Big Loop and Atomization: A Holistic Review on the Expansion Capabilities of Large Language Models

Large language models (LLMs) have demonstrated impressive capabilities, yet they face significant limitations in real-world applications. To overcome these boundaries, research areas such as tool learning, model collaboration, agents, and multi-agent systems have increasingly drawn attention. However, current studies are often conducted in isolation, lacking a unified framework for systematic integration, which hinders the synergy among closely related research efforts. To address this gap, this study, for the first time, brings together tool learning, model collaboration, and agent-related fields under a unified framework based on the concepts of the “Big Loop” and “Atomization”. In this framework, atomic components refer to fundamental units such as models, tools, and agents. The Big Loop is formed through interactions among these atomic components to achieve end-to-end task completion—for example, tool calling requires the integration of agent modules, tool retrieval models, and tools themselves; multi-agent systems require coordination among multiple agent units. This review first clarifies the foundational concepts of the Big Loop and Atomization and elaborates on the advantages of the Big Loop compared to a single LLM. It then systematically introduces the construction of atomic components, the scheduling of these components within the Big Loop, and the optimization of the overall system. The paper also discusses existing challenges and outlines future research directions. This work aims to offer a systematic perspective for both academia and industry, and to chart a course for exploration in this emerging and highly promising field of Big Loop and Atomization.