新溪-gordon
V2025.06
书籍
书
2021年看的书
图解密码技术
Deep learning with Python
网络是怎么连接的
微服务设计
微服务设计原理与架构
微服务治理: 体系、架构及实践
TCP/IP ILLustrated Volume 1: The Protocols
SRE
The Site Reliability Workbook
Bitcoin and Cryptocurrency Technologies
其他
详情
编码实践
设计模式
工程实践
领域驱动设计
产品与需求
开发文化
管理
科幻小说
其他相关
要看的书
经典书
IT Core
编译原理
组成原理
IT 设计
管理
AI 相关
推荐系统
IT相关
区块链
统计
网络
实时协同
方法论
小说
科幻小说science fiction
书籍来源
极客来源
数学基础
语言经典
C 语言
计算机经典
计算机语言设计
其他
ai
Microsoft: AI-For-Beginners
I Introduction to AI
II Symbolic AI
III Introduction to Neural Networks
IV Computer Vision
V. NLP
VI. Other
VII. Ethics
Microsoft: Machine Learning for Beginners
1. Introduction
2. Regression
3. Web-App
4. Classification
5. Clustering
6. NLP
7. TimeSeries
8. Reinforcement
9. Real-World
Microsoft: Generative AI For Beginners
第一章: 简介
第二章: 不同的 LLMs对比
第三章: AI安全
第四章: 提示工程基础
第五章: 提示工程进阶
第八章: 创建搜索应用
Getting Started with OpenCV
Microsoft Learn: Introduction to PyTorch
简介
Microsoft Learn: Introduction to NLP with PyTorch
简介
2. Representing text as Tensors
3. Bag-of-Words and TF-IDF representations
4. Embeddings
HuggingFace: Learn
Kaggle Learn
Intermediate Machine Learning
Intro to Deep Learning
参考
scikit-learn 1.3.2
Build a Large Language Model (From Scratch)
1. Understanding LLM
2. Working with Text Data
3. Coding Attention Mechanisms
4 Implementing a GPT model from Scratch To Generate Text
5 Pretraining on Unlabeled Data
6 Fine-tuning for classification
7 Fine-tuning to follow instructions
Appendix A. Introduction to PyTorch
Appendix B. References and Further Reading
Appendix C. Exercise Solutions
Appendix D. Adding Bells and Whistles to the Training Loop
appendix E Parameter-efficient fine- tuning with LoRA
其他
动手学深度学习(Dive into Deep Learning)
前言
Part 1: Basics and Preliminaries
Part 2: Modern Deep Learning Techniques
Part 3: Scalability, Efficiency, and Applications
架构相关
微服务设计原理与架构
微服务建模
服务拆分与集成
微服务架构关键要素
参考
领域驱动设计精粹
第 2 章 运用限界上下文与通用语言进行战略设计
第 3 章 运用子域进行战略设计
第 4 章 运用上下文映射进行战略设计
第 5 章 运用聚合进行战术设计
第 6 章 运用领域事件进行战术设计
第 7 章 加速和管理工具
实现领域驱动设计
领域驱动设计
优化相关
性能之巅
书评
协议相关
UNIX 网络编程卷1
第1章 简介
第2章 传输层: TCP, UDP和SCTP
第3章 套接字编程简介
第4章 基本TCP套接字编程
第5章 TCP客户/服务器程序示例
第6章 I/O复用: select和poll函数
第7章 套接字选项
第8章 基本UDP套接字编程
第9章 基本SCTP套接字编程
第11章 名字与地址转换
第12章 IPv4与IPv6的互操作性
第13章 守护进程和inetd超级服务器
第14章 高级I/O函数
第15章 Unix域协议
第16章 非阻塞式I/O
第17章 ioctl操作
第18章 路由套接字
第19章 密钥管理套接字
第20章 广播
第21章 多播
第22章 高级UDP套接字编程
第23章 高级SCTP套接字编程
第24章 带外数据
第25章 信号驱动式I/O
第26章 线程
第27章 IP选项
第28章 原始套接字
第29章 数据链路访问
第30章 客户/服务器程序设计范式
附录A IPv4, IPv6, ICMPv4和ICMPv6
其他
TCP/IP 详情-卷1
第1章 概 述
第2章 链 路 层
第3章 IP:网际协议
其他
TCP:IP 详情卷1-第2版
第1章 概 述
边缘相关
雾计算与边缘计算: 原理及范式
边缘计算入门 20 课
第 01 课: 边缘计算深度调研
第 02 课: 云走向边缘, 云将无处不在
第 03 课: 信通院-边缘计算发展现状与趋势展望
第 04 课: EdgeRec: 边缘计算在推荐系统中的应用
第 05 课: 阿里云边缘云原生应用实践应用实践
第 06 课: KubeEdge 子项目 Sedna 0.1 发布
第 07 课: 用 SuperEdge 统管边缘设备和机器
第 08 课: 如何使用 k8s 管理 10 万边缘节点
第 09 课: 边云协同-打通 AI 最后一公里
第 10 课: 用 edgeadm 一键安装边缘 K8s 集群
第 11 课: 基于 KubeEdge 实现 10086 客服云边协同平台
第 12 课: Volcano 架构设计与原理
第 13 课: 一文读懂 SuperEdge 的云边隧道
第 14 课: 打破内网壁垒-从云端一次添加上千边缘节点
第 15 课: 一文读懂 SuperEdge 边缘容器架构与原理
第 16 课: 2020 十大边缘计算开源项目
第 17 课: Addon SuperEdge 让原生 K8s 管理边缘应用
第 18 课: SuperEdge 云边隧道新特性
第 19 课: 《深入理解边缘计算》
第 20 课: FabEdge 边缘网络方案
第 21 课: 边缘计算云原生开源方案选型比较
参考
边缘计算方法与工程实践
第1章 边缘计算综述
第2章 边缘计算基础资源架构技术
第3章 边缘计算软件架构
第4章 边缘计算安全管理
第5章 边缘计算应用案例
第6章 边缘计算发展展望
物联网相关
图解物联网
第1章 物联网的基础知识
第 2 章 物联网的架构
第 3 章 物联网设备
第 4 章 先进的感测技术
第 5 章 物联网服务的系统开发
第 6 章 物联网与数据分析
第 7 章 物联网与可穿戴设备
第 8 章 物联网与机器人
物联网设计
第一部分 原型阶段
自己动手设计物联网
编程语言相关
C 程序设计
极客时间
[重要]编程基础
深入浅出计算机组成原理
指令和运算
处理器
书籍
网络编程实战
第一模块: 基础篇
第二模块: 提高篇
第三模块: 性能篇
第四模块: 实战篇
结束语
趣谈 Linux 操作系统
第二部分 系统初始化 (4 讲)
第三部分 进程管理 (10 讲)
第四部分 内存管理 (7 讲)
第五部分 文件系统 (4 讲)
第六部分 输入输出系统 (5 讲)
第七部分 进程间通信 (7 讲)
第八部分 网络系统 (7 讲)
第九部分 虚拟化 (7 讲)
第十部分 容器化 (4 讲)
实战串讲篇 (9 讲)
学习攻略
编译原理实战课
语法分析
语义分析
运行时机制
中间代码 IR
代码优化
代码生成
解析树和 AST 的区别
Golang
Erlang
并发
元编程-Meta-Programming
泛型
函数式编程
远程办公
如何学习
收集
参考
操作系统实战
整体设计
程序的基石：硬件
同步原语
参考
手把手带你写一门编程语言
开篇
词法分析
语法分析
语义分析
计算机基础实战课
课程设计
01以史为鉴 (3讲)
02硬件-芯片(手写mini CPU) (9讲)
03环境准备 (2讲)
04语言与指令 (9讲)
05应用与内存 (8讲)
06国庆策划 (3讲)
07IO与文件 (6讲)
08综合应用 (6讲)
09结束语 (4讲)
10技术雷达 (5讲)
架构相关
左耳听风-陈皓
程序员如何用技术变现
05 _ 何为技术领导力
06 _ 如何才能拥有技术领导力
许式伟的架构课
编程语言
操作系统
外置存储
需求分析
详细设计
导致故障的因素
软件架构
架构设计文档
软件质量管理
软件工程
架构设计的优劣
参考
软件工程之美
软件工程之美summary
软件工程之美
基础理论 (9 讲)
需求分析篇
系统设计篇
开发编码篇 (7 讲)
设计模式之美
DDD 实战课
开篇词
基础篇 (5 讲)
02进阶篇 (6 讲)
03实战篇 (10 讲)
结束语
架构实战案例解析
01概述篇 (2 讲)
02业务架构篇 (9 讲)
03技术架构篇 (9 讲)
总结篇 (2 讲)
乔新亮的 CTO 成长复盘
00开篇词
01对个人认知的复盘 (6 讲)
02对管理工作的复盘 (10 讲)
03对专业成长的复盘 (10 讲)
结束语
如何落地业务建模
开篇词
旧约: “前云时代” 的领域驱动设计 (11 讲)
深度答疑专题 (4 讲)
新约: 云时代的业务建模 (2 讲)
郭东白的架构课
我的收获
课程设计
00开篇词|没有战略意图,就成不了一个顶尖的架构师
01模块一:生存法则 (15 讲)
02模块二:创造价值 (21讲)
03模块三:职业成长 (9讲)
04模块四:思考力 (11讲)
05结束语
06加餐
李智慧 · 高并发架构实战课
安全
实用密码学
00开篇词 _ 人人都要会点密码学
01 | 学习密码学有什么用
02 | 单向散列函数: 如何保证信息完整性
03 | 如何设置合适的安全强度
04 | 选择哈希算法应该考虑哪些因素
05|如何有效避免长度延展攻击
06|对称密钥: 如何保护私密数据
07 | 怎么选择对称密钥算法
09 | 为什么ECB模式不安全
10 | 怎么防止数据重放攻击CBC
11 | 怎么利用解密端攻击
12 | 怎么利用加密端攻击
13 | 如何防止数据被调包
14 | 加密数据能够自我验证吗
15 | AEAD 有哪些安全陷阱
16 | 为什么说随机数都是骗人的
17 | 加密密钥是怎么来的
18 | 如何管理对称密钥
19|量子时代,你准备好了吗
结束语
Web 安全攻防实战
1. 前端基础
2. Web安全之后端安全
测试相关
接口测试入门课
点评
开篇词 | 把接口测试这件小事做深/做透
01 | 基础: 跳出细节看全局
02 | 方法论: 没有任何文档, 怎么才能快速了解接口的信息
程序员的测试课
开篇词
基础篇 (11 讲)
应用篇 (5 讲)
03扩展篇 (2 讲)
结束语
软件测试 52 讲
01测试基础知识篇 (11讲)
02GUI自动化测试篇 (10讲)
03API自动化测试篇 (3讲)
04代码测试篇 (3讲)
05性能测试篇 (7讲)
06测试数据准备篇 (4讲)
07测试基础架构篇 (4讲)
08测试新技术篇 (5讲)
09测试人员的互联网架构核心知识篇 (5讲)
10特别放送篇 (8讲)
云原生
容器实战高手课
Namespace
Cgroups
Linux Kernel
Load Average
Memory Cgroup
存储
Network
容器安全
k8s
思考
管理&长成
跟着高手学复盘
01基础概念篇 (3 讲)
02实操流程篇 (9 讲)
03实战案例篇 (7 讲)
结束语
春节荐书
程序员进阶攻略
启程
修炼
修行
徘徊
寻路
蜕变
10x 程序员工作法
思考框架
四个思考原则
总结
一. 以终为始
二. 任务分解
三. 沟通反馈
四. 自动化
五. 综合运用
好书推荐
提问
大厂晋升指南
晋升原则
晋升逻辑
能力模型
职级档次
P7
P8
P9
P10/P11
面评技巧-PPT框架
面评技巧-PPT 讲解
面评技巧-PPT 答辩
面评技巧-注意点
面评技巧-技术大会
面评技巧-其他
学习方法-指导原则
学习方法-找时间：海绵学习法
学习方法-学什么：三段分解法
学习方法-怎么学
学习方法-保证效果
做事方法-总
做事方法-KPI&OKR
做事方法-3C 方案设计法
做事方法-PDCA执行法
做事方法-5W根因分析法
做事方法-5S 问题处理法
做事方法-4D 总结法
做事方法-金字塔汇报法
做事方法-四线复盘法
专项提升-业务
专项提升-业务:5W1H8C1D 分析法
专项提升-业务:AARRR 漏斗模型
专项提升-业务:宝洁战略模型
专项提升-管理
专项提升-管理:管理四象限
专项提升-管理:管理五模式
别人的心得
其他
10000小时定律
领域分层图
参考
其他
数据分析
数据分析实战 45 讲
思维导图
开篇词 | 你为什么需要数据分析能力
01基础篇 (16 讲)
02算法篇 (20 讲)
03实战篇 (7 讲)
04工作篇 (2 讲)
结束语
数据分析思维课
思维导图
00开篇词 (2 讲)
01数据分析基础 (11 讲)
02数据算法基础 (9 讲)
03如何用数据说话 (6 讲)
04分析工具 (5 讲)
05特别放送 (6 讲)
其他
AI 相关
人工智能基础课
数学基础
机器学习
推荐系统三十六式
内容推荐
近邻推荐
矩阵分解
个人成长
AI 大模型之美
课前必读 (2 讲)
基础知识篇: 探索大型语言模型的能力 (8 讲)
实战提高篇一: 利用NLP技术完成高级任务 (10讲)
实战提高篇(二) 大型语音与图像模型的应用 (9讲)
扩展
机器学习 40 讲
01机器学习概观 (10 讲)
02统计机器学习模型 (18 讲)
03概率图模型 (14 讲)
PyTorch 深度学习实战
开篇词 | 如何高效入门 PyTorch
01基础篇 (5 讲)
02模型训练篇 (12 讲)
03实战篇 (9 讲)
加餐| 基础模型
结束语| 人生充满选择, 选择与努力同样重要
零基础 GPT 应用入门课
开篇词
基础速通 (3讲)
黄金密钥 (7讲)
综合实战 (6讲)
AI 大模型系统实战
热身篇 (4讲)
架构基础篇 (6讲)
技术原理篇 (5讲)
AI 绘画核心技术与实战
开篇词 (2讲)
热身篇:AI 绘画初体验 (4讲)
基础篇:AI 绘画原理揭秘 (9讲)
进阶篇:从 DALL-E 2 到 Stable Diffusion (5讲)
综合演练篇:AI 绘画高手养成计划 (8讲)
零基础实战机器学习
08 | 模型优化1: 怎么用特征工程提高模型效率
区块链
说透区块链
数字人民币
书籍
代码精进
代码之丑
开篇词
13 类典型坏味道 (13 讲)
延伸阅读 (4 讲)
参考
软件设计之美
软件设计之美
代码精进之路
01第一模块: 代码 “规范” 篇 (16 讲)
02第二模块: 代码 “经济” 篇 (14 讲)
03第三模块: 代码 “安全” 篇 (14 讲)
编程语言
Go 语言核心 36 讲
TonyBai Go语言第一课
课程设计
00开篇
01入门篇: 勤加练手 (7 讲)
02基础篇: “脑勤” 多理解 (20 讲)
03核心篇: “脑勤 +” 洞彻核心 (5 讲)
04实战篇: 打通“最后一公里” (4讲)
大咖助阵
加餐
泛型
Python 核心技术与实战
产品&运营
