Agentic Large Language Models
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Beschreibung
Agentic Large Language Models represent a rapidly emerging frontier in artificial intelligence, integrating generative modeling with the capacity to reason, act, and interact autonomously. This graduate‑level textbook provides the first systematic and comprehensive treatment of agentic LLMs, offering a unified framework that spans foundational principles, computational mechanisms, and advanced applications.
The book surveys the full methodological landscape: the architecture and training pipeline of LLMs; scaling laws; multi-step reasoning and self-reflection; multimodal vision-language-action models; world-model construction; interaction styles and memory architectures; and agentic tool-chains, including the Model Context Protocol and the robustness, safety, and security concerns that arise once agents can act in the world. It further examines behavioral and cognitive dimensions of agentic systems, including Theory of Mind, strategic behavior, emergent norms, and collective intelligence in multi-agent environments. Advanced chapters address synthetic data generation, mechanistic interpretability, and cognitive architectures, highlighting open challenges and research opportunities.
Four hands-on case studies take readers from training a small language model from scratch, through benchmarking reasoning and self-reflection on games and puzzles, to building a multi-agent medical diagnosis system and simulating a society of interacting LLM agents. Written for graduate students and LLM developers with foundational AI knowledge and supported by Python-based examples and open code repositories, this textbook serves as both a course text and a reference for the theoretical foundations, computational models, and future directions of agentic LLM research.
Agentic Large Language Models represent a rapidly emerging frontier in artificial intelligence, integrating generative modeling with the capacity to reason, act, and interact autonomously. This graduate‑level textbook provides the first systematic and comprehensive treatment of agentic LLMs, offering a unified framework that spans foundational principles, computational mechanisms, and advanced applications.
The book surveys the full methodological landscape: the architecture and training pipeline of LLMs; scaling laws; multi-step reasoning and self-reflection; multimodal vision-language-action models; world-model construction; interaction styles and memory architectures; and agentic tool-chains, including the Model Context Protocol and the robustness, safety, and security concerns that arise once agents can act in the world. It further examines behavioral and cognitive dimensions of agentic systems, including Theory of Mind, strategic behavior, emergent norms, and collective intelligence in multi-agent environments. Advanced chapters address synthetic data generation, mechanistic interpretability, and cognitive architectures, highlighting open challenges and research opportunities.
Four hands-on case studies take readers from training a small language model from scratch, through benchmarking reasoning and self-reflection on games and puzzles, to building a multi-agent medical diagnosis system and simulating a society of interacting LLM agents. Written for graduate students and LLM developers with foundational AI knowledge and supported by Python-based examples and open code repositories, this textbook serves as both a course text and a reference for the theoretical foundations, computational models, and future directions of agentic LLM research.
Covers the full landscape—from training pipelines to world models, tool use, cognition, and multi agent behavior Four hands on case studies guide readers in building and evaluating agentic LLM assistants across domains First Graduate level Textbook on Agentic LLM
Autor*in
Max van Duijn
Themen in »Agentic Large Language Models«
LLMs Agents Reasoning AI Large Language Models Natural Language Processing Multimodal Reinforcement Learning Multi-agent Simulation Deep Learning NLP Agentic LLMs LLM Reasoning LLM Training Pipeline Agentic Large Language Models