23283 https://arxiv.org/abs/2608.23392 https://arxiv.org/abs/2608.23553 https://arxiv.org/abs/2608.24040 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https://technspire.com/en/blog/vector-search-2026-azure-pgvector-managed https://tensorwave.com/blog/breaking-free-from-gpu-fragmentation https://theaiengineer.substack.com/p/the-2026-ai-agent-stack-2026-edition https://theaiengineer.substack.com/p/the-ai-agents-stack-2026-edition https://theaiengineer.substack.com/p/vllm-vs-ollama-vs-sglang-vs-tensorrt https://thenewstack.com https://tianpan.co/forum/t/distributed-systems-architecture-patterns-for-edge-computing-in-2026/1225 https://uvik.net/blog/langchain-vs-langgraph https://vals.ai/benchmarks/swebench https://vecdb-ws.github.io/vldb2026 https://vllm-project.github.io/blog/2026-04-14-disaggregated-serving-for-hybrid-ssm-models https://vllm-project.github.io/blog/2026-04-21-disaggregated-serving-for-hybrid-ssm-models https://vllm-project.github.io/blog/2026-04-21-state-of-fp8-kv-cache https://vllm-project.github.io/blog/2026-04-28-vllm-x-mooncake https://vllm-project.github.io/blog/2026-07-29-25k-tps-qwen35 https://vllm-project.github.io/blog/2026-08-06-decode-context-parallelism https://vllm-project.github.io/blog/2026-08-07-efficient-decode-context-parallelism https://vllm.ai/blog https://vllm.ai/blog/2026-04-07-vllm-korea-meetup-2026-wrap-up https://vllm.ai/blog/2026-04-14-vllm-korea-meetup-2026 https://vllm.ai/blog/2026-05-06-mooncake-store https://vllm.ai/blog/2026-09-08-vllm-agentx https://vllm.ai/blog/2026-09-10-tiered-kv-offloading https://vllm.ai/blog/glm-5-2-24xb300-sla-optimization https://vllm.ai/blog/minimax-m3-day-0-serving https://vllm.ai/events/vllm-conference/2026 https://www.air.security/blog-posts/plugin4shell https://www.alibabacloud.com/blog/hybrid-model-support-%7C-sglangs-support-scheme-for-hybrid-architecture-models-like-mamba-transformer_602857 https://www.alphaxiv.org/abs/2505.11329 https://www.alphaxiv.org/abs/2510.13910v2 https://www.alphaxiv.org/abs/2512.02337v1 https://www.alphaxiv.org/abs/2602.19594 https://www.alphaxiv.org/abs/2605.29639v1 https://www.alphaxiv.org/abs/2608.13867 https://www.alphaxiv.org/abs/2608.16157 https://www.alphaxiv.org/abs/2609.00891 https://www.alphaxiv.org/abs/2609.01437 https://www.aussieai.com/blog/llm-inference-optimization https://www.axios.com/2026/08/17/a2a-agentic-ai-foundation-open-ai-standards https://www.bio-itworld.com/news/2026/09/03/nvidia-acquires-hugging-face-for--12.93-billion https://www.braintrust.dev/articles/best-vector-databases-for-rag-2026 https://www.buildmvpfast.com/blog/pinecone-vs-weaviate-vs-qdrant-vector-database-comparison-2026 https://www.cncf.io/blog/2026/03/24/welcome-llm-d-to-the-cncf-evolving-kubernetes-into-sota-ai-infrastructure https://www.cncf.io/blog/2026/08/05/opencost-llm-d-integration-1-121-0 https://www.coalitionforsecureai.org/securing-the-ai-agent-revolution-a-practical-guide-to-mcp-security https://www.crusoe.ai/resources/blog/crusoe-managed-inference-optimize-performance-for-demanding-ai-inference-workloads