梁宁-产品思维 30 讲
发刊词
模块一: 同理心
模块二: 机会判断
模块三: 系统能力
模块四: 用户体验
模块五: 创新模式
产品世界观
彩蛋
参考
面试
后端工程师的高阶面经
开篇词
01微服务架构 (10讲)
数据库与MySQL (13讲)
消息队列 (10讲)
缓存 (9讲)
NoSQL (5讲)
结束语
软件工程
说透敏捷
开篇词
原理篇 (2 讲)
实战篇 (4 讲)
策略篇 (2 讲)
管理篇 (2 讲)
结束语
其它
互联网人的英语私教课
KSA
独立主格结构
介词
收集
关键英语
并列句 VS 复杂句
单词
英语谚语
常用短语
口语专用词汇
好的英文网站
其他
会不会阅读
词汇学习
paraphrase
动词
其他
参考
从 0 打造音视频直播系统
WebRTC 1 对 1 通话 (23 讲)
WebRTC 多人音视频实时通话 (7 讲)
支持上万人同时在线的直播系统 (8 讲)
其他
快手·音视频技术入门课
开篇基础 (4讲)
流媒体技术速成 (5讲)
FFmpeg API 应用 (4讲)
FFmpeg 社区“玩法” (2讲)
结束语 | 音视频技术更宠爱脚踏实地的人
攻克视频技术
图像基础和前处理 (3 讲)
视频编码 (5讲)
参考
评论
搞定音频技术
音频基础 (4 讲)
02音频降噪 (2 讲)
03回声消除 (2 讲)
04音频网络传输 (3 讲)
05空间音频 (2 讲)
06音频特效生成与算法 (3 讲)
专利写作第一课
开篇词 | 写专利, 将是知识工作者的核心产出
01 _ 为什么我推荐互联网人要积极写专利
02 _ 奖金是专利写作中最不值得一提的事儿
03 _ 找到KeyPerson利益点, 提升专利通过率
04 _ 像写PRD一样, 撰写专利交底书1
05 _ 像写PRD一样, 撰写专利交底书2
06 _ 如何把常见的生活问题变成专利(案例-节假日不响起闹钟)
07 _ 专利创新的步伐不必迈得特别大
08 _ 那些异想天开的专利是怎么诞生的
答疑 _ 专利申请十大常见问题
WebAssembly 入门课
课前必读
01核心原理篇 (6 讲)
02应用篇 (6 讲)
03实战篇 (6 讲)
其他
Matter 协议
Matter Core
Chapter 1. Introduction
Chapter 2. Architecture
2.1. Overview
2.2. Layered Architecture
2.3. Network Topology
2.4. Scoped names
2.5. Identifiers
2.6. Device identity
2.7. Security
2.8. Device Commissioning
2.9. Sleepy End Device (SED)
2.10. Data Model Root
2.11. Stack Limits
2.12. List of Provisional Items
Chapter 3. Cryptographic Primitives
Chapter 4. Secure Channel
4.1. General Description
4.2. IPv6 Reachability
4.3. Discovery
4.4. Message Frame Format
4.5. Message Counters
4.6. Message Processing
4.7. Message Security
4.8. Message Privacy
4.9. Message Exchanges
4.10. Secure Channel Protocol
4.11. Message Reliability Protocol (MRP)
4.12. Unicast Communication
4.13. Session Establishment
4.14. Group Communication
4.15. Group Key Management
4.16. Message Counter Synchronization Protocol(MCSP)
4.17. Bluetooth Transport Protocol (BTP)
Chapter 5. Commissioning
5.1. Onboarding Payload
5.2. Initiating Commissioning
5.3. User Directed Commissioning
5.4. Device Discovery
5.5. Commissioning Flows
5.6. Administrator Assisted Commissioning Flows
5.7. Device Commissioning Flows
5.8. In-field Upgrade to Matter
Chapter 6. Device Attestation and Operational Credentials
6.1. Common Conventions
6.2. Device Attestation
6.3. Certification Declaration
6.4. Node Operational Credentials Specification
6.5. Operational Certificate Encoding
6.6. Access Control
Chapter 7. Data Model Specification
7.1. Practical Information
7.2. Data Qualities
7.3. Conformance
7.4. Element
7.5. Fabric
7.6. Access
7.7. Other Qualities
7.8. Node
7.9. Endpoint
7.10. Cluster
7.11. Command
7.12. Attribute
7.13. Global Elements
7.14. Event
7.15. Device Type
7.16. Non-Standard
7.17. Data Field
7.18. Data Types
7.19. Manufacturer Specific Extensions
Chapter 8. Interaction Model Specification
8.1. Practical Information
8.2. Concepts
8.3. Status and Interaction
8.4. Read Interaction
8.5. Subscribe Interaction
8.6. Report Transaction
8.7. Write Interaction
8.8. Invoke Interaction
8.9. Common Action Information Blocks and Paths
8.10. Status Codes
Chapter 9. System Model Specification
9.1. Practical Information
9.2. Endpoint Composition
9.3. Interaction Model Relationships
9.4. Binding Relationship
9.5. Descriptor Cluster
9.6. Binding Cluster
9.7. Label Cluster
9.8. Fixed Label Cluster
9.9. User Label Cluster
9.10. Access Control Cluster
9.11. Group Relationship
9.12. Bridge for non-Matter devices
9.13. Bridged Device Basic Information Cluster
9.14. Actions Cluster
9.15. Proxy Architecture
Chapter 10. Interaction Model Encoding Specification
10.1. Overview
10.2. Messages
10.3. Data Types
10.4. Sample Cluster
10.5. Information Blocks
10.6. Message Definitions
Chapter 11. Service and Device Management
11.1. Basic Information Cluster
11.2. Group Key Management Cluster
11.3. Localization Configuration Cluster
11.4. Time Format Localization Cluster
11.5. Unit Localization Cluster
11.6. Power Source Configuration Cluster
11.7. Power Source Cluster
11.8. Network Commissioning Cluster
11.9. General Commissioning Cluster
11.10. Diagnostic Logs Cluster
11.11. General Diagnostics Cluster
11.12. Software Diagnostics Cluster
11.13. Thread Network Diagnostics Cluster
11.14. Wi-Fi Network Diagnostics Cluster
11.15. Ethernet Network Diagnostics Cluster
11.16. Time Synchronization
11.17. Node Operational Credentials Cluster
11.18. Administrator Commissioning Cluster
11.19. Over-the-Air (OTA) Software Update
11.20. Over-the-Air (OTA) Software Update File Format
11.21. Bulk Data Exchange Protocol (BDX)
11.22. Distributed Compliance Ledger
Chapter 12. Multiple Fabrics
12.1. Multiple Fabrics
Chapter 13. Security Requirements
13.2. Device vs. Node
13.4. Factory Reset
13.7. Threats and Countermeasures
Appendix A: Tag-length-value (TLV) Encoding Format
A.1. Scope & Purpose
A.2. Tags
A.9. Length Encoding
A.10. End of Container Encoding
A.11. Value Encodings
A.12. TLV Encoding Examples
Appendix B: Tag-length-value (TLV) Schema Definitions
B.1. Introduction
B.2. Definitions
B.3. Types
B.4. Pseudo-Types
B.5. Qualifiers
Appendix C: Tag-length-value (TLV) Payload Text Representation Format
C.1. Introduction
C.3. Examples
Appendix D: Status Report Messages
D.3. Message Format
Appendix E: Matter-Specific ASN.1 Object Identifiers (OIDs)
Appendix F: Cryptographic test vectors for some procedures
Appendix G: Minimal Resource Requirements
Matter协议分析
简介
算法
椭圆曲线密码学 (ECC) 原理
Bridge
Factory Data
安全
PASE
配网过程
CASE
Group
cluster
OTA
参考
chatGPT学习
Chapter 01 — Introduction Document
Chapter 02 — Architecture Document
Chapter 03 — Cryptographic Primitives Document
Chapter 04 — Secure Channel Document
Chapter 05 — Commissioning Document
Chapter 06 — Device Attestation Document
Chapter 07 — Data Model Document
Chapter 08 — Interaction Model Document
Chapter 09 — System Model Document
概述和定义
设备类型和服务类型
特征
系统模型实例
Chapter 10 — Interaction Encoding Document
概述和定义
数据类型
交互编码格式
Chapter 11 — Device Management Document
概述和定义
设备组成
设备状态
设备操作
Chapter 12 — Multiple Fabrics Document
Chapter 13 — Security Requirements Document
Appendix A: Tag-length-value (TLV) Encoding Format
Appendix B: Tag-length-value (TLV) Schema Definitions
Appendix C: Tag-length-value (TLV) Payload Text Representation Format
Appendix D: Status Report Messages
rfc
RFC791: IP: INTERNET PROTOCOL
PREFACE
1. INTRODUCTION
2. OVERVIEW
2.1. Relation to Other Protocols
2.2. Model of Operation
2.3. Function Description
2.4. Gateways
3. SPECIFICATION
3.1. Internet Header Format
3.2 Discussion
3.3 Interfaces
APPENDIX A: Examples & Scenarios
minimal data carrying internet datagram
moderate size internet datagram (452 data octets)
datagram containing options
APPENDIX B: Data Transmission Order
参考
RFC792: ICMP
参考
RFC3569: An Overview of Source-Specific Multicast (SSM)
1. Introduction
2. Terminology
Any-Source Multicast (ASM)
Source-Specific Multicast (SSM)
Source-Filtered Multicast (SFM)
3. The IGMP/PIM-SM/MSDP/MBGP Protocol Suite for ASM
4. Problems with Current Architecture
5. Source Specific Multicast (SSM): Benefits and Requirements
6. SSM Framework
6.1. Address Allocation
6.2. Session Description and Channel Discovery
