用四个智能代理构建可自适应搜索的下一代AI系统
Towards AI Search Paradigm
- 四类LLM代理协同工作，动态处理从简单到复杂的查询
- 支持任务分解、工具调用与内容生成的全流程自动化
- 适合研发智能搜索系统或希望提升AI决策能力的团队
本文提出AI搜索范式，一种能模拟人类信息处理与决策的下一代搜索系统蓝图。该范式采用由四个大语言模型驱动的模块化架构：主控（Master）、规划（Planner）、执行（Executor）和写作（Writer）代理，能够动态适应从简单事实查询到复杂多阶段推理任务的全谱信息需求。这些代理通过协调工作流，实现查询复杂度评估、问题分解为可执行计划、工具使用调度、任务执行与内容合成。论文系统阐述了实现该范式的四大核心技术：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LLM推理，涵盖算法与基础设施优化。本工作旨在为可信、自适应、可扩展的AI搜索系统开发提供基础指引。
原文摘要 · Abstract (English)
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.
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