References and Supporting Papers

Mon, 01 Jan 0001 00:00:00 +0000

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.

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