6.3. SSM-Aware Applications
6.4. IGMPv3/MLDv2 Host Reporting and Querier
6.5. PIM-SSM Routing
7. Interoperability with Existing Multicast Service Models
应用
SSM示例
参考
RFC4301: Security Architecture for the IP
参考
RFC4302: IP Authentication Header
参考
RFC4303: IP Encapsulating Security Payload (ESP)
参考
RFC4693: Classless Inter-domain Routing (CIDR)
RFC3306: Unicast-Prefix-based IPv6 Multicast Addresses
IPv6 多播地址中前缀长度的取值范围
多播地址的分配规则
Abstract
1. Introduction
2. Motivation
4. Multicast Address Format
6. SSM(Source-Specific Multicast Addresses)
7. Examples
参考
RFC4007: IPv6 Scoped Address Architecture
Abstract
1. Introduction
4. Address Scope
5. Scope Zones
6. Zone Indices
7. Sending Packets
8. Receiving Packets
9. Forwarding
10. Routing
11. Textual Representation
Examples
参考
RFC4291: IP Version 6 Addressing Architecture
学习
keypoints
地址类型
地址分配
1. Introduction
2. IPv6 Addressing
2.1. Addressing Model
2.2. Text Representation of Addresses
2.3. Text Representation of Address Prefixes
2.4. Address Type Identification
2.5. Unicast Addresses
2.6. Anycast Addresses
2.7. Multicast Addresses
2.7.1. Pre-Defined Multicast Addresses
2.8. A Node’s Required Addresses
3. Security Considerations
Appendix A: Creating Modified EUI-64 Format Interface Identifiers
Links or Nodes with IEEE EUI-64 Identifiers
Links or Nodes with IEEE 802 48-bit MACs
Links with Other Kinds of Identifiers
Links without Identifiers
参考
RFC6437: IPv6 Flow Label Specification
参考
RFC7346: IPv6 Multicast Address Scopes
Abstract
1. Introduction
2. Definition of IPv6 Multicast Address Scopes (Updates RFC 4291)
3. Definition of Realm-Local Scopes
5. Definition of Realm-Local Scope for IEEE 802.15.4
参考
RFC7707: Network Reconnaissance in IPv6 Networks
参考
RFC8200 Internet Protocol, Version 6 (IPv6) Specification
1. Introduction
changes from IPv4 to IPv6
related RFC
2. Terminology
3. IPv6 Header Format
4. IPv6 Extension Headers
4.1. Extension Header Order
4.2. Options
4.3. Hop-by-Hop Options Header
4.4. Routing Header
4.5. Fragment Header
4.6. Destination Options Header
4.7. No Next Header
4.8. Defining New Extension Headers and Options
5. Packet Size Issues
6. Flow Labels
7. Traffic Classes
8. Upper-Layer Protocol Issues
8.1. Upper-Layer Checksums
8.2. Maximum Packet Lifetime
8.3. Maximum Upper-Layer Payload Size
8.4. Responding to Packets Carrying Routing Headers
9. IANA Considerations
10. Security Considerations
same with ipv4
compare with ipv4
11. References
Appendix A. Formatting Guidelines for Options
Appendix B. Changes Since RFC 2460
参考
RFC8201: Path MTU Discovery for IP version 6
参考
RFC9293: Transmission Control Protocol (TCP)
参考
RFC0768: User Datagram Protocol
参考
rfc7230: HTTP/1.1: Message Syntax and Routing
定义
hop-by-hop and end-to-end
Inbound and Outbound
head-of-line (HOL) blocking problem
ABNF语法
Abstract
1. Introduction
1.2. Syntax Notation
2. Architecture
2.1. Client/Server Messaging
2.2. Implementation Diversity
2.3. Intermediaries
2.4. Caches
2.5. Conformance and Error Handling
2.6. Protocol Versioning
2.7. Uniform Resource Identifiers
3. Message Format
3.1. Start Line
3.2. Header Fields
3.3. Message Body
3.4. Handling Incomplete Messages
4. Transfer Codings
4.1. Chunked Transfer Coding
4.2. Compression Codings
4.3. TE
4.4. Trailer
5. Message Routing
5.1. Identifying a Target Resource
5.2. Connecting Inbound
5.3. Request Target
5.4. Host
5.5. Effective Request URI
5.6. Associating a Response to a Request
5.7. Message Forwarding
6. Connection Management
6.1. Connection
6.2. Establishment
6.3. Persistence
6.4. Concurrency
6.5. Failures and Timeouts
6.6. Tear-down
6.7. Upgrade
7. ABNF List Extension: #rule
8. IANA Considerations
8.1. Header Field Registration
8.2. URI Scheme Registration
8.3. Internet Media Type Registration
8.4. Transfer Coding Registry
8.5. Content Coding Registration
8.6. Upgrade Token Registry
rfc7231: HTTP/1.1: Semantics and Content
rfc7232: HTTP/1.1: Conditional Requests
rfc7233: HTTP/1.1: Range Requests
rfc7234: HTTP/1.1: Caching
rfc7235: HTTP/1.1: Authentication
rfc9110: HTTP Semantics
1. Introduction
rfc9111: HTTP Caching
rfc9112: HTTP/1.1
RFC9000: QUIC: A UDP-Based Multiplexed and Secure Transport
参考
RFC9001: Using TLS to Secure QUIC
RFC9002: QUIC Loss Detection and Congestion Control
参考
RFC9114: HTTP/3
参考
RFC9204: QPACK: Field Compression for HTTP/3
参考
RFC1035: DOMAIN NAMES-IMPLEMENTATION AND SPECIFICATION
RFC2782: DNS SRV
RFC6762: mDNS
收集
chatGPT
规范和要求
实现和应用
安全性
性能和可扩展性
Abstract
1. Introduction
3. mDNS Names
4. Reverse Address Mapping
5. Querying
5.1. One-Shot mDNS Queries
5.2. Continuous mDNS Querying
5.3. Multiple Questions per Query
5.4. Questions Requesting Unicast Responses
5.5. Direct Unicast Queries to Port 5353
6. Responding
common
6.1. Negative Responses
6.2. Responding to Address Queries
7. Traffic Reduction
8. Probing and Announcing on Startup
9. Conflict Resolution
10. Resource Record TTL Values and Cache Coherency
11. Source Address Check
12. Special Characteristics of mDNS Domains
13. Enabling and Disabling mDNS
14. Considerations for Multiple Interfaces
15. Considerations for Multiple Responders on the Same Machine
16. mDNS Character Set
17. mDNS Message Size
18. mDNS Message Format
18.1. ID (Query Identifier)
18.2. QR (Query/Response) Bit
18.3. OPCODE
18.4. AA (Authoritative Answer) Bit
18.5. TC (Truncated) Bit
18.6. RD (Recursion Desired) Bit
18.7. RA (Recursion Available) Bit
18.8. Z (Zero) Bit
18.9. AD (Authentic Data) Bit
18.10. CD (Checking Disabled) Bit
18.11. RCODE (Response Code)
18.12. Repurposing of Top Bit of qclass in Question Section
18.13. Repurposing of Top Bit of rrclass in Resource Record Sections
18.14. Name Compression
18.5. TC (Truncated) Bit
18.6. RD (Recursion Desired) Bit
18.7. RA (Recursion Available) Bit
18.8. Z (Zero) Bit
18.9. AD (Authentic Data) Bit
18.10. CD (Checking Disabled) Bit
18.11. RCODE (Response Code)
18.12. Repurposing of Top Bit of qclass in Question Section
18.13. Repurposing of Top Bit of rrclass in Resource Record Sections
18.14. Name Compression
19. Summary of Differences between mDNS and Unicast DNS
20. IPv6 Considerations
21. Security Considerations
22. IANA Considerations
Appendix A. Design Rationale for Choice of UDP Port Number
Appendix B. Design Rationale for Not Using Hashed Multicast Addresses
Appendix C. Design Rationale for Maximum Multicast DNS Name Length
Appendix D. Benefits of Multicast Responses
Appendix E. Design Rationale for Encoding Negative Responses
Appendix F. Use of UTF-8
Appendix G. Private DNS Namespaces
Appendix H. Deployment History
参考
RFC6763: DNS-Based Service Discovery
收集
1. Introduction
3. Design Goals
4. Service Instance Enumeration (Browsing)
4.1. Structured Service Instance Names
4.2. User Interface Presentation
4.3. Internal Handling of Names
5. Service Instance Resolution
6. Data Syntax for DNS-SD TXT Records
6.1. General Format Rules for DNS TXT Records
6.2. DNS-SD TXT Record Size
6.3. DNS TXT Record Format Rules for Use in DNS-SD
6.4. Rules for Keys in DNS-SD Key/Value Pairs
6.5. Rules for Values in DNS-SD Key/Value Pairs
