References and Supporting Papers

This section collects papers, essays, and project references related to the themes in the book. It should be treated as a starting point for further reading, not as a fully audited citation apparatus. Some entries need source verification before a formal publication pass.

Chapter 1: Introduction to LLM Agents

• Vaswani et al. (2017). Attention is All You Need. Introduced the transformer architecture foundational to modern LLMs.
• Brown et al. (2020). Language Models are Few-Shot Learners (GPT-3). Demonstrates general capabilities of LLMs as zero/few-shot learners.
• OpenAI (2023). Introducing Function Calling. Relevant to tool-calling interfaces and structured model outputs.

Chapter 2: Methodologies and Core Patterns

• Yao et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. Relevant to reasoning-and-acting loops.
• Jiang et al. (2023). Active-Prompt: Prompt Engineering with Chain-of-Thought Reasoning. Related to prompt refinement and idea iteration.
• McLuhan, M. (1964). Understanding Media: The Extensions of Man. “The medium is the message” section reference.

Chapter 3: The Architecture of Agent Behavior

• Shinn et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. Relevant to reflection and feedback loops.
• Liu et al. (2023). ToolLLM: Facilitating Tool Learning with Language Models. Basis for tool-augmented agent capabilities.
• Rajani et al. (2019). Explain Yourself! Leveraging Language Models for Commonsense Reasoning. Relevant background for explanation and reasoning traces.
• Microsoft (2023). AutoGen: Enabling Next gen LLM Applications. Practical implementation of agent roles and multi-agent coordination.

Chapter 4: Designing Your First Agent (On Your Phone)

• OpenAI Community & Prompt Engineering Guides (2022–2023). Prompt design as an accessible interface to agent behaviors.
• Qin et al. (2023). ToolBench: Towards Empowering Large Language Models with In-Context Tool Learning. Basis for simulating tools with prompts.
• Shinn et al. (2023). Reflexion. Related to critique and revision patterns.

Chapter 5: The Thinking Agent

• Madaan et al. (2023). Self-Refine: Iterative Refinement with Self-Feedback. The model-as-critic structure.
• Liu et al. (2023). Reviewer LLMs. Citation needs verification before publication.
• Bai et al. (2022). Training a Helpful and Harmless Assistant with RLHF. Introduces reward feedback loops and output alignment strategies.

Chapter 6: Architecting Agent-Based Systems

• Wu et al. (2023). AgentVerse: Facilitating Multi-Agent Collaboration. Details centralized vs decentralized agent architectures.
• Zhang et al. (2023). CAMEL: Communicative Agents for Mind Exploration of Large Scale Language Model Society. Supports multi-agent dialogue frameworks.
• Zeng et al. (2022). A Survey of Multi-Agent Systems. Gives academic grounding to MAS coordination techniques.
• Patil et al. (2023). Gorilla: Large Language Model Connected with Massive APIs. Relevant to tool and API use.

Chapter 7: A New Way of Working With Technology

• Schick et al. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. arXiv:2302.04761
  → Relevant to tool-use interfaces.
• Paranjape et al. (2023). DSPy: Compiling Declarative Language Model Programs. arXiv:2310.01848
  → Relevant to declarative language-model programs.
• Yao et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv:2210.03629
  → Relevant to reasoning-and-acting loops.
• Wu et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Frameworks. arXiv:2309.11455
  → Demonstrates modular, conversation-first interactions among agents.
• Shinn et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv:2303.11366
  → Relevant to reflection and revision patterns.

Chapter 8: Companion Agents

• Shinn et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning.
  → Related to reflection loops; does not by itself establish companion-agent memory.
• Liu et al. (2023). CAMEL: Communicative Agents for Mind Exploration of Large Scale Language Model Society.
  → Relevant to role assignment in multi-agent simulations.
• Paranjape et al. (2023). DSPy.
  → Relevant to modular language-model program design.
• Wu et al. (2023). AutoGen.
  → Foundation for prompt-based team construction and multi-role behavior.
• Yao et al. (2024). MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization.
  → Citation needs verification before publication.

Chapter 9: Designing Your Digital Lens

• Yao et al. (2022). ReAct.
  → Relevant to task-contextual reasoning.
• Paranjape et al. (2023). DSPy.
  → Relevant to declarative interfaces; filtering claim needs verification.
• Wu et al. (2023). AutoGen.
  → Conversation-driven interface for lens behavior.
• Schick et al. (2023). Toolformer.
  → Relevant to tool-use learning; content-filtering application is an extrapolation.
• Shinn et al. (2023). Reflexion.
  → Relevant to feedback-driven revision loops.
• Yao et al. (2024). MCTS-RAG: Enhance Retrieval-Augmented Generation with Monte Carlo Tree Search.
  → Retrieval/planning relevance needs verification before publication.

The following references are candidates for Chapter 10’s discussion of freestyle cognition, research automation, and AI-assisted prototyping. Verify each title, author list, date, and relevance before final publication.

Chapter 10: Freestyle Cognition

• Co-Intelligence: A Unified Framework for Learning with Language Agents
  Shinn et al., 2023 – arXiv:2309.00615
  Needs verification.

• Language Agents as Collaborators for Scientific Discovery
  Yao et al., 2023 – arXiv:2310.02634
  Needs verification.

• Autoformalization with Large Language Models
  Lehner et al., 2023 – arXiv:2301.13867
  Relevant to translating natural language into formal representations.

• DSPy: Deep Language Systems Made Easy
  Khattab et al., 2024 – arXiv:2402.19151
  Relevant to programmatic language-model workflows.

• Open Reasoner Zero: Scaling Reasoning in Language Models via Thought Decoding
  Sun et al., 2024 – arXiv:2403.09353
  Needs verification.

• MCTS-RAG: Enhance Retrieval-Augmented Generation with Monte Carlo Tree Search
  Xu et al., 2024 – arXiv:2403.05942
  Needs verification.

• Auto-J: Judging Large Language Models Without Ground Truth
  Xu et al., 2024 – arXiv:2403.10001
  Needs verification.

• ReaRec: Think Before Recommend – Unleashing Latent Reasoning in Recommenders
  Zhang et al., 2024 – arXiv:2503.22675
  Needs verification.

Chapter 11: The World You’re Building

• Anthropic (2023). Constitutional AI.
  → Emphasizes building AI systems aligned with user-defined values and principles.
• OpenAI (2023). GPTs Can Now Browse, Code, and Use Tools — System Card.
  → Demonstrates how agents extend human capability across domains.
• Sutton, R. (2019). The Bitter Lesson.
  → Long-term AI advancement comes from letting systems learn from interaction at scale.
• McLuhan, M. (1964). Understanding Media.
  → Returning to the theme of tools as extensions of human thought and agency.