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Spring AI Alibaba 官方文档https://github.com/alibaba/spring-ai-alibaba
https://github.com/java-ai-tech/spring-ai-summary#项目系列
MindMark（心印）是一款基于 SpringAI 的 RAG 系统https://gitee.com/mumu-osc/mind-mark
My AI Agent 是一个基于 Spring Boot 和 Spring AI 框架构建的智能代理服务https://github.com/Cunninger/my-ai-agent
https://github.com/java-ai-tech/spring-ai-summary#博客系列
码匠的流水账--Spring AI 系列专栏https://cloud.tencent.com/developer/column/72423
深入解析 Spring AI 系列https://www.cnblogs.com/guoxiaoyu/p/18666904
如何用Spring AI构建MCP Client-Server架构https://spring.didispace.com/article/spring-ai-mcp.html
Building Effective Agents with Spring AIhttps://spring.io/blog/2025/01/21/spring-ai-agentic-patterns
Spring AI 大模型返回内容格式化源码分析及简单使用https://juejin.cn/post/7378696051082199080
Spring AI EmbeddingModel 概念与源码分析https://my.oschina.net/u/2391658/blog/18534829
全量RAG技术：更简单、更实用的实现方法 ✨https://www.readme-i18n.com/FareedKhan-dev/all-rag-techniques?lang=zh
Spring AI 框架原理与实战https://juejin.cn/column/7375109287716372520
LLM 系列（一）：发展历程篇https://mp.weixin.qq.com/s/72omFtMqinJs4MMX41MCyw
LLM 系列（二）：基础概念篇https://mp.weixin.qq.com/s/Fg0bczAxejgKs_65kWxWSg
LLM 系列（三）：架构模式篇https://mp.weixin.qq.com/s/d6Y76I_Vxfmj6wa5sAN3uQ
LLM 系列（四）：神奇数字篇https://mp.weixin.qq.com/s/tEozMs4QWDYx5vrhitA9uQ
LLM 系列（五）：模型训练篇https://mp.weixin.qq.com/s/TK8rTcrjS1pfigQxj2mq8A
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LLM 系列（七）：数学概念篇https://mp.weixin.qq.com/s/SBtmPoWtAoguWFUVPB1PGQ
LLM 系列（八）：RAG 篇https://mp.weixin.qq.com/s/y4yAGqs6p4PhErPmRKtDkg
LLM 系列（九）：RAG 番外篇-从文档到向量https://mp.weixin.qq.com/s/HojxR-tC5Gxn3tsxCz17Ow
LLM 系列（十）：RAG 番外篇-向量检索https://mp.weixin.qq.com/s/QFnfBU2we_4UG6v4spEFgA
LLM 系列（十一）：模型精度篇https://mp.weixin.qq.com/s/E_AeSOGYzoynm5hVicaKfw
LLM 系列（十二）：解读 Function Callinghttps://mp.weixin.qq.com/s/RiILquzDnKZCaZG8nry2Eg
LLM 系列（十三）：解读 Context Engineeringhttps://mp.weixin.qq.com/s/o77X-ZKBbSwYq0qMqpTP4Q
LLM 系列（十四）：解读 Deep Researchhttps://mp.weixin.qq.com/s/t_CAsHp5cnFQIKFbbQqkLg
LLM 系列（十五）：解读 Transformer 位置编码https://mp.weixin.qq.com/s/y4rDTx0BTNFTamem6S_WKw
LLM 系列（十六）：解读 Transformer 输出采样https://mp.weixin.qq.com/s/daR0asrVN01nFjFA2JcsJQ
LLM 系列（十七）：解读 Transformer 残差连接https://mp.weixin.qq.com/s/GEwu__TklVjBN0o9cildpA
LLM 系列（十八）：解读 Transformer 注意力机制https://mp.weixin.qq.com/s/dttX6ODjnoyKfFfpyZGepQ
https://github.com/java-ai-tech/spring-ai-summary#视频系列
How to Build Agents with Spring AIhttps://www.youtube.com/watch?v=d7m6nJxfi0g
Spring AI 系列视频教程https://www.youtube.com/watch?v=yyvjT0v3lpY&list=PLZV0a2jwt22uoDm3LNDFvN6i2cAVU_HTH
马克的技术工作坊https://space.bilibili.com/1815948385
大模型技术 AI 社区 EZ-Encoderhttps://space.bilibili.com/3546829121652889?spm_id_from=333.788.upinfo.detail.click
https://github.com/java-ai-tech/spring-ai-summary#优质论文
Brook for GPUs（2004）https://graphics.stanford.edu/projects/brookgpu/