6.6. Example TXT Record
6.7. Version Tag
6.8. Service Instances with Multiple TXT Records
7. Service Names
7.1. Selective Instance Enumeration (Subtypes)
7.2. Service Name Length Limits
8. Flagship Naming
9. Service Type Enumeration
10. Populating the DNS with Information
11. Domain Enumeration
12. DNS Additional Record Generation
12.1. PTR Records
12.2. SRV Records
12.3. TXT Records
12.4. Other Record Types
13. Working Examples
14. IPv6 Considerations
15. Security Considerations
16. IANA Considerations
Appendix A. Rationale for Using DNS as a Basis for Service Discovery
Appendix B. Ordering of Service Instance Name Components
B.1. Semantic Structure
B.2. Network Efficiency
B.3. Operational Flexibility
Appendix C. What You See Is What You Get
Appendix D. Choice of Factory-Default Names
Appendix E. Name Encodings in the Domain Name System
Appendix F. “Continuous Live Update” Browsing Model
参考
RFC8766: Discovery Proxy for Multicast DNS-Based Service Discovery
参考
RFC2974: Session Announcement Protocol
RFC3261: SIP: Session Initiation Protocol
Abstract
1 Introduction
2 Overview of SIP Functionality
4 Overview of Operation
5 Structure of the Protocol
6 Definitions
7 SIP Messages
7.1 Requests
7.2 Responses
7.3 Header Fields
7.4 Bodies
7.5 Framing SIP Messages
8 General User Agent Behavior
8.1 UAC Behavior
8.2 UAS Behavior
8.3 Redirect Servers
9 Canceling a Request
9.1 Client Behavior
9.2 Server Behavior
10 Registrations
10.1 Overview
10.2 Constructing the REGISTER Request
10.3 Processing REGISTER Requests
11 Querying for Capabilities
12 Dialogs
12.1 Creation of a Dialog
12.2 Requests within a Dialog
12.3 Termination of a Dialog
13 Initiating a Session
13.1 Overview
13.2 UAC Processing
13.3 UAS Processing
14 Modifying an Existing Session
14.1 UAC Behavior
14.2 UAS Behavior
15 Terminating a Session
15.1 Terminating a Session with a BYE Request
16 Proxy Behavior
16.1 Overview
16.2 Stateful Proxy
16.3 Request Validation
16.4 Route Information Preprocessing
16.5 Determining Request Targets
16.6 Request Forwarding
16.7 Response Processing
16.8 Processing Timer C
16.9 Handling Transport Errors
16.10 CANCEL Processing
16.11 Stateless Proxy
16.12 Summary of Proxy Route Processing
17 Transactions
17.1 Client Transaction
17.2 Server Transaction
18 Transport
19 Common Message Components
19.1 SIP and SIPS Uniform Resource Indicators
19.2 Option Tags
19.3 Tags
20 Header Fields
20.1 Accept
20.2 Accept-Encoding
20.3 Accept-Language
20.4 Alert-Info
20.5 Allow
20.6 Authentication-Info
20.7 Authorization
20.8 Call-ID
20.9 Call-Info
20.10 Contact
20.11 Content-Disposition
20.12 Content-Encoding
20.13 Content-Language
20.14 Content-Length
20.15 Content-Type
20.16 CSeq
20.17 Date
20.18 Error-Info
20.19 Expires
20.20 From
20.21 In-Reply-To
20.22 Max-Forwards
20.23 Min-Expires
20.24 MIME-Version
20.25 Organization
20.26 Priority
20.27 Proxy-Authenticate
20.28 Proxy-Authorization
20.29 Proxy-Require
20.30 Record-Route
20.31 Reply-To
20.32 Require
20.33 Retry-After
20.34 Route
20.35 Server
20.36 Subject
20.37 Supported
20.38 Timestamp
20.39 To
20.40 Unsupported
20.41 User-Agent
20.42 Via
20.43 Warning
20.44 WWW-Authenticate
21 Response Codes
21.1 Provisional 1xx
21.2 Successful 2xx
21.3 Redirection 3xx
21.4 Request Failure 4xx
21.5 Server Failure 5xx
21.6 Global Failures 6xx
22 Usage of HTTP Authentication
22.1 Framework
22.2 User-to-User Authentication
22.3 Proxy-to-User Authentication
22.4 The Digest Authentication Scheme
23 S/MIME
23.1 S/MIME Certificates
23.2 S/MIME Key Exchange
23.3 Securing MIME bodies
23.4 SIP Header Privacy and Integrity using S/MIME: Tunneling SIP
24 Examples
24.1 Registration
24.2 Session Setup
25 Augmented BNF for the SIP Protocol
26 Security Considerations: Threat Model and Security Usage Recommendations
27 IANA Considerations
28 Changes From RFC 2543
28.1 Major Functional Changes
28.2 Minor Functional Changes
A Table of Timer Values
RFC3550: RTP: A Transport Protocol for Real-Time Applications
Abstract
1. Introduction
2. RTP Use Scenarios
2.1 Simple Multicast Audio Conference
2.2 Audio and Video Conference
2.3 Mixers and Translators
2.4 Layered Encodings
3. Definitions
4. Byte Order, Alignment, and Time Format
5. RTP Data Transfer Protocol
5.1 RTP Fixed Header Fields
5.2 Multiplexing RTP Sessions
5.3 Profile-Specific Modifications to the RTP Header
6. RTP Control Protocol – RTCP
6.1 RTCP Packet Format
6.2 RTCP Transmission Interval
6.3 RTCP Packet Send and Receive Rules
6.4 Sender and Receiver Reports
6.5 SDES: Source Description RTCP Packet
6.6 BYE: Goodbye RTCP Packet
6.7 APP: Application-Defined RTCP Packet
7. RTP Translators and Mixers
7.1 General Description
7.2 RTCP Processing in Translators
7.3 RTCP Processing in Mixers
7.4 Cascaded Mixers
8. SSRC Identifier Allocation and Use
9. Security
9.1 Confidentiality
9.2 Authentication and Message Integrity
10. Congestion Control
11. RTP over Network and Transport Protocols
12. Summary of Protocol Constants
12.1 RTCP Packet Types
12.2 SDES Types
13. RTP Profiles and Payload Format Specifications
Appendix A. Algorithms
Appendix B. Changes from RFC 1889
RFC3551: RTP Profile for Audio and Video Conferences with Minimal Control
Abstract
1. Introduction
2. RTP and RTCP Packet Forms and Protocol Behavior
3. Registering Additional Encodings
4. Audio
4.1 Encoding-Independent Rules
4.2 Operating Recommendations
4.3 Guidelines for Sample-Based Audio Encodings
4.4 Guidelines for Frame-Based Audio Encodings
4.5 Audio Encodings
5. Video
6. Payload Type Definitions
7. RTP over TCP and Similar Byte Stream Protocols
8. Port Assignment
9. Changes from RFC 1890
RFC6184: RTP Payload Format for H.264 Video
1. Introduction
1.1. The H.264 Codec
1.2. Parameter Set Concept
1.3. Network Abstraction Layer Unit Types
2. Conventions
3. Scope
4. Definitions and Abbreviations
4.1. Definitions
4.2. Abbreviations
5. RTP Payload Format
5.1. RTP Header Usage
5.2. Payload Structures
5.3. NAL Unit Header Usage
5.4. Packetization Modes
5.5. Decoding Order Number (DON)
5.6. Single NAL Unit Packet
5.7. Aggregation Packets
5.8. Fragmentation Units (FUs)
6. Packetization Rules
7. De-Packetization Process
8. Payload Format Parameters
8.1. Media Type Registration
8.2. SDP Parameters
8.3. Examples
8.4. Parameter Set Considerations
8.5. Decoder Refresh Point Procedure Using In-Band Transport of Parameter Sets (Informative)
12. Informative Appendix: Application Examples
12.1. Video Telephony According to Annex A of ITU-T Recommendation H.241
12.2. Video Telephony, No Slice Data Partitioning, No NAL Unit Aggregation
12.3. Video Telephony, Interleaved Packetization Using NAL Unit Aggregation
12.4. Video Telephony with Data Partitioning
12.5. Video Telephony or Streaming with FUs and Forward Error Correction
12.6. Low Bitrate Streaming
12.7. Robust Packet Scheduling in Video Streaming
13. Informative Appendix: Rationale for Decoding Order Number
13.3. Example of Robust Packet Scheduling
RFC7826: Real-Time Streaming Protocol
Abstract
1. Introduction
2. Protocol Overview
2.1. Presentation Description
