In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to evaluate query complexity, decompose problems into executable plans, and orchestrate tool usage, task execution, and content synthesis. We systematically present key methodologies for realizing this paradigm, including task planning and tool integration, execution strategies, aligned and robust retrieval-augmented generation, and efficient LLM inference, spanning both algorithmic techniques and infrastructure-level optimizations. By providing an in-depth guide to these foundational components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems.

翻译：本文提出人工智能搜索范式，这是一种能够模拟人类信息处理与决策过程的下一代搜索系统综合蓝图。该范式采用由四个大语言模型驱动的智能体（Master、Planner、Executor和Writer）组成的模块化架构，可动态适应从简单事实查询到复杂多阶段推理任务的全谱系信息需求。这些智能体通过协调的工作流程进行动态协作，以评估查询复杂度、将问题分解为可执行计划，并统筹工具使用、任务执行与内容合成。我们系统性地阐述了实现该范式的关键方法，涵盖任务规划与工具集成、执行策略、对齐且鲁棒的检索增强生成，以及高效的大语言模型推理，同时涉及算法技术与基础设施层面的优化。通过对这些基础组件提供深入指导，本研究旨在为开发可信赖、自适应且可扩展的人工智能搜索系统提供参考。