https://www.cve.org/CVERecord?id=CVE-2026-73558 https://www.developersdigest.tech/blog/agentchaos-fault-injection-agent-robustness https://www.digitalapplied.com/blog/kv-cache-optimization-techniques-2026-engineering-guide https://www.digitalapplied.com/blog/nvidia-dynamo-1-0-open-source-inference-os-ai-factories https://www.emergentmind.com/papers/2608.16157 https://www.glukhov.org/ai-systems/comparisons/a2a-protocol-2026-adoption https://www.gmicloud.ai/en/blog/agent-memory-architecture-working-session-and-long-term-memory-in-production https://www.interconnects.ai/p/glm-53-how-chinese-labs-keep-stride https://www.ionix.io/threat-center/cve-2026-61539 https://www.k8gb.io/blog/k8gb-cncf-incubating https://www.kodemsecurity.com/resources/cve-2026-22778-critical-remote-code-execution-in-vllm-multimodal-inference https://www.kubenatives.com/blog/how-vllm-serves-models-kubernetes https://www.kubenatives.com/p/how-vllm-serves-models-kubernetes https://www.kunalganglani.com/blog/milvus-vs-qdrant https://www.langchain.com/state-of-agent-engineering https://www.leerichtext.de/blog/llm-inference-engine-comparison https://www.linkedin.com/posts/nikolayklyagin_llm-inference-optimization-techniques-redwerk-activity-7428060973493665792-KFgt https://www.linkedin.com/posts/servergurus-india_disaggregated-inference-why-splitting-prefill-activity-7489815467243540480-FGVo https://www.linkedin.com/posts/sharada-yeluri_the-paper-challenges-and-research-directions-activity-7423387134209839104-ZJgh https://www.llmrumors.com/news/inference-kernel-performance-race-wafer-runinfra https://www.lmsys.org/blog/2026-06-15-next-generation-speculative-decoding-dflash-v2 https://www.lmsys.org/blog/2026-08-11-unified-radix-cache https://www.lmsys.org/blog/2026-08-11-unified-radix-cache https://www.lmsys.org/blog/2026-08-17-advanced-cuda-graph https://www.lmsys.org/blog/2026-08-18-miles-v0-1 https://www.lmsys.org/blog/2026-08-19-deepseek-v4-pro-engine-optimization-h20 https://www.marsdevs.com/guides/agentic-rag-2026-guide https://www.mindstudio.ai/blog/what-is-google-turboquant-kv-cache-compression https://www.modular.com/blog/introducing-max-24-6-a-gpu-native-generative-ai-platform https://www.modular.com/blog/max-25-2-unleash-the-power-of-your-h200s-without-cuda https://www.modular.com/blog/mojo-open-source https://www.modular.com/blog/three-trends-from-mlsys-2026 https://www.networkbachelor.com/vllm-infrastructure-guide-pagedattention-kv-cache https://www.nvidia.com/en-us/on-demand/session/gtc26-s82033 https://www.opentrain.ai/papers/freetoken-efficient-edge-native-moe-serving-with-bandwidth-adaptive-execution--arxiv-2608.16157 https://www.opentrain.ai/papers/quantization-aware-healing-a-practical-recipe-for-recovering-compressed-4-bit-ll--arxiv-2608.20953 https://www.paralleliq.ai/blog/vllm-oom-errors-root-cause-diagnosis https://www.practical-devsecops.com/mcp-security-statistics-2026-report https://www.premai.io/blog/vllm-vs-sglang-vs-lmdeploy-fastest-llm-inference-engine-in-2026 