> For the complete documentation index, see [llms.txt](https://yeasy.gitbook.io/agentic_ai_guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://yeasy.gitbook.io/agentic_ai_guide/di-wu-bu-fen-fu-lu/12_appendix/12.2_reading_list.md). # 12.2 推荐论文与阅读清单 本节提供一份“按主题组织”的阅读清单，帮助你从论文与工程资料中有重点地学习智能体。考虑到框架与产品更迭很快，本书避免维护“某平台官方文档链接合集”与“某年最新文章清单”，而是给出更稳定的筛选与阅读路径。 > **阅读建议**： > > 建议的阅读顺序：先从 **必读论文**(12.2.1) 和 **推理技术**(12.2.2) 建立基础概念，再根据你的专注方向选读相应主题。如果关注生产环境部署，**优先阅读安全与对齐部分** 的论文，特别是提示词注入与供应链安全相关的工作，这些直接影响系统稳定性。**工程资料选择**(12.2.8) 提供的优先级建议可帮助你避免被快速迭代的工具细节淹没。 ## 12.2.1 必读论文 建议优先阅读这几类“奠基性工作”，它们对后续工程实践影响最大： | 论文 | 主题 | 年份 | | ------------------------------------------------------------------------------------------------- | ------------- | ---- | | [Chain-of-Thought Prompting Elicits Reasoning](https://arxiv.org/abs/2201.11903) | 逐步推理与提示词设计 | 2022 | | [ReAct: Synergizing Reasoning and Acting](https://arxiv.org/abs/2210.03629) | 推理与行动结合 | 2022 | | [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) | 工具学习 | 2023 | | [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) | 反思与自我改进 | 2023 | | [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) | 生成式智能体与记忆驱动行为 | 2023 | ## 12.2.2 推理技术 | 论文 | 主题 | 年份 | | ---------------------------------------------------------------------------------------------------------------------- | --------------- | ---- | | [Tree of Thoughts: Deliberate Problem Solving](https://arxiv.org/abs/2305.10601) | 树搜索式思考 | 2023 | | [Self-Consistency Improves CoT Reasoning](https://arxiv.org/abs/2203.11171) | 多样采样与自一致性 | 2022 | | [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/abs/2501.12948) | 开源推理模型（见 7.5 节） | 2025 | ## 12.2.3 多智能体 | 论文 | 主题 | 年份 | | ------------------------------------------------------------------------------------------------------------ | ----------- | ---- | | [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155) | 对话式多智能体 | 2023 | | [MetaGPT: Meta Programming for Multi-Agent](https://arxiv.org/abs/2308.00352) | 面向软件开发的多智能体 | 2023 | | [CAMEL: Communicative Agents for Mind Exploration](https://arxiv.org/abs/2303.17760) | 角色扮演与协作 | 2023 | ## 12.2.4 检索增强 | 论文 | 主题 | 年份 | | ------------------------------------------------------------------------------------------ | ---------- | ---- | | [RAG: Retrieval-Augmented Generation](https://arxiv.org/abs/2005.11401) | 检索增强生成 | 2020 | | [Self-RAG: Learning to Retrieve, Generate, and Critique](https://arxiv.org/abs/2310.11511) | 自适应检索与自我批判 | 2023 | ## 12.2.5 安全与对齐 | 论文 | 主题 | 年份 | | --------------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---- | | [Ignore This Title and HackAPrompt](https://arxiv.org/abs/2311.16119) | 提示词注入攻防 | 2023 | | [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) | 价值对齐与无害性 | 2022 | | [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858) | 模型安全评估与对齐实践 | 2022 | | [Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling](https://arxiv.org/abs/2306.11489) | 事实锚定与幻觉减少 | 2023 | | [Large Language Model Supply Chain: Open Problems From the Security Perspective](https://arxiv.org/abs/2411.01604) | 供应链安全与依赖管理 | 2024 | | [IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs](https://arxiv.org/abs/2603.10521) | 指令层级鲁棒性训练 | 2026 | | [Many-Tier Instruction Hierarchy in LLM Agents](https://arxiv.org/abs/2604.09443) | 多层级指令优先级建模 | 2026 | ## 12.2.6 智能体编程 | 论文 | 主题 | 年份 | | ----------------------------------------------------------------------------------------------------- | ------------------ | ---- | | [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) | 软件工程智能体评估 | 2023 | | [AgentBench: Evaluating LLMs as Agents](https://arxiv.org/abs/2308.03688) | 智能体综合评估基准（见 7.2 节） | 2023 | | [AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct](https://arxiv.org/abs/2405.14906) | 自动化代码生成增强 | 2024 | | [SkillOpt: Executive Strategy for Self-Evolving Agent Skills](https://arxiv.org/abs/2605.23904) | 可验证的 Skill 自进化 | 2026 | ## 12.2.7 综述论文 | 论文 | 主题 | 年份 | | -------------------------------------------------------------------------------------------- | ----- | ---- | | [A Survey on Large Language Model based Autonomous Agents](https://arxiv.org/abs/2308.11432) | 智能体综述 | 2023 | | [The Rise and Potential of LLM Based Agents: A Survey](https://arxiv.org/abs/2309.07864) | 智能体发展 | 2023 | ## 12.2.8 工程资料如何选 建议把工程材料按“可迁移性”排序： 1. **方法论与流程**：评测设计、可观测性、权限边界、回归样例集、事故复盘模板。 2. **系统能力结构**：工具调用、结构化输出、缓存、检索、沙箱、审批与治理。 3. **工具/框架手册**：具体配置文件、命令行参数、SDK 示例。 这样阅读能避免被短周期的产品变化牵着走。 *** *注：论文链接多为 arXiv，版本可能随作者更新。* *** **下一节**: [AGENTS.md 规范指南](/agentic_ai_guide/di-wu-bu-fen-fu-lu/12_appendix/12.3_agents_md.md)