2.2. Session Establishment
2.3. Media Delivery Control
2.4. Session Parameter Manipulations
2.5. Media Delivery
2.6. Session Maintenance and Termination
2.7. Extending RTSP
3. Document Conventions
3.2. Terminology
4. Protocol Parameters
4.1. RTSP Version
4.2. RTSP IRI and URI
4.3. Session Identifiers
4.4. Media-Time Formats
4.5. Feature Tags
4.6. Message Body Tags
4.7. Media Properties
5. RTSP Message
5.1. Message Types
5.2. Message Headers
5.3. Message Body
5.4. Message Length
6. General-Header Fields
7. Request
7.1. Request Line
7.2. Request-Header Fields
8. Response
8.1. Status-Line
8.2. Response Headers
9. Message Body
9.1. Message Body Header Fields
9.2. Message Body
9.3. Message Body Format Negotiation
10. Connections
10.1. Reliability and Acknowledgements
10.2. Using Connections
10.3. Closing Connections
10.4. Timing Out Connections and RTSP Messages
10.5. Showing Liveness
10.6. Use of IPv6
10.7. Overload Control
11. Capability Handling
12. Pipelining Support
13. Method Definitions
13.1. OPTIONS
13.2. DESCRIBE
13.3. SETUP
13.4. PLAY
13.5. PLAY_NOTIFY
13.6. PAUSE
13.7. TEARDOWN
13.8. GET_PARAMETER
13.10. REDIRECT
14. Embedded (Interleaved) Binary Data
15. Proxies
15.1. Proxies and Protocol Extensions
15.2. Multiplexing and Demultiplexing of Messages
16. Caching
16.1. Validation Model
16.2. Invalidation after Updates or Deletions
17. Status Code Definitions
18. Header Field Definitions
18.33. Pipelined-Requests
19. Security Framework
19.1. RTSP and HTTP Authentication
19.2. RTSP over TLS
19.3. Security and Proxies
20. Syntax
20.1. Base Syntax
20.2. RTSP Protocol Definition
21. Security Considerations
21.1. Signaling Protocol Threats
Appendix A. Examples
A.1. Media on Demand (Unicast)
A.2. Media on Demand Using Pipelining
A.3. Secured Media Session for On-Demand Content
A.4. Media on Demand (Unicast)
A.5. Single-Stream Container Files
A.6. Live Media Presentation Using Multicast
A.7. Capability Negotiation
Appendix B. RTSP Protocol State Machine
B.1. States
B.2. State Variables
B.3. Abbreviations
B.4. State Tables
Appendix C. Media-Transport Alternatives
C.1. RTP
C.2. RTP over TCP
C.3. Handling Media-Clock Time Jumps in the RTP Media Layer
C.4. Handling RTP Timestamps after PAUSE
C.5. RTSP/RTP Integration
C.6. Scaling with RTP
C.7. Maintaining NPT Synchronization with RTP Timestamps
C.8. Continuous Audio
C.9. Multiple Sources in an RTP Session
C.10. Usage of SSRCs and the RTCP BYE Message during an RTSP Session
C.11. Future Additions
Appendix D. Use of SDP for RTSP Session Descriptions
D.1. Definitions
D.2. Aggregate Control Not Available
D.3. Aggregate Control Available
D.4. Grouping of Media Lines in SDP
D.5. RTSP External SDP Delivery
Appendix E. RTSP Use Cases
E.1. On-Demand Playback of Stored Content
E.2. Unicast Distribution of Live Content
E.3. On-Demand Playback Using Multicast
E.4. Inviting an RTSP Server into a Conference
E.5. Live Content Using Multicast
Appendix F. Text Format for Parameters
Appendix G. Requirements for Unreliable Transport of RTSP
Appendix H. Backwards-Compatibility Considerations
H.1. Play Request in Play State
H.2. Using Persistent Connections
Appendix I. Changes
I.1. Brief Overview
I.2. Detailed List of Changes
RFC8866: SDP-Session Description Protocol
1. Introduction
2. Glossary of Terms
3. Examples of SDP Usage
3.1. Session Initiation
3.2. Streaming Media
3.3. Email and the World Wide Web
3.4. Multicast Session Announcement
4. Requirements and Recommendations
4.1. Media and Transport Information
4.2. Timing Information
5. SDP Specification
5.1. Protocol Version (“v=”)
5.2. Origin (“o=”)
5.3. Session Name (“s=”)
5.4. Session Information (“i=”)
5.5. URI (“u=”)
5.6. Email Address and Phone Number (“e=” and “p=”)
5.7. Connection Information (“c=”)
5.8. Bandwidth Information (“b=”)
5.9. Time Active (“t=”)
5.10. Repeat Times (“r=”)
5.11. Time Zone Adjustment (“z=”)
5.12. Encryption Keys (“k=”)
5.13. Attributes (“a=”)
5.14. Media Descriptions (“m=”)
6. SDP Attributes
6.1. cat (Category)
6.2. keywds (Keywords)
6.3. tool
6.4. ptime (Packet Time)
6.5. maxptime (Maximum Packet Time)
6.6. rtpmap
6.7. Media Direction Attributes
6.8. orient (Orientation)
6.9. type (Conference Type)
6.10. charset (Character Set)
6.11. sdplang (SDP Language)
6.12. lang (Language)
6.13. framerate (Frame Rate)
6.14. quality
6.15. fmtp (Format Parameters)
7. Security Considerations
8. IANA Considerations
9. SDP Grammar
10. Summary of Changes from RFC 4566
rfc7350: DTLS as Transport for STUN
RFC5769: Test Vectors for STUN
rfc5780: NAT Behavior Discovery Using STUN
rfc7443: ALPN Labels for STUN Usages
rfc7635: STUN Extension for Third-Party Authorization
RFC8489: STUN - Session Traversal Utilities for NAT
RFC5280: Internet X.509 PKIC and CRL Profile
参考
RFC5652: Cryptographic Message Syntax (CMS)
参考
RFC5912: New ASN.1 Modules for the PKIX
参考
RFC2045: (MIME) Part One: Format of Internet Message Bodies
1. Introduction
2. Definitions, Conventions, and Generic BNF Grammar
3. MIME Header Fields
4. MIME-Version Header Field
示例
5. Content-Type Header Field
5.1 Syntax of the Content-Type Header Field
5.2 Content-Type Defaults
6. Content-Transfer-Encoding Header Field
6.1. Content-Transfer-Encoding Syntax
6.2. Content-Transfer-Encodings Semantics
6.3. New Content-Transfer-Encodings
6.4. Interpretation and Use
6.5. Translating Encodings
6.6. Canonical Encoding Model
6.7. Quoted-Printable Content-Transfer-Encoding
6.8. Base64 Content-Transfer-Encoding
7. Content-ID Header Field
8. Content-Description Header Field
9. Additional MIME Header Fields
Appendix A – Collected Grammar
content
encoding
id
description
MIME-extension-field
通用
RFC2046: (MIME) Part Two: Media Types
3. Overview Of The Initial Top-Level Media Types
4. Discrete Media Type Values
4.1. Text Media Type
4.2. Image Media Type
4.3. Audio Media Type
4.4. Video Media Type
4.5. Application Media Type
5. Composite Media Type Values
5.1. Multipart Media Type
5.2 Message Media Type
6. Experimental Media Type Values
RFC2047: (MIME) Part Three: Message Header Extensions for Non-ASCII Text
1. Introduction
2. Syntax of encoded-words
3. Character sets
4. Encodings
5. Use of encoded-words in message headers
6. Support of ‘encoded-word’s by mail readers
7. Conformance
8. Examples
RFC2048: (MIME) Part Four: Registration Procedures
1. Introduction
2. Media Type Registration
2.1. Registration Trees and Subtype Names
2.2 Registration Requirements
2.3 Registration Procedure
2.4 Comments on Media Type Registrations
2.5 Location of Registered Media Type List
2.6. IANA Procedures for Registering Media Types
2.7. Change Control
2.8 Registration Template
3. External Body Access Types
4. Transfer Encodings
RFC2049: (MIME) Part Five: Conformance Criteria and Examples
1. Introduction
2. MIME Conformance
3. Guidelines for Sending Email Data
4. Canonical Encoding Model
Appendix A – A Complex Multipart Example
rfc1123
RFC3232: ASSIGNED NUMBERS
RFC1700
rfc3339
参考
RFC5234: Augmented BNF for Syntax Specifications: ABNF
示例
iana
IEEE
常用
IEEE 754
工具
📦 IEEE 754 定义的内容包括：
✅ 1.