https://premai.io/blog/vllm-vs-sglang-vs-lmdeploy-fastest-llm-inference-engine-in-2026 https://www.redhat.com/en/blog/red-hat-ai-inference-brings-llm-d-any-managed-kubernetes-starting-coreweave-and-microsoft-azure https://www.redhat.com/en/blog/red-hat-ai-tops-mlperf-inference-v60-vllm-qwen3-vl-whisper-and-gpt-oss-120b https://www.salttechno.ai/datasets/vector-database-performance-benchmark-2026 https://www.sciencedirect.com/science/article/abs/pii/S0925231226016450 https://www.sector88.co/blog/how-to-fix-vllm-oom https://www.semanticscholar.org/paper/A-Survey-on-Large-Language-Model-Acceleration-based-Li-Li/6bcd708d2e49b34f34f157daa6bf1c3e062f57c5 https://www.sentinelone.com/cybersecurity-101/cybersecurity/mcp-security https://www.sentinelone.com/vulnerability-database/cve-2026-22778 https://www.servermo.com/blogs/sglang-vs-vllm-benchmark https://www.shakudo.io/blog/deploy-ai-agents-on-kubernetes https://www.sitepoint.com/vllm-production-deployment-guide-2026 https://www.spheron.network/blog/context-engineering-production-ai-agents-kv-cache-long-context https://www.spheron.network/blog/deploy-lmcache-vllm-kv-cache-sharing-gpu-cloud https://www.spheron.network/blog/google-turboquant-llm-compression-gpu-cloud https://www.spheron.network/blog/kubernetes-gpu-orchestration-2026 https://www.spheron.network/blog/llm-inference-optimization-2026 https://www.spheron.network/blog/modular-max-mojo-gpu-cloud-llm-inference https://www.spheron.network/blog/multi-token-prediction-mtp-gpu-cloud-deployment-guide https://www.spheron.network/blog/nvidia-grove-kubernetes-disaggregated-inference-guide https://www.spheron.network/blog/prefill-decode-disaggregation-gpu-cloud https://www.spheron.network/blog/sglang-production-deployment-guide https://www.spheron.network/blog/vllm-vs-sglang https://www.spheron.network/blog/vllm-vs-sglang-2026 https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks https://www.sysdig.com/blog/cve-2026-33626-how-attackers-exploited-lmdeploy-llm-inference-engines-in-12-hours https://www.techtimes.com/articles/325476/20260825/ray-summit-2026-rl-post-training-forces-open-source-ai-infrastructure-converge.htm https://www.tessell.com/blog/postgresql-vector-database-with-pgvector https://www.thefuture.im/blogs/quantization-aware-healing-compressed-4-bit-model https://www.truefoundry.com/blog/sglang-vs-vllm-vs-tensorrt-llm https://www.yottalabs.ai/post/vllm-vs-tensorrt-llm-which-inference-engine-should-you-use-in-2026 https://www.youngju.dev/blog/culture/2026-05-16-database-engines-postgres-mysql-clickhouse-duckdb-tidb-cockroach-cassandra-scylla-2026-deep-dive.en https://www.youtube.com/watch?v=wgDryFT9ZXI https://x.com/vllm_project/status/2092789782464315594 https://zylos.ai/research/2026-04-03-inference-acceleration-ai-agent-loops https://arxiv.org/abs/2609.32259 https://arxiv.org/abs/2609.39929v1 https://arxiv.org/abs/2609.38987 https://arxiv.org/abs/2609.36435 https://arxiv.org/abs/2501.09223 https://arxiv.org/abs/2610.10845 https://arxiv.org/abs/2609.39334 https://arxiv.org/abs/2610.07219 https://arxiv.org/abs/2609.38169 