# 附录 B：参考文献

本附录列举全书引用的学术论文、技术规范和行业报告，按主题分类。开源工具和学习资源见附录 C。

## 学术论文与研究

### 智能体基准与评估

1. **GAIA: A Benchmark for General AI Assistants**
   * Mialon, Fourrier 等（Meta, Hugging Face 等机构），2023
   * <https://arxiv.org/abs/2311.12983> | [archive.org](https://web.archive.org/web/*/arxiv.org/abs/2311.12983)
   * 涵盖三个难度等级的约 466 个任务，用于评估智能体推理和工具使用能力
2. **WebArena: A Realistic Web Environment for Building Autonomous Agents**
   * Zhou 等(CMU)，2023
   * <https://arxiv.org/abs/2307.13854>
   * 812 个现实网站自动化任务，包含电商、社交、政府等域
3. **SWE-bench: Can Language Models Resolve Real-World GitHub Issues?**
   * Princeton & OpenAI, 2024
   * <https://arxiv.org/abs/2310.06770> | [archive.org](https://web.archive.org/web/*/arxiv.org/abs/2310.06770)
   * 2294个真实GitHub问题，评估代码理解和修改能力
4. **AgentBench: Evaluating LLMs as Intelligent Agents**
   * Liu 等（Tsinghua University 等），2023
   * <https://arxiv.org/abs/2308.03688>
   * 跨 8 个领域的多类别基准任务
5. **SkillOpt: Executive Strategy for Self-Evolving Agent Skills**
   * Yang 等（Microsoft Research 等），2026
   * <https://arxiv.org/abs/2605.23904>
   * 将自然语言 Skill 文档作为冻结智能体的外部可训练状态，通过 scored rollout、受控编辑和 held-out 验证门禁优化可复用 Skill

### 提示词工程与优化

6. **Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**
   * Google, 2022
   * <https://arxiv.org/abs/2201.11903>
   * 基础论文，展示逐步推理如何改善LLM能力
7. **ReAct: Synergizing Reasoning and Acting in Language Models**
   * Google & Princeton, 2023
   * <https://arxiv.org/abs/2210.03629>
   * 推理与行动结合的智能体框架原理

### 安全性与对抗性

8. **Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection**
   * Greshake, Abdelnabi 等（CISPA, Saarland University 等），2023
   * <https://arxiv.org/abs/2302.12173>
   * 间接提示注入攻击的系统分类与实际危害分析，涵盖数据窃取、蠕虫传播等攻击向量
9. **Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)**
   * Ruan 等（UChicago, Stanford, CMU），2024（ICLR 2024）
   * <https://arxiv.org/abs/2309.15817>
   * 用 LM 模拟工具执行环境，评估智能体安全风险；36 个高风险工具 + 144 个测试用例

### 多智能体系统

10. **Agent-SafetyBench: Evaluating the Safety of LLM Agents**

* Zhang, Cui 等，2024
* <https://arxiv.org/abs/2412.14470>
* 349 个交互环境、2000 个测试用例，覆盖 8 类安全风险和 10 种常见失败模式

### 长期记忆与推理

11. **In-Context Learning and Induction Heads**
    * Anthropic, 2022
    * <https://arxiv.org/abs/2209.11895>
    * 理解LLM如何利用上下文进行学习

## 技术文档与规范

### Anthropic官方文档

12. **Claude API Documentation**
    * Anthropic, 2026
    * <https://docs.anthropic.com> | [archive.org](https://web.archive.org/web/*/docs.anthropic.com)
    * Claude模型的API使用、限制、最佳实践
13. **Model Context Protocol (MCP) Specification**
    * Anthropic 发起，Linux Foundation 托管，2024-2026
    * <https://github.com/modelcontextprotocol/specification> | [archive.org](https://web.archive.org/web/*/github.com/modelcontextprotocol/specification)
    * 工具定义和交互的开放标准协议；最新版本以官方 specification/latest 页面为准
14. **Claude Code Documentation**
    * Anthropic, 2026
    * Harness框架特定文档，含权限、路径校验、护栏细节

### 国际标准

15. **NIST AI Agent Standards Initiative**
    * NIST CAISI(Center for AI Standards and Innovation)，2026
    * <https://www.nist.gov/caisi/ai-agent-standards-initiative> | [archive.org](https://web.archive.org/web/*/nist.gov/caisi/ai-agent-standards-initiative)
    * 美国国家标准与技术研究院发起的 AI 智能体标准化工作，涵盖互操作性和安全等方面
16. **IEEE Standards for Autonomous Systems**
    * IEEE, 2024
    * 自主系统的行为、安全、可靠性标准

### 开源框架文档

17. **LangChain Documentation**
    * LangChain, 2023-2026
    * <https://python.langchain.com>
    * Agent、工具、链式推理、记忆管理等
18. **LlamaIndex (formerly GPT Index)**
    * Jerry Liu & team, 2023-2026
    * <https://www.llamaindex.ai>
    * 数据连接与检索增强生成(RAG)
19. **AutoGen: Enabling Next-Gen Large Language Model Applications**
    * Microsoft, 2023
    * <https://microsoft.github.io/autogen>
    * 多智能体框架和对话工程

## 行业报告

20. **State of AI Intelligent Agents in Production**
    * LangChain, 2024
    * 57% 的组织已在生产运行智能体系统；智能体工程的成熟度和挑战分析
21. **Magic Quadrant for Generative AI Development Platforms**
    * Gartner, 2024
    * 智能体框架和工具的市场定位和评估

***

**获取方法**：大多数论文可通过 arXiv、Google Scholar、官方网站免费获取。开源项目均可通过 GitHub 访问。商业工具通常提供免费试用。


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