浮点数格式
✅ 2.
浮点数结构（三部分）
✅ 3.
特殊值支持
📚 衍生标准（对深度学习重要）
FP4
IEEE 802.3
Ethernet II
802.11: Wireless LAN & Mesh
Protocol
参考
802.15: Wireless PAN
802.15.4: Low-Rate wireless PAN
ITU
常用
电信标准化
研究组
X-Series
DIRECTORY
ASN.1
参考
G-Series
参考
H-Series
参考
ISO
常用
ISO/IEC 10646: Universal coded character set (UCS)
ISO/IEC 13818
Part 1: Systems
Part 6: Extensions for DSM-CC
中标
GB/T28181安全技术视频监控联网系统信息传输, 交换, 控制技术要求
pep
pep-3333
背景与动机
pep-3333主要变化
规范概述
应用程序端
服务器端
中间件
pep-0440
简介
基本格式
版本号的比较
PEP 420 – Implicit Namespace Packages
核心要点
主要优势
当前现有方案
pkgutil-style namespace packages
pkg_resources-style namespace packages
两方案的不足
Specification 规范
说明
命名空间包和常规包之间的区别
Examples
Nested namespace packages
Dynamic path computation
论文
通用
通用
如何看一个论文是不是重要
学术网站
学术搜索平台
资源共享
论文数据库
Agents
2210.03629_ReAct
2303.08268_Chat-with-the-Environment
正文
2303.11366_Reflexion: Language Agents with Verbal Reinforcement Learning
2303.16434_TaskMatrix.AI
大脑
接口平台
API 选择器
2304.03442_Generative-Agents
Generative Agent Architecture
2307.07924_ChatDev: Communicative Agents for Software Development
2308.00352_MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
2308.04026_AgentSims: An Open-Source Sandbox for Large Language Model Evaluation
2308.08155_AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
2308.10848_AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
理念
2310.06117_Step-Back: Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
2402.18679_MetaGPT_DI: Data Interpreter: An LLM Agent For Data Science
INTRODUCTION
2407.07061_IoA: Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
2.1 OVERVIEW OF IOA
2.2 ARCHITECTURE OF IOA
2.3 KEY MECHANISMS
2.5 Putting It All Together
2408.08435_ADAS: Automated Design of Agentic Systems
Prompt
2410.17238_SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
1 Introduction
2 Related Works
3 Method
2408.08435_ADAS: Automating Agentic Workflow Generation
Introduce
PRELIMINARY
2410.21012_FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval
Introduce
2504.01990_Advances and Challenges in Foundation Agents
2506.12508_AgentOrchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving
Abstract
1.Introduction
3.AgentOrchestra
4.Experiments
视觉 Agent&AIOS
2312.13771_AppAgent: Multimodal Agents as Smartphone Users
3.1 Environment and Action Space
3.2 Exploration Phase
3.3 Deployment Phase
2402.07939_UFO: A UI-Focused Agent for Windows OS Interaction
Abstract
1.Introduction
2.Related Work
3.The Design of UFO
4.Experiment
5.Limitations & Lessons Learned
6.Conclusion
2406.01014_Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
2501.11733_Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks
Abstract
1. Introduction
2. Mobile-Agent-E
3. Experiments
4. Results
5. Related Work
6. Conclusion and Future Work
Appendix A Full Trajectory Comparison Example with Previous SOTA
Appendix B Error Recovery with Escalation to Manager
Appendix C Remaining Limitations
Appendix D All Tasks in Mobile-Eval-E Benchmark
Appendix E Atomic Operation Space
Appendix F Full list of Self-Evolved Shortcuts
Appendix G Full list of Self-Evolved Tips
2501.12326_UI-TARS: Pioneering Automated GUI Interaction with Native Agents
Abstract
1. Introduction
2. Evolution Path of GUI Agents
3. Core Capabilities of Native Agent Model
4. UI-TARS
5. Experiment
6. Conclusion
2502.14282_PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC
Abstract
1. Introduction
2. PC-Agent
3. Experiments
4. Related Work
5. Conclusion
2403.16971_AIOS: LLM Agent Operating System
Abstract
1. Introduction
2. The Architecture of AIOS
3. AIOS Kernel
4 Evaluation
Appendix E Discussion
2504.14603_UFO2: The Desktop AgentOS
Abstract
1.Introduction
2.Background
3.System Design of UFO2
4.Picture-in-Picture Interface
5.Implementation and Specialized Engineering Design
6.Evaluation
7.Discussion & Future Work
8.Related Work
9.Conclusion
大模型调优
2101.00190_Prefix-Tuning: Optimizing Continuous Prompts for Generation
2103.10385_p-tuning: GPT Understands, Too
2104.08691_Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning
2106.09685_LoRA: Low-Rank Adaptation of Large Language Models
2401.01335_Self-Play: Fine-Tuning Converts Weak Language Models to Strong Language Models
2402.09353_DoRA: Weight-Decomposed Low-Rank Adaptation
2402.12354_LoRA+: Efficient Low Rank Adaptation of Large Models
2403.03507_GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
2403.13372_LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
竞争框架
3. Efficient Fine-Tuning Techniques
4 LlamaFactory Framework
6 Conclusion and Future Work
2203.02155_Training language models to follow instructions with human feedback(InstructGPT)
Abstract
1. Introduction
2. Related work
3. Methods and experimental details
4. Results
5. Discussion
Appendix A Additional prompt data details
Appendix B Additional human data collection details
Appendix C Additional model details
Appendix D Automatic evaluation details
2305.20050_Let’s Verify Step by Step
1. 研究背景
2. 监督方法对比
3. 核心发现
总结
2408.03314_Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
1. Introduction
3. How to Scale Test-Time Computation Optimally
5. Scaling Test-Time Compute via Verifiers
6. Refining the Proposal Distribution
其他
2412.14135_Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
FromGPT
1. Introduction
2. Background
3. Policy Initialization
4. Reward Design
5. Search
6. Learning
7 Open-source o1 Project
8. Future Directions
分布式模型
通用
1701.06538_MoE: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
1806.03377_PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Abstract
1. Introduction
2. Background & Related Work
3. Parallel Training in PipeDream
4. Implementation
5. Evaluation
1811.06965_GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
收集
1. Introduction
2. The GPipe Library
3. Performance Analyses
4. Image Classification
5. Massive Massively Multilingual Machine Translation
6. Design Features and Trade-Offs
1909.08053_Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
收集
Abstract
1. Introduction
2. Background and Challenges
3. Model Parallel Transformers
19xx_PipeDream: Generalized Pipeline Parallelism for DNN Training
收集
ABSTRACT
1. Introduction
2. BACKGROUND AND RELATED WORK
3. 流水线并行(PIPELINE PARALLELISM)
4. 实现
6. 结论
2006.15704_PyTorch Distributed: Experiences on Accelerating Data Parallel Training
2006.16668_GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
2006.09503_PipeDream-2BW: Memory-Efficient Pipeline-Parallel DNN Training
Abstract
2104.04473_Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
Abstract
2205.14135_FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Abstract
1. Introduction
2 Background
3. FLASHATTENTION: Algorithm, Analysis, and Extensions
4. Experiments
5. Limitations and Future Directions
Appendix A Related Work
Appendix B Algorithm Details
Appendix C Proofs
Appendix D Extension Details
2307.08691_FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Abstract
1. Introduction
2. Background
3. FlashAttention-2: Algorithm, Parallelism, and Work Partitioning
4. Empirical Validation
5. Discussion and Future Directions
LLM NLP
18xx_GPT1: Improving Language Understanding by Generative Pre-Training
Abstract
1. Introduction
2. Related Work
3. Framework
4 Experiments
5 Analysis
6 Conclusion
引文口碑
要点解读
1810.04805_BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
1 Introduction
2 Related Work
3 BERT
Appendix A Additional Details for BERT
19xx_GPT2: Language Models are Unsupervised Multitask Learners
The Illustrated GPT-2
参考
2012.00413_CPM: A Large-scale Generative Chinese Pre-trained Language Model
2302.13971_LLaMA: Open and Efficient Foundation Language Models
2307.09288_Llama 2: Open Foundation and Fine-Tuned Chat Models
2309.16609_Qwen Technical Report
1. Introduction
2. Pretraining
3. Alignment
4. CODE-QWEN: SPECIALIZED MODEL FOR CODING
5. MATH-QWEN: SPECIALIZED MODEL FOR MATHEMATICS REASONING
6. Related Work
7. Conclusion
A.1 MORE TRAINING DETAILS
A.2 EVALUATION
2401.14196_DeepSeek-Coder: When the Large Language Model Meets Programming – The Rise of Code Intelligence
2404.06395_MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
5. Two Stage Pre-training Strategy
6. Model
7 MiniCPM Family
2405.04434_DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
2406.12793_ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
2407.10671_Qwen2 Technical Report
Abstract
1. Introduction
2. Tokenizer & Model
3. Pre-training
4. Post-training
5. Evaluation
6. Conclusion
2412.15115_Qwen2.5
Abstract
1. Introduction
2. Architecture and Tokenizer
3. Pre-training
4. Post-training
5. Evaluation
6. Conclusion
2505.09388_Qwen3
Abstract
1. Introduction
2. Architecture
3. Pre-training
4. Post-training
5. Conclusion
LLM MoE
2410.07490_MoDEM: Mixture of Domain Expert Models
2408.15664_AUXILIARY-LOSS-FREE LOAD BALANCING STRATEGY FOR MIXTURE-OF-EXPERTS
LLM 多模态
2304.08485_LLaVA: Visual Instruction Tuning
Abstract
1. Introduction
2. Related Work
3. GPT-assisted Visual Instruction Data Generation
4. Visual Instruction Tuning
5. Experiments
6. Conclusion
2308.12966_Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Methodology
Training
Evaluation