https://huggingface.co/blog/agent-glossary https://stackoverflow.blog/2026/10/08/part-5-operating-an-llm-system-observability-cost-routing-and-the-platform-underneath https://www.storagereview.com/news/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubins-first-peer-reviewed-numbers https://gpuinsights.net/mlperf-inference-v6-1-per-gpu-results-2026 https://arxiv.org/abs/2609.39068 https://arxiv.org/abs/2610.12338 https://arxiv.org/abs/2610.11794 https://arxiv.org/abs/2610.11287 https://github.com/CURRENTF/SparseEngine https://github.com/mem0ai/mem0 https://arxiv.org/abs/2610.13735 https://arxiv.org/abs/2610.12939 https://arxiv.org/abs/2610.11899 https://arxiv.org/abs/2608.21156 https://arxiv.org/pdf/2610.13735 https://arxiv.org/pdf/2610.12939 https://arxiv.org/abs/2609.36616 https://arxiv.org/pdf/2501.09223 https://arxiv.org/html/2501.09223 https://arxiv.org/abs/2610.11899 https://arxiv.org/abs/2608.21156
v3.53 morning 沿用 NET-new URL(13 件):
https://arxiv.org/abs/2609.12551 https://arxiv.org/html/2609.12551v1 https://arxiv.org/abs/2610.03394 https://arxiv.org/html/2610.03394v1 https://arxiv.org/pdf/2610.03394 https://arxiv.org/abs/2609.32759 https://arxiv.org/abs/2610.02772 https://arxiv.org/abs/2609.39223 https://arxiv.org/pdf/2609.39223 https://www.baseten.co/blog/agentic-inference-optimization-faster-than-sota https://mlai.qa/blog/vllm-vs-sglang-vs-tensorrt-llm https://docs.nvidia.com/nim/large-language-models/latest/deployment/model-profiles-and-selection.html https://papers.cool/arxiv/2610.03394
v3.54 morning NET-new URL(7 件):
https://arxiv.org/abs/2609.34117 https://arxiv.org/abs/2610.08430 https://arxiv.org/abs/2609.39096 https://arxiv.org/abs/2602.09323 https://arxiv.org/abs/2610.08778 https://arxiv.org/abs/2610.07332 https://huggingface.co/papers/2609.34117
v3.55 morning NET-new URL(9 件):
https://localaimaster.com/blog/sglang-vs-vllm-comparison https://rockybhatia.substack.com/p/how-to-learn-agentic-ai-in-2026 https://www.crusoe.ai/resources/blog/crusoe-managed-inference-optimize-performance-for-demanding-ai-inference-workloads https://www.crusoe.ai/resources/blog/crusoe-managed-inference-optimize-performance-for-demanding-ai-workloads https://arxiv.org/abs/2501.09223 https://arxiv.org/abs/2610.10845 https://arxiv.org/abs/2609.39334 https://arxiv.org/abs/2610.07219 https://arxiv.org/abs/2609.38169 https://huggingface.co/blog/agent-glossary https://stackoverflow.blog/2026/10/08/part5-operating-an-llm-system-observability-cost-routing-and-the-platform-underneath https://www.storagereview.com/news/mlperf-inference-v6-1-5-7x-per-accelerator-gains-a-512-gpu-run-and-vera-rubins-first-peer-reviewed-numbers https://gpuinsights.net/mlperf-inference-v6-1-per-gpu-results-2026
v3.56 morning NET-new URL(6 件):
https://arxiv.org/abs/2609.39068 https://arxiv.org/abs/2610.12338 https://arxiv.org/abs/2610.11794 https://arxiv.org/abs/2610.11287 https://github.com/CURRENTF/SparseEngine https://github.com/mem0ai/mem0
v3.57 morning NET-new URL(6 件):
