References¶

All sources used in this knowledge base. Organised by topic.

RAG Fundamentals¶

Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS 2020. https://arxiv.org/abs/2005.11401
Gao, Y. et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. https://arxiv.org/abs/2312.10997

Yao, S. et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023. https://arxiv.org/abs/2210.11610
Asai, A. et al. (2023). Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. ICLR 2024. https://arxiv.org/abs/2310.11511
Shi, W. et al. (2024). Corrective Retrieval Augmented Generation. https://arxiv.org/abs/2401.15884
Jeong, S. et al. (2024). Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity. https://arxiv.org/abs/2403.14403
Edge, D. et al. (Microsoft, 2024). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. https://arxiv.org/abs/2404.16130
Microsoft GraphRAG Open Source. https://github.com/microsoft/graphrag
Traag, V. A. et al. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports. https://doi.org/10.1038/s41598-019-41695-z

Es, S. et al. (2023). RAGAS: Automated Evaluation of Retrieval Augmented Generation. https://arxiv.org/abs/2309.15217
RAGAS Framework Documentation. https://docs.ragas.io
Liu, N. F. et al. (2023). Lost in the Middle: How Language Models Use Long Contexts. https://arxiv.org/abs/2307.03172

Yao, S. et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023. https://arxiv.org/abs/2210.11610
Wang, L. et al. (2024). A Survey on Large Language Model based Autonomous Agents. Frontiers of Computer Science. https://arxiv.org/abs/2308.11432
Xi, Z. et al. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. https://arxiv.org/abs/2309.07864

Packer, C. et al. (2023). MemGPT: Towards LLMs as Operating Systems. https://arxiv.org/abs/2310.08560
Park, J. S. et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior. UIST 2023. https://arxiv.org/abs/2304.03442
Tulving, E. (1985). Memory and Consciousness. Canadian Psychology / Psychologie canadienne. (Foundational taxonomy: episodic vs. semantic memory)
Liu, N. F. et al. (2023). Lost in the Middle: How Language Models Use Long Contexts. https://arxiv.org/abs/2307.03172

Yao, S. et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023. https://arxiv.org/abs/2210.11610
Wang, L. et al. (2023). Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models. ACL 2023. https://arxiv.org/abs/2305.04091
Zhou, A. et al. (2023). Language Agent Tree Search (LATS). https://arxiv.org/abs/2310.04406
Shinn, N. et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. NeurIPS 2023. https://arxiv.org/abs/2303.11366

Wu, Q. et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. https://arxiv.org/abs/2308.08155
LangGraph Documentation. https://langchain-ai.github.io/langgraph/
CrewAI Documentation. https://docs.crewai.com
AG2 (AutoGen fork) GitHub. https://github.com/ag2ai/ag2
Google Agent Development Kit (ADK). https://google.github.io/adk-docs
Google ADK Announcement Blog (April 2025). https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/

Anthropic. (November 2024). Model Context Protocol (MCP) Announcement. https://www.anthropic.com/news/model-context-protocol
Model Context Protocol Specification. https://modelcontextprotocol.io
MCP GitHub Repository. https://github.com/modelcontextprotocol
Google. (April 2025). Agent2Agent Protocol (A2A) Announcement. https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
A2A Protocol Specification. https://google.github.io/A2A
Linux Foundation AI & Data (AAIF). MCP Donation Announcement (December 2025). https://lfaidata.foundation/blog/2025/12/model-context-protocol-joins-linux-foundation/

Du, Y. et al. (2023). Improving Factuality and Reasoning in Language Models through Multiagent Debate. https://arxiv.org/abs/2305.14325
Wu, Q. et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. https://arxiv.org/abs/2308.08155

OWASP Top 10 for LLM Applications (2025 Edition). https://owasp.org/www-project-top-10-for-large-language-model-applications/
Perez, F. & Ribeiro, I. (2022). Ignore Previous Prompt: Attack Techniques For Language Models. (Prompt Injection). https://arxiv.org/abs/2211.09527

Liu, N. F. et al. (2023). Lost in the Middle: How Language Models Use Long Contexts. https://arxiv.org/abs/2307.03172
Jiang, H. et al. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. EMNLP 2023. https://arxiv.org/abs/2310.05736
Jiang, H. et al. (2024). LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression. https://arxiv.org/abs/2403.12968
Xu, F. F. et al. (2023). RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation. https://arxiv.org/abs/2310.04408
Kwon, W. et al. (2023). Efficient Memory Management for Large Language Model Serving with PagedAttention. SOSP 2023. https://arxiv.org/abs/2309.06180
vLLM GitHub Repository. https://github.com/vllm-project/vllm

Weng, L. (2023). LLM-powered Autonomous Agents. Lilian Weng's Blog. https://lilianweng.github.io/posts/2023-06-23-agent/
Anthropic Claude Documentation. https://docs.anthropic.com
OpenAI Platform Documentation (Function Calling). https://platform.openai.com/docs/guides/function-calling