B. Data Format Details of Training
2310.03744_LLaVA2: Improved Baselines with Visual Instruction Tuning
Abstract
1. Introduction
2. Related Work
3. Approach
4. Empirical Evaluation
5. Open Problems in LMMs
6. Conclusion
A. Implementation Details
B. Qualitative Results
2312.07533_VILA: On Pre-training for Visual Language Models
Abstract
1. Introduction
2. Background
3. On Pre-training for Visual Language Models
4. Experiments
5. Related Work
6. Conclusion
2403.05525_DeepSeek-VL: Towards Real-World Vision-Language Understanding
Abstract
2408.01800_MiniCPM-V: A GPT-4V Level MLLM on Your Phone
Abstract
1. Introduction
2. Related Work
3. Model Architecture
4. Training
5. End-side Deployment
6. Experiments
7. Conclusion
2409.17146_Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Abstract
1. Introduction
2. Architecture
3. Data
4. Training
5. Evaluation
6. Ablations
Appendix A: Model Details
Appendix B: Training Details
Appendix C: Evaluation Results
Appendix D: Result Details
Appendix E Ablations Details
Appendix F Data Details
Appendix G Dataset Examples
Appendix H Related Work
2411.00774_Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM
Abstract
1. Introduction
2. Model
3. Experience
4. Conclusion and Future Work
2412.04468_NVILA: Efficient Frontier Visual Language Models
Abstract
1. Introduction
2. Approach
3. Experiments
4. More Capabilities
5. Related Work
6. Conclusion
2502.13923_Qwen2.5-VL
Abstract
1. Introduction
2. Approach
3. Experiments
4. Conclusion
2503.20215_Qwen2.5-Omni Technical Report
Abstract
1. Introduction
2. Archtecture
3 预训练
4 后训练（Post-training）
5. Evaluation
6. Conclusion
2506.13642_Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model
Abstract
1. Introduction
2. Related Work
3. Stream-Omni
4. Experiments
5. Results and Analyses
6. Conclusion
Appendix A Construction of InstructOmni
Appendix B Construction of SpokenVisIT
LLM 音频
2005.08100_Conformer: Convolution-augmented Transformer for Speech Recognition
2112.02418_YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
关键概念
Abstract
1. Introduction
2. YourTTS Model
3. Experiments
4. Results and Discussion
5. Zero-Shot Voice Conversion
6. Speaker Adaptation
7. Conclusions, limitations and future work
2212.04356_whisper: Robust Speech Recognition via Large-Scale Weak Supervision
Abstract
1. Introduction
2. Approach
3. Experiments
4. Analysis and Ablations
5. Related Work
6. Limitations and Future Work
7. Conclusions
A. Evaluation Datasets
B Compared Models
C. Text Standardization
2301.02111_Vall-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
Abstract
1. Introduction
2. Related Work
3. Background: Speech Quantization
4. VALL-E
5. Experiments
6. Conclusion, Limitations, and Future Work
2303.03926_VALL-E_X: Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling
Abstract
1. Introduction
2. Related Work
3 Cross-Lingual Codec Language Model
4. VALL-E X Application
5. Experiments
6. Conclusion
A. Appendix
2406.05370_VALL-E2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers
Abstract
1. Introduction
2. Related Work
3. VALL-E 2
4. Experiments
5. Conclusion
2407.05407_CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens
Abstract
1. Instructions
2. CosyVoice: A Scalable TTS model using Supervised Semantic Tokens
3. Dataset
4. Experimental Settings
6. Conclusion
2407.10759_Qwen2-Audio Technical Report
Abstract
1. Introduction
2. Methodology
3. Experiments
5. Conclusion
2410.00037_Moshi: a speech-text foundation model for real-time dialogue
Abstract
1.Introduction
2.Related Work
3.Model
4. Datasets and Training
5. Evaluation
2412.10117_CosyVoice2: Scalable Streaming Speech Synthesis with Large Language Models
Abstract
1. Instroduction
2. CosyVoice 2
3. Experimental Settings
4. Experimental Results
5. Conclusion
2501.06282_MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Abstract
1.Instruction
2.Related Work
3.MinMo
4.Experiments
5.Conclusion
6.Limitations
A. Prompts for Voice Understanding Tasks
2505.02707_Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play
Abstract
1. Introduction
2. Related Work
3. Voila: Voice-Language Foundation Models
4. Experiments
5. Conclusion
2505.17589_CosyVoice3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
Abstract
1.Introduction
2.CosyVoice 3
3.The Multilingual Data Pipeline
4.Experimental Settings
5.Experimental Results
6.Conclusion
7.Limitations
LLM强化学习
1703.03864_Evolution Strategies: as a Scalable Alternative to Reinforcement Learning
2504.02495_DeepSeek-GRM: Inference-Time Scaling for Generalist Reward Modeling
Abstract
1. Introduction
2. Preliminaries
3. Self-Principled Critique Tuning (SPCT)
4. Inference-Time Scaling with SPCT
5. Results on Reward Modeling Benchmarks
6. Related Work
7. Conclusion and Future Work
A. Additional Related Work
B. Limitations and Future Directions
G. Prompt Templates
2504.13958_ToolRL: Reward is All Tool Learning Needs
LLM 量化
通用
混合精度
浮点数格式
weight-only quantization
2110.02861_bitsandbytes: 8-bit Optimizers via Block-wise Quantization
Abstract
1. Background
2. 8-bit Optimizers
3. 8-bit vs 32-bit Optimizer Performance for common Benchmarks
4. Analysis
5. Related Work
2206.01861_ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Abstract
1. Introduction
2. Relative Work
3. Background and Challenges
4. Methodology
5. Results
6. Conclusions
Appendix A Background
Appendix D Details about System Optimization
2206.09557_LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
Abstract
1. Instructions
2. Background
3. Design Methodology of LUT-GEMM
4. Experimental results
5. Accelerating Quantized OPT-175B
6. Conclusion
Appendix A LLM Inference Latency Breakdown
Appendix B Detailed Implementation
2208.07339_LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
相关参考
Abstract
1. Introduction
2. Background
3. Int8 Matrix Multiplication at Scale
4. Emergent Large Magnitude Features in Transformers at Scale
5. Related Work
6. Discussion and Limitations
7. Broader Impacts
其他
2209.05433_FP8: FP8 Formats For Deep Learning
Abstract
1. Introduction
2. Aspects of FP8 Usage in Deep Learning
3. FP8 Binary Interchange Format
示例讲解
4. Empirical Results
5. Conclusions
2210.17323_GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Abstract
1. Introduction
2. Related Work
3. Background
4. The GPTQ Algorithm
5. Experimental Validation
6. Summary and Limitations
2211.10438_SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Abstract
1. Introduction
2. Preliminaries
3. Review of Quantization Difficulty
4. SmoothQuant
5. Experiments
6. Related Work
7. Conclusion
Appendix A. Discussion on Weight-Only Quantization
2305.14314_QLoRA: Efficient Finetuning of Quantized LLMs
关键词
Abstract
1. Introduction
2. Background
3. QLoRA Finetuning
4. QLoRA vs. Standard Finetuning
5. Pushing the Chatbot State-of-the-art with QLoRA
6. Qualitative Analysis
7. Related Work
8. Limitations and Discussion
2306.00978_AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Abstract
1. Introduction
2. Related Work
3. AWQ: Activation-aware Weight Quantization
4. TinyChat: Mapping AWQ onto Edge Platforms
5. Experiments
6. Conclusion
2309.05516_AutoRound: Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
Abstract
1. Introduction
2. Related Work
3. Methodology
4. Experiments
5. Conclusion
LLM 闭源模型
2303.08774_GPT-4 Technical Report
2312.11805_Gemini: A Family of Highly Capable Multimodal Models
Abstract
1. Introduction
2. Model Architecture
3. Training Infrastructure
5. Evaluation
6. Post-Training Models
7. Responsible Deployment
8. Discussion and Conclusion
2403.05530_Gemini1.5: Unlocking multimodal understanding across millions of tokens of context
2503.20020_Gemini2: Gemini Robotics: Bringing AI into the Physical World
3D
2003.08934_NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Abstract
1. Introduction
2. Related Work
3. Neural Radiance Field Scene Representation
4. Volume Rendering with Radiance Fields
5. Optimizing a Neural Radiance Field
6. Result
7. Conclusion
2203.08586: Deep vanishing point detection: Geometric priors make dataset variations vanish
概念
Abstract
1. Introduction
2. Related Work
3. Geometric priors for VP detection
4. Experiments
5. Conclusion and limitations
2312.14132_DUSt3R: Geometric 3D Vision Made Easy
关键词
相关概念
Abstract
1. Introduction
2. Related Work
3. Method
4. Experiments with DUSt3R
5. Conclusion
Appendix A
附录概览
Appendix B. Qualitative results
Appendix C. Extended Related Work
Appendix D. 多视角姿态估计（Multi-view Pose Estimation）
Appendix E. 视觉定位（Visual Localization）
Appendix F. Training details
2406.09756_MASt3R: Grounding Image Matching in 3D with MASt3R
前言
Abstract
1. Introduction
🧠 思维导图式总结
2. Related works
🧠 总结思维导图
3. Method
4. Experimental results
5. Conclusion
Appendix
Appendix A Additional Qualitative Results
B. Fast Reciprocal Matching
C. Coarse-to-Fine
D. Detailed experimental settings
2412.09401_SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos
术语
Abstract
1. Introduction
2. Related Work
3. Method
4. Experiments
5. Conclusion
6. 致谢
Appendix
Appendix A Implementation details
Appendix B Details for experimental settings
Appendix C Additional comparisons and analyses