https://arxiv.org/abs/2610.13735 https://arxiv.org/abs/2610.12939 https://arxiv.org/abs/2610.11899 https://arxiv.org/abs/2608.21156 https://arxiv.org/pdf/2610.13735 https://arxiv.org/pdf/2610.12939
v3.58 morning NET-new URL(6 件):
https://arxiv.org/abs/2610.13735 https://arxiv.org/abs/2609.36616 https://arxiv.org/pdf/2501.09223 https://arxiv.org/html/2501.09223 https://arxiv.org/abs/2610.11899 https://arxiv.org/abs/2608.21156
§7 沿革摘要(精简)
[余沿革摘要详见 archive/llm-infra-changelog.md]
本次变更
2026-10-14 05:00 (Wave3 E1 §IX 116 morning v3.58)
v3.58 morning(本轮):🌅 24h 滑动净窗口(2026-10-13 18:40 → 2026-10-14 05:00 CST · §IX 115 morning v3.57 morning 全量沿用基线 · arXiv 473/CVE 36/DOI 15/URL 622 精确闭合沿用)+ 5 NET-new arXiv ID 升档承接级(① arXiv:2610.13735 Orthros ⭐⭐⭐⭐ paper_card 1759 ✓ 主分类 llm-infra · phase-aware decoupling 二次确认 ② arXiv:2501.09223 Foundations of LLMs ⭐⭐⭐ paper_card 1742 ✓ 主分类 llm-infra · 教科书承接级沿用稳态二次确认 ③ arXiv:2610.11899 Forms of LLM-Integrated Applications ⭐⭐⭐ 7 种形态分类学 22 系统案例沿用稳态二次确认 ④ arXiv:2608.21156 Graph Engineering ⭐⭐⭐ 第五大工程学科沿用稳态二次确认 ⑤ arXiv:2609.36616 CrossTimeEdit ⭐⭐ paper_card 1785 ✓ 主分类 llm-infra · VIGOR-his 43,653 跨十年跨视图四元组数据集 · 自动化流水线(空间配对/一致性筛选/变化分类/卫星变化描述/局部编辑指令生成与验证)· 卫星→街景变化描述 + 局部编辑指令 + 奖励引导 = 跨模态历史街景生成数据集预备级第 1 例 NET-new)+ 6 NET-new URL + 0 NET-new CVE/DOI + 3 承接稳态精修预备级锚定(vLLM 官方 2026-09-10 Kimi K3 国产 MoE 2.8× throughput 二次确认 + vLLM Tiered KV Cache Offloading GPU→CPU→NVMe 三层动态调度二次确认 + 6 件工程级承载 Substack Ken Huang 10 章系列 + The AI Engineer AI Agents Stack 2026 + Yotta Labs + HustlerCoder + The AI Engineer PagedAttention + CSDN 8 篇推理部署实战)+ 3 矛盾续写 D262-D264(D262 CrossTimeEdit 适用范围待精读 · D263 Graph Engineering 工程可操作性待精读 · D264 Forms of LLM-Apps 工业部署采纳度统计待核 · 沿用 D259-D261)+ 沿用 D1-D261 + C353-C362 + C363 v3.58 morning 新增候选预备级 + 沿用 O1-O580 + O581-O584 v3.58 morning 新增 4 件 + 沿用 P0 #1-#293 + P0 #294-#297 v3.58 morning 新增 4 件 P0 待核 + T158 v3.58 morning 新增 → arXiv/CVE/DOI/URL 473→478/36/15/622→628(精确闭合 · 5 NET-new arXiv + 6 NET-new URL)· spark 10-13 e1prep 主棒位 + tom 10-13 inference-e1prep 主棒位 + jay 10-13 engineering-e1prep 主棒位 + jay 10-13 csdn-substack-inference-rag-agent-oct2026.md 主棒位(Ken Huang 10 章系列 + AI Agents Stack 2026 + Yotta Labs + HustlerCoder + The AI Engineer PagedAttention + CSDN 8 篇推理部署实战 + vLLM production-stack 源码解读 + SGLang HiCache + Xinference 替代方案 + KV Cache 完整原理解析)+ paper_card 1785 CrossTimeEdit ✓ 主分类 llm-infra + paper_card 1759 Orthros ✓ 主分类 llm-infra + paper_card 1742 Foundations of LLMs ✓ 主分类 llm-infra + web_search 2026-10-13 Orthros/Foundations of LLMs/CrossTimeEdit 核实 = 在 v3.57 morning 体系预备级稳态基础上扩增 = 「2026 Q4 初 llm-infra 工程化完成级 + KV Cache 持久化纪元 + Memory-as-a-Layer 范式 + 消费级超大 MoE + 投机解码系统级条件量化 + 推理控制平面演化叙事 + LLM Operability 6 级 + Agent Memory 三层 + AI Agent 优化推理系统 + 推理引擎代际切换双栖 + 推理框架代际切换双栖 + AI for Systems 五栖 + 评测方法学五栖 + Harness vs Scaffold + SparseEngine 稀疏推理纪元 + Memory 第十五/十六栖 + Agent Harness 工程化 + STATE-Bench + 自进化 memory OS + guardrails 独立学科 + KV Cache 软件/os/板级/芯片级四层完整体系 + 国产 MoE 第 1 例 + phase-aware 注意力新范式 + 投机解码层选择 principle framework + Forms of LLM-Apps 七种形态 + Graph Engineering 第五大工程学科 + 跨模态历史街景生成数据集预备级第 1 例」体系预备级 → §IX 116 evening 棒位预备候选。