ImageNet Classification with Deep CNNs（AlexNet）https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
Sequence to Sequence Learning with Neural Networks（2014）https://arxiv.org/abs/1409.3215
Neural Machine Translation by Jointly Learning to Align and Translate（2014）https://arxiv.org/abs/1409.0473
Distilling the Knowledge in a Neural Network（2015）https://arxiv.org/abs/1503.02531
Deep Residual Learning for Image Recognition（ResNet）（2015）https://arxiv.org/abs/1512.03385
Outrageously Large Neural Networks: Mixture of Experts（2017）https://arxiv.org/abs/1701.06538
Attention Is All You Need（Transformer）（2017）https://arxiv.org/abs/1706.03762
Mastering the Game of Go without Human Knowledge（AlphaGo Zero）（2017）https://www.nature.com/articles/nature24270
The Bitter Lesson（2018）https://www.gwern.net/The-Bitter-Lesson
Chain-of-Thought Prompting（2022）https://arxiv.org/abs/2201.11903
LoRA: Low-Rank Adaptation of LLMs（2021）https://arxiv.org/abs/2106.09685
ReAct: Reasoning and Acting in Language Models（2022）https://arxiv.org/abs/2210.03629
ZeRO: Memory Optimization for Trillion-Parameter Models (2019)https://arxiv.org/abs/1910.02054
Scaling Laws for Neural Language Models (2020) https://arxiv.org/abs/2001.08361
Training Compute-Optimal LLMs (Chinchilla) (2022) https://arxiv.org/abs/2203.15556
LAION-5B Dataset (2022) — Schuhmannhttps://arxiv.org/abs/2210.08402
The RefinedWeb Dataset (2023)https://arxiv.org/abs/2306.01116
MegaScale: Training on 10,000+ GPUs (2024)https://arxiv.org/abs/2402.15627
Efficient Estimation of Word Representations（Word2Vec）（2013）https://arxiv.org/abs/1301.3781
Google’s Neural Machine Translation System（2016）https://arxiv.org/abs/1609.08144
Improving Language Understanding by Generative Pre-Training（GPT-1）https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
BERT: Pre-training of Deep Bidirectional Transformers（2018）https://arxiv.org/abs/1810.04805
Language Models are Unsupervised Multitask Learners（GPT-2）（2019）https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
GPT-3: Language Models are Few-Shot Learners（2020）https://arxiv.org/abs/2005.14165
Training Language Models with Human Feedback（InstructGPT）（2022）https://arxiv.org/abs/2203.02155
Tulu 3 Technical Report（2024）https://arxiv.org/abs/2411.15124
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learninghttps://arxiv.org/abs/2501.12948
DeepSeek-V3 Technical Reporthttps://arxiv.org/abs/2412.19437
DeepSeek LLM: Scaling Open-Source Language Models with Longtermismhttps://arxiv.org/abs/2401.02954
Kimi k1.5: Scaling Reinforcement Learning with LLMshttps://arxiv.org/abs/2501.12599
Kimi K2: Open Agentic Intelligencehttps://arxiv.org/abs/2507.20534
Qwen Technical Reporthttps://arxiv.org/abs/2309.16609
Qwen2.5 Technical Reporthttps://arxiv.org/abs/2412.15115
Qwen3 Technical Reporthttps://arxiv.org/abs/2505.09388
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