D. More visual results
2412.12392_MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
GPT
先验知识
Abstract
1. Introduction
2. Related Work
3. Method
4. Results
5. Limitations and Future Work（局限与未来工作）
🧾 6. Conclusion（总结）
🧠 总结一句话版：
8. Initialisation（初始化）
9. Runtime Breakdown（运行时分析）
10. Evaluation Setup（评估设置）
11. EuRoC 结果总结
2503.11651_VGGT: Visual Geometry Grounded Transformer
Abstract
1. Introduction
2. Related Work
3. Method
4. Experiments
5. Discussions
6. Conclusions
Appendix A Formal Definitions
Appendix B Implementation Details
Appendix C Additional Experiments
Appendix D Qualitative Examples
Appendix E Related Work
LLM 安全
2312.06674_Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Benchmarking
通用
评测标准
准确率(Accuracy)
精确率(Precision, 精准率)
召回率(Recall)
F1 Score
可视化精度和召回率
2009.03300_MMLU: Measuring Massive Multitask Language Understanding
Abstract
1.Introduction
2.Related Work
3.A Multitask Test
4.Experiments
5.Discussion
6.Conclusion
2103.03874_MATH: Measuring Mathematical Problem Solving With the MATH Dataset
2311.12022_GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Abstract
1.Introduction
2.Data Collection
3.Dataset Analysis
4.Baseline
5.Related Work
6.Limitations
7.Conclusion
2311.12983_GAIA: a benchmark for General AI Assistants
Abstract
1.Introduction
2.Related work
3.GAIA
4.LLMs results on GAIA
5.Discussion
6.Limitations
Appendix A Extended related work
Appendix C Extended description of GAIA
Appendix D Extended description of our question design framework
2404.07972_OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Abstract
1. Introduction
2. OSWORLD Environment
3. OSWORLD Benchmark
4. Benchmarking LLM and VLM Agent Baselines
5. Analysis
6. Related Work
7. Conclusion and Future Work
A. Details of OSWORLD Environment
C. Details of Baseline Methods
D. Examples of Qualitative Analysis
2411.04368_SimpleQA: Measuring short-form factuality in large language models
Abstract
1.Introduction
2.Data Collection and Verification
4.Measuring calibration
Appendix B Guessing strategy and F-score
2501.14249_HLE: Humanity’s Last Exam
Abstract
1.Introduction
2.Related Work
3.Dataset
4.Evaluation
5.Discussion
数据集&数据蒸馏
通用
Dataset distillation
1811.10959v3_Dataset Distillation
ABSTRACT
LLM总结
1. INTRODUCTION
3. APPROACH
2112.15093_CTR: Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study
Abstract
1. Introduction
2. Preliminaries
3. Datasets
4. Baselines
5. An Empirical Study
6. Conclusions
Appendix A Details of PRAB
Appendix C Visualization of Failure Cases.
2502.20653_Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
Abstract
1. Introduction
2. Related Work
7. Conclusion
Framework
1712.05889_Ray: A Distributed Framework for Emerging AI Applications
Abstract
1. Introduction
2. Motivation and Requirements
3. Programming and Computation Model
4. Architecture
5. Evaluation
6 Related Work
7 Discussion and Experiences
8. Conclusion
1910.02054_DeepSpeed_ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Abstract
1. Extended Introduction
2. Related Work
3 Where Did All the Memory Go?
4 ZeRO: Insights and Overview
5 Deep Dive into ZeRO-DP
6 Deep Dive into ZeRO-R
7 Communication Analysis of ZeRO-DP
8. Communication Analysis of ZeRO-R
9. Step Towards 1 Trillion Parameters
10. Implementation and Evaluation
11. Concluding Remarks
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Transformers: State-of-the-Art Natural Language Processing
2210.XX_Ray v2 Architecture
Overview
Architecture Overview
Object Management
Task Management
Resource Management and Scheduling
Actor management
Global Control Service
Cluster Management
Appendix
2309.06180_Efficient Memory Management for Large Language Model Serving with PagedAttention
1. Introduction
2. Background
3. Memory Challenges in LLM Serving
4. Method
5. Implementation
6. Evaluation
7. Ablation Studies
10. Conclusion
ML
2112.09332_WebGPT: Browser-assisted question-answering with human feedback
2203.11147_GopherCite: Teaching language models to support answers with verified quotes
2305.14251_FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
2304.09848_Generative_Search: Evaluating Verifiability in Generative Search Engines
2307.02185_Citation: A Key to Building Responsible and Accountable Large Language Models
2307.16883_HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution
2305.14627_ALCE: Enabling Large Language Models to Generate Text with Citations
NLI 在引用质量评估中的应用
论文中用的prompt
ML 多模态相关
2108.03353_ Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Abstract
1. Introduction
2. Related Work
3. Dataset Creation
4. Model Design
其它
2209.08199_ScreenQA: Large-Scale Question-Answer Pairs over Mobile App Screenshots
Abstract
1. Introduction
2. Related Work
3. Problem Setting: Tasks and Metrics
4. Data Annotation
5. Dataset Analysis
6. Experiments and Baselines
7. Conclusion
8. Limitations
9. Ethical Considerations
A. Data Annotation Details
B. Data Examples
2212.06817_RT-1: ROBOTICS TRANSFORMER FOR REAL-WORLD CONTROL AT SCALE
ABSTRACT
1. Introduction
2. Related Work
3. Preliminaries
4. System Overview
5. RT-1: ROBOTICS TRANSFORMER
6. EXPERIMENTS
7. CONCLUSIONS, LIMITATIONS AND FUTURE WORK
B. MODEL CARD
C. MODEL AND DATA
D. EXPERIMENTS
2401.10935_SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
Abstract
1. Introduction
2. Related work
3. Approach
4. ScreenSpot: A Grounding Benchmark
5. Experiments
6. Conclusion
Limitations
Ethical considerations
A. Details of SeeClick Pre-training
B ScreenSpot Annotation & Evaluation
C. Downstream Agent Tasks
2402.04615_ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Abstract
1. Introduction
2. Methodology
3. Automatic data generation
4. Data Mixtures
5. Experiments and Results
6. Conclusions
A Definitions of Metrics
B. Screen Schema Examples
C. Prompts For LLM Generated Content
D. Screen Navigation Generated Examples
F. ScreenQA Short Answers Generation
G. Complex Question Answering Datasets
H. New Benchmarks Repositories
2411.02059_TableGPT2: A Large Multimodal Model with Tabular Data Integration
Abstract
ML Vision
1506.02640_You Only Look Once: Unified, Real-Time Object Detection
Abstract
1612.08242_YOLO9000: Better, Faster, Stronger
Abstract
1804.02767_YOLOv3
2004.10934_YOLOv4: Optimal Speed and Accuracy of Object Detection
Abstract
2205.00159_SVTR: Scene Text Recognition with a Single Visual Model
Abstract
1. Introduction
2. Method
3. Experiments
4. Conclusion
2207.02696_YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Abstract
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
2304.08485_Visual Instruction Tuning
2402.13616_YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Abstract
2405.14458_YOLOv10: Real-Time End-to-End Object Detection
Abstract
2411.15858_SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
定义
Abstract
1. Introduction
2. Related Work
3. Methods
4 Experiments
5. Conclusion
8. More detail of real-world datasets
RAG
2005.11401_Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
2312.10997_Retrieval-Augmented Generation for Large Language Models: A Survey
II. Overview of RAG
III. Retrieval
IV. Generation
V. Augmentation process in RAG
VI. Task and Evaluation
VII. Discussion and Future Prospects
2401.15884_CRAG: Corrective Retrieval Augmented Generation
2403.14403_Adaptive-RAG
2404.16130_From Local to Global: A Graph RAG Approach to Query-Focused Summarization
简介
相关的技术讨论
2405.16506_GRAG: Graph Retrieval-Augmented Generation
GraphRAG 官方文档
Indexing
Query
2406.13213_Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata
2410.10450_KBLaM: Knowledge Base augmented Language Model
Abstract
1. Introduction
2. Related work
3. Background
4. Augmenting LLM with the KB
5. KB instruction tuning
6. EXPERIMENTS
7. CONCLUSION
8. LIMITATIONS AND FUTURE WORK
Appendix A Extended related work
Appendix B Ablation study
Appendix C Sample KB
SAMPLE Q&A
PROMPT
SAMPLE OUTPUT
2504.03137_LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Abstract
Introduction
Related Work
Preliminaries
Methodology
Experiments
Conclusion
Tools
2205.00445_MRKL
2302.04761_Toolformer: Language Models Can Teach Themselves to Use Tools
2303.17580_HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
AGI
1905.10985_AI-GA: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
2408.06292_The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
others
A PAINLESS GUIDE TO CRC ERROR DETECTION ALGORITHMS
The Basic Idea Behind CRC Algorithms
Polynomical Arithmetic
Binary Arithmetic with No Carries
一个可用的实例
Choosing A Poly
A Straightforward CRC Implementation
A Table-Driven Implementation
A Slightly Mangled Table-Driven Implementation
参考
Distributed Representations of Sentences and Documents
TODO
大模型
别人的收集
临时
学习记录
局域网内的服务发现会有什么方法
mDNS协议
命令工具
组播&广播
IPv4多播转发
多播功能
任播
新溪-gordon
Docs
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2005.08100_Conformer: Convolution-augmented Transformer for Speech Recognition
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2005.08100_Conformer: Convolution-augmented Transformer for Speech Recognition
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https://arxiv.org/abs/2005.08100